diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..a6efabce7a4f914faffed3201e1af8163fedc374 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +*.csv filter=lfs diff=lfs merge=lfs -text +*.log filter=lfs diff=lfs merge=lfs -text diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/_arf_generate.py b/hyperparameter/c2/arf/arf-c2-20260504_204630/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..42c17d82ad8528cea3b22b9ba2184d4f6f651c13 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(1382) +c_csv = "/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c2' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf-c2-1382-20260504_204635.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf-c2-1382-20260504_204635.csv") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/_arf_train.py b/hyperparameter/c2/arf/arf-c2-20260504_204630/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..600ee3b3c18130abafbd8766548a8f6ce5850e60 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf_model.pkl") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/arf-c2-1382-20260504_204635.csv b/hyperparameter/c2/arf/arf-c2-20260504_204630/arf-c2-1382-20260504_204635.csv new file mode 100644 index 0000000000000000000000000000000000000000..f09fb1f3551d7afe7ff27c60bbc63ab00e8f6d88 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/arf-c2-1382-20260504_204635.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a9df075eacde58f79639181162596f36d15c0429e861fd263a248ad87305cee3 +size 41656 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/arf_model.pkl 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"dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, 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"status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/public_gate/staged_input_manifest.json b/hyperparameter/c2/arf/arf-c2-20260504_204630/public_gate/staged_input_manifest.json new file mode 100644 index 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/run_config.json b/hyperparameter/c2/arf/arf-c2-20260504_204630/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..76e1aaa2b34334af11f554c037a8a8e6573a3200 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:46:30", + "dataset_id": "c2", + "model": "arf", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "arf", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf-c2-1382-20260504_204635.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/arf/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "ARF_DELTA": "0.01", + "ARF_MAX_ITERS": "3", + "ARF_MIN_NODE_SIZE": "7", + "ARF_NUM_TREES": "11" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/runtime_result.json b/hyperparameter/c2/arf/arf-c2-20260504_204630/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..c72201d34b0274fc438839c5687f151450bd2008 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "arf", + "run_id": "arf-c2-20260504_204630", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf-c2-1382-20260504_204635.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:46:30", + "ended_at": "2026-05-04T20:46:35", + "duration_sec": 4.953 + }, + "generate": { + "started_at": "2026-05-04T20:46:35", + "ended_at": "2026-05-04T20:46:37", + "duration_sec": 2.615 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/adapter_report.json b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..eeb615648e64323db4727886d67c571319b0b1c8 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/adapter_transforms_applied.json b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/model_input_manifest.json b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..67e15768397444ebfa4694d418ea50cfd8ee3f9f --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/arf/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "arf", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204630/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/staged_features.json b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/test.csv b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/train.csv b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/val.csv b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204630/train_20260504_204630.log b/hyperparameter/c2/arf/arf-c2-20260504_204630/train_20260504_204630.log new file mode 100644 index 0000000000000000000000000000000000000000..eab1f07793a7f6ac532b106471eb8a0c265b1bd6 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204630/train_20260504_204630.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:253c9d5a82654e53a261a511acde522fe48d5affeed38350eb4ef2f3fefb61ca +size 1002 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204810/_arf_generate.py b/hyperparameter/c2/arf/arf-c2-20260504_204810/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..1958756b1d85f97ac04d2cc7fa76f9bc72ff1ba4 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204810/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(1382) +c_csv = "/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204810/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204810/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c2' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204810/arf-c2-1382-20260504_204814.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204810/arf-c2-1382-20260504_204814.csv") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204810/_arf_train.py b/hyperparameter/c2/arf/arf-c2-20260504_204810/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8cb9b1c88b81db923c29f6a9b688fdd76fdab42b --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204810/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204810/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204810/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204810/arf_model.pkl") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204810/arf-c2-1382-20260504_204814.csv b/hyperparameter/c2/arf/arf-c2-20260504_204810/arf-c2-1382-20260504_204814.csv new file mode 100644 index 0000000000000000000000000000000000000000..60f47e0175216e4e3ad824032f63bd51220667ea --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204810/arf-c2-1382-20260504_204814.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:32362e2f23866523d5f892582e5cc2b208df5d33725c0707ec379ea9b04a5c30 +size 41763 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204810/arf_model.pkl 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b/hyperparameter/c2/arf/arf-c2-20260504_204810/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204810/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204810/staged/public/val.csv b/hyperparameter/c2/arf/arf-c2-20260504_204810/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204810/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204810/train_20260504_204810.log b/hyperparameter/c2/arf/arf-c2-20260504_204810/train_20260504_204810.log new file mode 100644 index 0000000000000000000000000000000000000000..6dded3953a3177594319e004d256f2c35df6b2a0 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204810/train_20260504_204810.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:766114cb37dc6a845fe6f50743760528206e5f07dc8cbb7a67c5069424376baa +size 999 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/_arf_generate.py b/hyperparameter/c2/arf/arf-c2-20260504_204831/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..f639ff6fc3e24ff0226d16d4b26804a3b2f0af5a --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(1382) +c_csv = "/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c2' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf-c2-1382-20260504_204837.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf-c2-1382-20260504_204837.csv") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/_arf_train.py b/hyperparameter/c2/arf/arf-c2-20260504_204831/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..cb50404482383904cf87ee0a048c9f07a4efaf6e --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf_model.pkl") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/arf-c2-1382-20260504_204837.csv b/hyperparameter/c2/arf/arf-c2-20260504_204831/arf-c2-1382-20260504_204837.csv new file mode 100644 index 0000000000000000000000000000000000000000..e77641c6b103c0365f928e98eb6a70484e910091 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/arf-c2-1382-20260504_204837.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:220d45777265ecddbcf2c8d2a46cc14c2a8062fd66ccb2e1f47165c8825787e4 +size 41587 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/arf_model.pkl 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"profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/arf/arf-c2-20260504_204831/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/run_config.json b/hyperparameter/c2/arf/arf-c2-20260504_204831/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..b20308e1f741b98ab1db3f51704b6793133e58a7 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:48:31", + "dataset_id": "c2", + "model": "arf", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "arf", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf-c2-1382-20260504_204837.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/arf/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "ARF_CLIP_QUANTILE_HIGH": "0.999", + "ARF_CLIP_QUANTILE_LOW": "0.001", + "ARF_DELTA": "0", + "ARF_EARLY_STOP": "true", + "ARF_MAX_ITERS": "10", + "ARF_MIN_NODE_SIZE": "5", + "ARF_NUM_TREES": "30" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/runtime_result.json b/hyperparameter/c2/arf/arf-c2-20260504_204831/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..051969947f2559447138f78dd956f8bf86202663 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "arf", + "run_id": "arf-c2-20260504_204831", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf-c2-1382-20260504_204837.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:48:31", + "ended_at": "2026-05-04T20:48:37", + "duration_sec": 6.228 + }, + "generate": { + "started_at": "2026-05-04T20:48:37", + "ended_at": "2026-05-04T20:48:40", + "duration_sec": 2.878 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/adapter_report.json b/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..0739fc36c25d95c12bba028b277819ca523d8b6f --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/adapter_transforms_applied.json b/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/model_input_manifest.json b/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..72afbe2876cc447794ef09805366ab3396a47f40 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/arf/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "arf", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204831/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/public/staged_features.json b/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false 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b/hyperparameter/c2/arf/arf-c2-20260504_204831/train_20260504_204831.log new file mode 100644 index 0000000000000000000000000000000000000000..1f14caa0a0179e22ad764dfc2d463d59199030ed --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204831/train_20260504_204831.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c297a442dff9e3ba6fbe2fd66ad3382fa16b477ba1b181b3feb15018d4e437d5 +size 575 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/_arf_generate.py b/hyperparameter/c2/arf/arf-c2-20260504_204853/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..32a1b1b29d37240c432037a30e4b9945652ff091 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(1382) +c_csv = "/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c2' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf-c2-1382-20260504_204902.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf-c2-1382-20260504_204902.csv") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/_arf_train.py b/hyperparameter/c2/arf/arf-c2-20260504_204853/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7cd1a3dbfb486cac41e24b53f0a079a00eb617e2 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf_model.pkl") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/arf-c2-1382-20260504_204902.csv b/hyperparameter/c2/arf/arf-c2-20260504_204853/arf-c2-1382-20260504_204902.csv new file mode 100644 index 0000000000000000000000000000000000000000..64788916375cb216fba91ab4b1a785c081a2d904 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/arf-c2-1382-20260504_204902.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d431671e34976621dab8fc43b94d57e19f9281dd0e44ad069015926a137c96a +size 41479 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/arf_model.pkl b/hyperparameter/c2/arf/arf-c2-20260504_204853/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..1c3338b793122effdd09bb954b92e21359a95194 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f6e0cc662e2ab4206707e39bee3f4c91af62e6506cfa142d31b36a7d7ab69bcc +size 13379602 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/gen_20260504_204902.log b/hyperparameter/c2/arf/arf-c2-20260504_204853/gen_20260504_204902.log new file mode 100644 index 0000000000000000000000000000000000000000..3e60ad2ea54046b6de99dac6170a5969b7b62d7e --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/gen_20260504_204902.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b68f8627de15fe877c7906166631320e5860c4356786f1ac59c762d47640d4ec +size 2615 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/input_snapshot.json 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"model": "arf", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "arf", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf-c2-1382-20260504_204902.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/public_gate/public_gate_report.json", + 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a/hyperparameter/c2/arf/arf-c2-20260504_204853/runtime_result.json b/hyperparameter/c2/arf/arf-c2-20260504_204853/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..656170dfe4d256617fe602711a65b7e43eba92d1 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "arf", + "run_id": "arf-c2-20260504_204853", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf-c2-1382-20260504_204902.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:48:53", + "ended_at": "2026-05-04T20:49:02", + "duration_sec": 8.796 + }, + "generate": { + "started_at": "2026-05-04T20:49:02", + "ended_at": "2026-05-04T20:49:05", + "duration_sec": 2.98 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/adapter_report.json b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..4ef5c54f98729280314dbbabfd29eee19899a9d1 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/adapter_transforms_applied.json b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/model_input_manifest.json b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b88c63b96162b065cdd1906f92ab839f813c785e --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/arf/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "arf", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": 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+ { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204853/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/public/staged_features.json b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/public/test.csv b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204853/staged/public/train.csv 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b/hyperparameter/c2/arf/arf-c2-20260504_204853/train_20260504_204853.log new file mode 100644 index 0000000000000000000000000000000000000000..c8cab8f659a77924de69c63d0a9b1bc5e5177868 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204853/train_20260504_204853.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2ff00603744a523d221abc183a02277603c3754ba0afb0bc772e924b309abc67 +size 575 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/_arf_generate.py b/hyperparameter/c2/arf/arf-c2-20260504_204918/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..0ead3b995a585b8e2f2057129a4a6eb2e45d2d53 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(1382) +c_csv = "/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c2' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf-c2-1382-20260504_204930.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf-c2-1382-20260504_204930.csv") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/_arf_train.py b/hyperparameter/c2/arf/arf-c2-20260504_204918/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b9f4fc487eb071cf69509b26f83e7d42b4a2c879 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf_model.pkl") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/arf-c2-1382-20260504_204930.csv b/hyperparameter/c2/arf/arf-c2-20260504_204918/arf-c2-1382-20260504_204930.csv new file mode 100644 index 0000000000000000000000000000000000000000..b4ed513d5d219e35d3022f155ff4869f03638344 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/arf-c2-1382-20260504_204930.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8351278ee5b0e208fdb28f3305084240ce38bd301ec9bd56122f451650aef0f4 +size 41656 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/arf_model.pkl 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"profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/public_gate/public_gate_report.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/public_gate/staged_input_manifest.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/public_gate/staged_input_manifest.json new file mode 100644 index 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/run_config.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..1e0c9b9da8db56d140c31c1b2254cf1e94343ca1 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:49:18", + "dataset_id": "c2", + "model": "arf", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "arf", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf-c2-1382-20260504_204930.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/arf/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "ARF_CLIP_QUANTILE_HIGH": "0.999", + "ARF_CLIP_QUANTILE_LOW": "0.001", + "ARF_DELTA": "0.01", + "ARF_EARLY_STOP": "true", + "ARF_MAX_ITERS": "15", + "ARF_MIN_NODE_SIZE": "3", + "ARF_NUM_TREES": "100" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/runtime_result.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..b0dbd472a1063c8e582aee10ff7b3736ae8440a5 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "arf", + "run_id": "arf-c2-20260504_204918", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf-c2-1382-20260504_204930.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:49:18", + "ended_at": "2026-05-04T20:49:30", + "duration_sec": 11.757 + }, + "generate": { + "started_at": "2026-05-04T20:49:30", + "ended_at": "2026-05-04T20:49:33", + "duration_sec": 3.22 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/adapter_report.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..4e50af5bf2cc5bf42873bce26b262af850177393 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/adapter_transforms_applied.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/model_input_manifest.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5ded79481fd73634b771b2a1c5165fa7740e39bb --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/arf/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "arf", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204918/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/staged_features.json b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/test.csv b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/train.csv b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/val.csv b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204918/train_20260504_204918.log b/hyperparameter/c2/arf/arf-c2-20260504_204918/train_20260504_204918.log new file mode 100644 index 0000000000000000000000000000000000000000..b1ff0db0a441725b0e993d67f844121104cd84fb --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204918/train_20260504_204918.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b9dc164bd6de5fc64f515ea1bcebb4b7b3ace29ac180bbb867facdf7013a48d +size 579 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/_arf_generate.py b/hyperparameter/c2/arf/arf-c2-20260504_204947/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..961fbca477246b08244ed63d85d8fd124d54d865 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(1382) +c_csv = "/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c2' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf-c2-1382-20260504_205024.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf-c2-1382-20260504_205024.csv") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/_arf_train.py b/hyperparameter/c2/arf/arf-c2-20260504_204947/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f6814d52aaea818a51e6eacb7a6cf96de225d1f4 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf_model.pkl") diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/arf-c2-1382-20260504_205024.csv b/hyperparameter/c2/arf/arf-c2-20260504_204947/arf-c2-1382-20260504_205024.csv new file mode 100644 index 0000000000000000000000000000000000000000..e0af1c69eb5fe9e0cee1b7859e8bab32b130b5e6 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/arf-c2-1382-20260504_205024.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f43f46946f888e3279c81aac941c09deb9efd59fe0fe162f051b59002d9a0c88 +size 41542 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/arf_model.pkl b/hyperparameter/c2/arf/arf-c2-20260504_204947/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..606d2b8c017f50dea56463878f9dd44bed5da660 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e0ffe5306fb2a384405dfe7c312ded4e426102c865c857342e05eb7d5a8474a +size 108011862 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/gen_20260504_205024.log b/hyperparameter/c2/arf/arf-c2-20260504_204947/gen_20260504_205024.log new file mode 100644 index 0000000000000000000000000000000000000000..ff79bbe04eacf4ec8e1c2d0a0a2601193599b213 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/gen_20260504_205024.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b9eeafef852e8d3af764df0c7ea079a814366eee5adbe92c444f583e02f5b70 +size 2615 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/input_snapshot.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5fa654607f22f68d2aaa5e57809017787b5eb02b --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/public_gate_report.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/staged_input_manifest.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..417151470435f0821ad68bba0f14f258265e6c1a --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/run_config.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f614bfe40482683269b1ce11c849322e3d40d859 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:49:47", + "dataset_id": "c2", + "model": "arf", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "arf", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf-c2-1382-20260504_205024.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/arf/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "ARF_CLIP_QUANTILE_HIGH": "0.9995", + "ARF_CLIP_QUANTILE_LOW": "0.0005", + "ARF_DELTA": "0.02", + "ARF_EARLY_STOP": "false", + "ARF_MAX_ITERS": "20", + "ARF_MIN_NODE_SIZE": "1", + "ARF_NUM_TREES": "150" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/runtime_result.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..9435ff2c3f17b681f2edefd2a3a917f211140e34 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "arf", + "run_id": "arf-c2-20260504_204947", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf-c2-1382-20260504_205024.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:49:47", + "ended_at": "2026-05-04T20:50:24", + "duration_sec": 37.599 + }, + "generate": { + "started_at": "2026-05-04T20:50:24", + "ended_at": "2026-05-04T20:50:27", + "duration_sec": 3.222 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/adapter_report.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..79cf841fe075abafc39696cd055772cdb19c6349 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/adapter_transforms_applied.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/model_input_manifest.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a418fd702f7b4a2ff967668b70d3bd3d78aa7d90 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/arf/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "arf", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204947/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/staged_features.json b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/test.csv b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/train.csv b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/val.csv b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/arf/arf-c2-20260504_204947/train_20260504_204947.log b/hyperparameter/c2/arf/arf-c2-20260504_204947/train_20260504_204947.log new file mode 100644 index 0000000000000000000000000000000000000000..1d6c2c5901ceef53f9bbb9a3d670ae5f1de7dc53 --- /dev/null +++ b/hyperparameter/c2/arf/arf-c2-20260504_204947/train_20260504_204947.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3934be60c1e2b41f0f5b27bb3e0a34894ee31149271aaaaf2ba55453b4aaee0e +size 579 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/_bayesnet_generate.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c697c2e7574f7a39f99551117e20c94efea59d3b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(1382) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet-c2-1382-20260504_204659.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet-c2-1382-20260504_204659.csv") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/_bayesnet_train.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..51188b8f6575e692d2c6be945df59e0efd6bd4d5 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_model.pkl") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet-c2-1382-20260504_204659.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet-c2-1382-20260504_204659.csv new file mode 100644 index 0000000000000000000000000000000000000000..8f2bbf42d3514130e8d014374e089430a597037b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet-c2-1382-20260504_204659.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09fb0a8eacecb9bd41437483b78fbe73c2f8d9d0a3e08747a7a52fe8f2141c89 +size 41572 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_coltypes.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..dc49694e781f314f19b59a568715784a7cabec24 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_model.pkl b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a41a2cb32a11c469c794a576140511b67c8f7bdb --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204651/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/staged_features.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/test.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/train.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/val.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/train_20260504_204651.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/train_20260504_204651.log new file mode 100644 index 0000000000000000000000000000000000000000..407da68ed9f34f29d841529c32295185e72d694a --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_204651/train_20260504_204651.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0445a0c944d995b4a40dacd2f761546488906e471b617ed24976362fd921a3da +size 3909 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/_bayesnet_generate.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..da6ed22c970c1182a9610978fb43b1838ef70fdb --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(1382) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet-c2-1382-20260504_205049.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet-c2-1382-20260504_205049.csv") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/_bayesnet_train.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..979eca33805d7937092ab2b900486a07317a9db3 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205041/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_model.pkl") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet-c2-1382-20260504_205049.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet-c2-1382-20260504_205049.csv new file mode 100644 index 0000000000000000000000000000000000000000..24766bc9b7eaa0083c89f6db2fb67e578192cab5 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet-c2-1382-20260504_205049.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8021a92edcd405f86b977b4275e65b7f0ce3ea051acdad2b6850bd2dee2b1f6f +size 41530 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_coltypes.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..dc49694e781f314f19b59a568715784a7cabec24 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_model.pkl b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a41a2cb32a11c469c794a576140511b67c8f7bdb --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9fc1dfaabb7725d9da65e2853e0e7d4de92da7281a7237ef5c6909e01f2a513 +size 4250 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/const_cols.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/gen_20260504_205049.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/gen_20260504_205049.log new file mode 100644 index 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a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/staged/public/train.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/staged/public/val.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/train_20260504_205041.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/train_20260504_205041.log new file mode 100644 index 0000000000000000000000000000000000000000..f79c124428ed50dce2e2deab95772a2b389d53cd --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205041/train_20260504_205041.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d6234b7169f94b2276c9afe8e73f7b2731590e1f3e7309c9f59fcc70ae4ad6 +size 3851 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/_bayesnet_generate.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ff1d106481a7b5dec924b0c3812cc7a53cb46b11 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(1382) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet-c2-1382-20260504_205118.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet-c2-1382-20260504_205118.csv") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/_bayesnet_train.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c0505548179e551e07a8d2e00409b3a68cd6ab50 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205110/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_model.pkl") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet-c2-1382-20260504_205118.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet-c2-1382-20260504_205118.csv new file mode 100644 index 0000000000000000000000000000000000000000..5b4d58c23201f493d0c51b7e8f0c4624122204b5 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet-c2-1382-20260504_205118.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58fdde1b52d69d3a9a1d2f4d14824cb26a1a39f88ab6062f227976e8769c63f4 +size 41641 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_coltypes.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..dc49694e781f314f19b59a568715784a7cabec24 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_model.pkl b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a41a2cb32a11c469c794a576140511b67c8f7bdb --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9fc1dfaabb7725d9da65e2853e0e7d4de92da7281a7237ef5c6909e01f2a513 +size 4250 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/const_cols.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/gen_20260504_205118.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/gen_20260504_205118.log new file mode 100644 index 0000000000000000000000000000000000000000..f19b27704b3b9166c13797b9b65f4d57f60d8719 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/gen_20260504_205118.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b2ef03f87794d4243cb8d14c978c41291c346f790daa634f8d741e6bb642c0d +size 3661 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/input_snapshot.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..85fdc398e0d261ad97c34409134cc4aebb1bb672 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + 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sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/train_20260504_205110.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/train_20260504_205110.log new file mode 100644 index 0000000000000000000000000000000000000000..16b6ee17d28928b5524538e81ca5723b1a51c7f0 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205110/train_20260504_205110.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e105b69effb35457c9bfb27985101b61191edc90cbb2b210e24921c429e0583 +size 3850 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/_bayesnet_generate.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9135b24f67a271626cbe0b02a0a6bdcb3674b127 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(1382) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet-c2-1382-20260504_205147.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet-c2-1382-20260504_205147.csv") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/_bayesnet_train.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c241490a4a60a30b84e617230f1dd631a485c462 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_model.pkl") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet-c2-1382-20260504_205147.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet-c2-1382-20260504_205147.csv new file mode 100644 index 0000000000000000000000000000000000000000..f5fda963f8a9c14e2d1b6c1fd317de2020723c02 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet-c2-1382-20260504_205147.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a98f5aba955e432efbc58ec3c246f98ce379e29ec18258fa131677c4a8207d0 +size 41319 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_coltypes.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..dc49694e781f314f19b59a568715784a7cabec24 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_model.pkl b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a41a2cb32a11c469c794a576140511b67c8f7bdb --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9fc1dfaabb7725d9da65e2853e0e7d4de92da7281a7237ef5c6909e01f2a513 +size 4250 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/const_cols.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/gen_20260504_205147.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/gen_20260504_205147.log new file mode 100644 index 0000000000000000000000000000000000000000..141154ecaf24d95838e9f8a6b420967d9ba6adb6 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/gen_20260504_205147.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5eb25790fd0b2dc6abb854ce32828dc7c81015a2095b6c669368b99610a6a929 +size 3661 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/input_snapshot.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..85fdc398e0d261ad97c34409134cc4aebb1bb672 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/public_gate_report.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/staged_input_manifest.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..69adc9f1d71ecd9b1f2e600dd7ea0ca554810116 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } 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] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/run_config.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6e7db4d7291cddc7948ba1f58daf9e468244b5c8 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/run_config.json @@ -0,0 +1,47 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:51:40", + "dataset_id": "c2", + "model": "bayesnet", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "bayesnet", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BAYESNET_DISABLE_AUTO_CAPS": "1", + "BAYESNET_EDGE_WEIGHTS_FN": "mutual_info", + "BAYESNET_ESTIMATOR_TYPE": "chow-liu", + "BAYESNET_FIT_ROWS": "120000", + "BAYESNET_MAX_BINS": "8", + "BAYESNET_MAX_CAT_LEVELS": "128", + "BAYESNET_N_JOBS": "1", + "BAYESNET_STRUCT_ROWS": "25000" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/runtime_result.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..064dbfca32af916dba63d143ba0f1dbf836fe27e --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "run_id": "bayesnet-c2-20260504_205140", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet-c2-1382-20260504_205147.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:51:40", + "ended_at": "2026-05-04T20:51:47", + "duration_sec": 7.766 + }, + "generate": { + "started_at": "2026-05-04T20:51:47", + "ended_at": "2026-05-04T20:51:55", + "duration_sec": 7.539 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/adapter_report.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..decf7b77179e28d8a1ec877ebc099ea7e1822f9f --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/adapter_transforms_applied.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/model_input_manifest.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..d2ede735c58ea9969638b87ff9100cecd708d05d --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + 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"name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205140/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/staged_features.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/test.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git 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sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/train_20260504_205140.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/train_20260504_205140.log new file mode 100644 index 0000000000000000000000000000000000000000..f7f3e215504117e0188e13f6abef9c0e9e50e5b4 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205140/train_20260504_205140.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f64ed9ef44a379f6c95036ed8bf937190a3d2a13978ad8d58a132eb221099a70 +size 3853 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/_bayesnet_generate.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..485ba03c61a57095158588b6c3791d74d966312e --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(1382) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet-c2-1382-20260504_205216.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet-c2-1382-20260504_205216.csv") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/_bayesnet_train.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..450ec35c05f6b5cdb27a410f288c18e9d999c32c --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet-c2-1382-20260504_205216.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet-c2-1382-20260504_205216.csv new file mode 100644 index 0000000000000000000000000000000000000000..bc7ec2655ce38f679e955d98e43754de2c050e6a --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet-c2-1382-20260504_205216.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ba814313cff8127cbfad72554dd67f9f9817adc15f276c9c7f46caa0889c142 +size 41627 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_coltypes.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..dc49694e781f314f19b59a568715784a7cabec24 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a41a2cb32a11c469c794a576140511b67c8f7bdb --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9fc1dfaabb7725d9da65e2853e0e7d4de92da7281a7237ef5c6909e01f2a513 +size 4250 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/const_cols.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/gen_20260504_205216.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/gen_20260504_205216.log new file mode 100644 index 0000000000000000000000000000000000000000..1a5795b4bab1192f3487c298c698981477e71e7f --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/gen_20260504_205216.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:079285e30320862933a5864a529c5be4a8132bf795f665d1e47eaab722dbfed4 +size 3661 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/input_snapshot.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..85fdc398e0d261ad97c34409134cc4aebb1bb672 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/public_gate_report.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/staged_input_manifest.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cac4f18c871535b8eacad2819839f21397c2e0c0 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/run_config.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..5988fc1555ce16266c443f0a5fb585b8dc6c4a3c --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/run_config.json @@ -0,0 +1,47 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:52:08", + "dataset_id": "c2", + "model": "bayesnet", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "bayesnet", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet-c2-1382-20260504_205216.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BAYESNET_DISABLE_AUTO_CAPS": "1", + "BAYESNET_EDGE_WEIGHTS_FN": "adjusted_mutual_info", + "BAYESNET_ESTIMATOR_TYPE": "chow-liu", + "BAYESNET_FIT_ROWS": "120000", + "BAYESNET_MAX_BINS": "8", + "BAYESNET_MAX_CAT_LEVELS": "64", + "BAYESNET_N_JOBS": "1", + "BAYESNET_STRUCT_ROWS": "25000" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/runtime_result.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..17f439a09effa22dddfe53cf1468efe76415e0b3 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "run_id": "bayesnet-c2-20260504_205208", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet-c2-1382-20260504_205216.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:52:08", + "ended_at": "2026-05-04T20:52:16", + "duration_sec": 7.51 + }, + "generate": { + "started_at": "2026-05-04T20:52:16", + "ended_at": "2026-05-04T20:52:24", + "duration_sec": 7.815 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/adapter_report.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..7d301889bb146ae088d5543e4463b874e7740129 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/adapter_transforms_applied.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/model_input_manifest.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0b6d592698e5346c2300c5551ed4e9b653d13ffb --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205208/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/staged_features.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/test.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/train.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/val.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/train_20260504_205208.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/train_20260504_205208.log new file mode 100644 index 0000000000000000000000000000000000000000..99fa04d0d303a023299cfe4b88c39915e9da5877 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205208/train_20260504_205208.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:04a616babcee3b5f77b0ec67fae34d175a11acd6119a5e80d9b390210b94deba +size 3861 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/_bayesnet_generate.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..27da8fff1a9f6836ca470666e956d0a83e802340 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(1382) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet-c2-1382-20260504_205244.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet-c2-1382-20260504_205244.csv") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/_bayesnet_train.py b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..642e4a1f1441ad54e4b23b74185a421684d81704 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl") diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet-c2-1382-20260504_205244.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet-c2-1382-20260504_205244.csv new file mode 100644 index 0000000000000000000000000000000000000000..668d83fef7e13287daaf2bc645d990d176556098 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet-c2-1382-20260504_205244.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e89519b5229826101a17e1517600e886222048d73e8677b05ec32d5892c5b997 +size 41508 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_coltypes.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..dc49694e781f314f19b59a568715784a7cabec24 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a41a2cb32a11c469c794a576140511b67c8f7bdb --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9fc1dfaabb7725d9da65e2853e0e7d4de92da7281a7237ef5c6909e01f2a513 +size 4250 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/const_cols.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/gen_20260504_205244.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/gen_20260504_205244.log new file mode 100644 index 0000000000000000000000000000000000000000..ec75ab17e20c7fa2082db6ac140c1787d8c2de89 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/gen_20260504_205244.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19823247d70b83f5722e4df214c381c77a8849f7b0fed8b3f272ec673e00dd42 +size 3661 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/input_snapshot.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..85fdc398e0d261ad97c34409134cc4aebb1bb672 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/public_gate_report.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/staged_input_manifest.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a17159e9edf46e1b81edd4c541428218f54c1ad8 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/run_config.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..8cd464a14e862019814d1219d056592a24b88e3d --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/run_config.json @@ -0,0 +1,47 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:52:36", + "dataset_id": "c2", + "model": "bayesnet", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "bayesnet", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet-c2-1382-20260504_205244.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BAYESNET_DISABLE_AUTO_CAPS": "1", + "BAYESNET_EDGE_WEIGHTS_FN": "normalized_mutual_info", + "BAYESNET_ESTIMATOR_TYPE": "chow-liu", + "BAYESNET_FIT_ROWS": "120000", + "BAYESNET_MAX_BINS": "8", + "BAYESNET_MAX_CAT_LEVELS": "64", + "BAYESNET_N_JOBS": "1", + "BAYESNET_STRUCT_ROWS": "25000" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/runtime_result.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..ae8c851be6619e77ebe0113e4fff3da8f509a6d2 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "run_id": "bayesnet-c2-20260504_205236", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet-c2-1382-20260504_205244.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:52:36", + "ended_at": "2026-05-04T20:52:44", + "duration_sec": 7.97 + }, + "generate": { + "started_at": "2026-05-04T20:52:44", + "ended_at": "2026-05-04T20:52:52", + "duration_sec": 7.765 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/adapter_report.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..6d73935627d16148b8e661b10a9a3b810571cfcf --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/adapter_transforms_applied.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/model_input_manifest.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..07f6093a5b910564d40992f3139db868839452d5 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_205236/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/staged_features.json b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/test.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/train.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/val.csv b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/train_20260504_205236.log b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/train_20260504_205236.log new file mode 100644 index 0000000000000000000000000000000000000000..3cdc866818732ecd16db1402dd84e85ea794f324 --- /dev/null +++ b/hyperparameter/c2/bayesnet/bayesnet-c2-20260504_205236/train_20260504_205236.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cbf815707e215209e5a74c7031596fc1da4cbdb2bb67aa8d3cf67540801fb149 +size 3863 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_generate.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..72d631b335a5126d37d39b7ce8a5a7267f2a06ad --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_train.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6b9b4676792febe944a8372bf1283e47afc4c4dd --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=8, + generator_dim=(16, 16), + discriminator_dim=(16, 16), + batch_size=32, + pac=1, + epochs=50, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv new file mode 100644 index 0000000000000000000000000000000000000000..681e27aa7742ed0c684b00f71f3be788e0e869d8 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29e976f587efbc955df869946a4bb105ce51b79458d3c62ceca09ac148b6ed20 +size 41616 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/ctgan_metadata.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/gen_20260504_152353.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/gen_20260504_152353.log new file mode 100644 index 0000000000000000000000000000000000000000..de002e4b88e95acd9bc50ca032cc0ba1f0b86e39 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/gen_20260504_152353.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9acf86561cff1e9c6a4c9c06233f1f9190b7ed2b840fc2e34e276dbd370ea385 +size 297 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/input_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e36636095420ce570b0bfe9047133f9ba54849ac --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39c7bef1dfa571bec7a879d742cb40f266707e489676ce3b74528b1c78d49d51 +size 279437 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/train_20260504_152304.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/train_20260504_152304.log new file mode 100644 index 0000000000000000000000000000000000000000..31881c865e73fc7a604711f229b0138561760091 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/train_20260504_152304.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34ee1fa8c63c912749b01e9ecd9d79d28d46671f3739ed5d339a692b3f263b85 +size 10893 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9adffa84c9a982333c5e541977c7b6dc9bc69810 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv", + "val_csv": 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} + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/run_config.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..fb41d8bc3413589c05aaeab9ea2b38014b7b853b --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T15:23:04", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "32", + "CTGAN_DEFAULT_EPOCHS": "50", + "CTGAN_DISCRIMINATOR_DIMS": "16,16", + "CTGAN_EMBEDDING_DIM": "8", + "CTGAN_GENERATOR_DIMS": "16,16", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/runtime_result.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..d04243918b6cf71cea8be69dbd1ac322df2d0d9b --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_152304", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T15:23:04", + "ended_at": "2026-05-04T15:23:53", + "duration_sec": 49.624 + }, + "generate": { + "started_at": "2026-05-04T15:23:53", + "ended_at": "2026-05-04T15:23:59", + "duration_sec": 6.006 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..7a10b79b3fd438b352d349856e6aaa5170be0296 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..73b58d2610a7c6335c463f5ded04d279904d13f3 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_generate.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..a6e2cc17bfbb8d558e91a73d47e786f6a6ea4a89 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_train.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..eb7e6332e2a5f17ac5c5659de2b5fa14f3402fa6 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=64, + pac=1, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv new file mode 100644 index 0000000000000000000000000000000000000000..24115f3ea8ecea36e3add98d8105e4e8fb44f121 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1a3894c926a9de28a2c5d069c22c5841dba3d46059ebb36e4d9d6a34cf64b9d7 +size 41628 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/ctgan_metadata.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/gen_20260504_152503.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/gen_20260504_152503.log new file mode 100644 index 0000000000000000000000000000000000000000..8dc6481b689cc2843ea2cf84ad6d5317d8d6db8d --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/gen_20260504_152503.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4fdf3116f7dbf5aa10526c09e0aa425ff5d1d7b6d646d7493b1947d94e53cdc3 +size 297 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/input_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..0e4044dc65ae5c3bd120e640c227865c3a03dc6f --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:374f0a3ab118b4c358cfa846e538a91d1c0312a5d866fcadfd475f070e6873d8 +size 295011 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/train_20260504_152412.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/train_20260504_152412.log new file mode 100644 index 0000000000000000000000000000000000000000..016ed31b2b3da069ab0f57b0e1d8afed0aedb0de --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/train_20260504_152412.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13eea713840c309e407bb778338ff2e10f7e51ab12a6d203f926b413c280d5ad +size 20259 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..80aeec1d38cb19d7ef9896f62f53b30f8a90381f --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/run_config.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..2cb71686f0f7799148a51aa35b2a4ee84aca72e3 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T15:24:12", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "64", + "CTGAN_DEFAULT_EPOCHS": "100", + "CTGAN_DISCRIMINATOR_DIMS": "32,32", + "CTGAN_EMBEDDING_DIM": "16", + "CTGAN_GENERATOR_DIMS": "32,32", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/runtime_result.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..937e58e0473c6bbd8b3ee0e92fae0b15b8422aeb --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_152412", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T15:24:12", + "ended_at": "2026-05-04T15:25:03", + "duration_sec": 50.201 + }, + "generate": { + "started_at": "2026-05-04T15:25:03", + "ended_at": "2026-05-04T15:25:08", + "duration_sec": 5.617 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..f5f5ece257cc2be54a51b3dd7cb44e9814f81e60 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4962ba89ebed95e0c2b0d4ca815088c5fa2c32b8 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + 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"profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_generate.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..16e1cbf8bd422bec2ca1d496741eb412a6d3b10c --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_train.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..78628106b930abae414832204fa9175a7455533f --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=128, + pac=1, + epochs=150, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv new file mode 100644 index 0000000000000000000000000000000000000000..fb43b629026c53e55167fb66183df12a8ede4f6e --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9e5f49ea1929935b2cbff19d40b624b643fce1ce9b25dcd47f8a95b18c8e658 +size 41931 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/ctgan_metadata.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/gen_20260504_152602.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/gen_20260504_152602.log new file mode 100644 index 0000000000000000000000000000000000000000..f33ebfa9b854a8e71a032a35483e56c9cc8683f6 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/gen_20260504_152602.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:90a4a7de75eec0fbee24fc86b99413d696184b508dbb5166fb1f6369c00a0259 +size 297 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/input_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..464b111ee5a641b1c23c4ed76cc24c07febeefd0 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e03e4717b96ceed0bbec1c8771604a12d35d2af80e6233323dea1b055eec9ae8 +size 336739 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/train_20260504_152521.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/train_20260504_152521.log new file mode 100644 index 0000000000000000000000000000000000000000..776c175a578fb21419b6be68f41b2a4305893332 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/train_20260504_152521.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3af72c614a2bedcd9896ce46da7ab7b8947307ee05fdababfa63c84c3265b34 +size 29431 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..66e850014a3d2c262c9f9ba6b2ea2fbd0b4277a8 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv", + "val_csv": 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} + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/run_config.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..17ebaa88d95e65cb684c715dc2ce0a786db7eba5 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T15:25:21", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "128", + "CTGAN_DEFAULT_EPOCHS": "150", + "CTGAN_DISCRIMINATOR_DIMS": "64,64", + "CTGAN_EMBEDDING_DIM": "32", + "CTGAN_GENERATOR_DIMS": "64,64", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/runtime_result.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..5441404826fb87c542aeb6ba3fe39ddb39ed7d05 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_152521", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T15:25:21", + "ended_at": "2026-05-04T15:26:02", + "duration_sec": 40.385 + }, + "generate": { + "started_at": "2026-05-04T15:26:02", + "ended_at": "2026-05-04T15:26:07", + "duration_sec": 5.523 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..0b097136f4896f9d5deee418a166899244ba88a2 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9e0cdf8d1c63e1c6f50e3ef553b75abd8d288f85 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_generate.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4a513b6c64f8729a0592980505e361f0ede7a9ea --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_train.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..dd7ddb7bea931b54947b0e3d88527c9ad137178d --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=80, + pac=5, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c2bcd6913d6fc848e66bb9fdda340a1e05554b7 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06f4bd2740e2d92884bded619bc47b8652f747c537f543c463da26b4ecc3b2e9 +size 41563 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/ctgan_metadata.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/gen_20260504_162321.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/gen_20260504_162321.log new file mode 100644 index 0000000000000000000000000000000000000000..121e2d02ec34b94bf50346ead1a3d5eb919fd14f --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/gen_20260504_162321.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e9a8bd298558c0d0c5121a8f68fd97cdcf80e5e349de95203e772ebf7d6a0d7 +size 297 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/input_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..185ce48c7b3861004a1ae943ced45428837221e1 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:89001f5bc9d51b23712eb3bb71f85ef655ff710707b825e7ae6ac1d586cc395b +size 294947 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/train_20260504_162240.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/train_20260504_162240.log new file mode 100644 index 0000000000000000000000000000000000000000..296e5914be6b77b0dc29b560dbcc0daa497eab65 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/train_20260504_162240.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a80e86a687df24cbf5cef0f767d6091a7728594215f9fdc8bbf3e4c508252b43 +size 20165 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3e04926e55a12a23c91b123f0fdf38caa78c98fe --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/run_config.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..7e77ec9db818a984c777289e51c82c6925a25d92 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:22:40", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "80", + "CTGAN_DEFAULT_EPOCHS": "100", + "CTGAN_DISCRIMINATOR_DIMS": "32,32", + "CTGAN_EMBEDDING_DIM": "16", + "CTGAN_GENERATOR_DIMS": "32,32", + "CTGAN_PAC": "5" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/runtime_result.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..c943130baae43b3a0a41eb9a2f1e7079a453a38e --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_162240", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:22:40", + "ended_at": "2026-05-04T16:23:21", + "duration_sec": 41.673 + }, + "generate": { + "started_at": "2026-05-04T16:23:21", + "ended_at": "2026-05-04T16:23:27", + "duration_sec": 5.382 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..a84ffcef8e6e4db6b0ae03f2de9ce8b993bf33d0 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b8bed5508038be911353f57dfc64f5c3fd221056 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_generate.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ff855eb274e019fb91b6476423543290aaf540c3 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_train.py b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e0dd700a1ac2533023d1f50e947dbe32044a8098 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=100, + pac=10, + epochs=200, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt") \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv new file mode 100644 index 0000000000000000000000000000000000000000..475fb7eee9738c341bde5529d780b2427ba4b094 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dcd29852524ef13d0009da75adaf69c0d09dc640b8fbb13ba387045198c5c725 +size 40940 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/ctgan_metadata.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/gen_20260504_162437.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/gen_20260504_162437.log new file mode 100644 index 0000000000000000000000000000000000000000..fc73a61c7203640af7a492332124986abd5d10c0 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/gen_20260504_162437.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa41d337e65206a8c960582d8745c3fa02cfd860fe74c08c6c3f3d519666af42 +size 297 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/input_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c1729bc2cff9e82169bbe4211dae27af200fe92d --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e731fdc58e37596134fe5bcee815a30e38f087c1aa3dc2944f679c1f1549cdb +size 338275 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/train_20260504_162339.log b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/train_20260504_162339.log new file mode 100644 index 0000000000000000000000000000000000000000..80e17b806b94f465ac519b8881a2409fcf01ee0b --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/train_20260504_162339.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94b3afa2766398447e2c41d92e271ce3a826d7c72cc942e24d400d387a03beb0 +size 38648 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/public_gate_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/staged_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ed2127908350c287e7b5c03a577bcba2ffe54ddb --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv", + "val_csv": 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} + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/run_config.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..1c6884477d3f20bea1a3d689207b894b3a522c28 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:23:39", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "100", + "CTGAN_DEFAULT_EPOCHS": "200", + "CTGAN_DISCRIMINATOR_DIMS": "64,64", + "CTGAN_EMBEDDING_DIM": "32", + "CTGAN_GENERATOR_DIMS": "64,64", + "CTGAN_PAC": "10" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/runtime_result.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7b60a25f52ff5134761945aa34333c69f75663d0 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_162339", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:23:39", + "ended_at": "2026-05-04T16:24:37", + "duration_sec": 57.631 + }, + "generate": { + "started_at": "2026-05-04T16:24:37", + "ended_at": "2026-05-04T16:24:42", + "duration_sec": 5.235 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/adapter_report.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..4c71e56b64844e27c80124db363f5c163b8af547 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/model_input_manifest.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..578cbcb2eaf976baa0f04ce2aaf4ef92ca0996d1 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_X_host.npy b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..2c631eb05814d7aa116816e9799b9c52f8c0e566 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:260745c93bbaaa167bd761bd653a194f7b25b477b3929a24e343741a3c4fa280 +size 38824 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_gen.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..17539111eb302e45301a2e3119e1107630e1ed23 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_044951/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(1382)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_044951/forest-c2-1382-20260505_045141.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_meta_host.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..be467ac019d08a03e8ba5c0d436f59170db78786 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["buying", "maint", "doors", "persons", "lug_boot", "safety", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_train.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bd9da13f95fdf53491b4aceb8609a2877543692a --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_044951/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_044951/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=4 " + f"n_estimators=5 duplicate_K=2 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=4, n_estimators=5, duplicate_K=2, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_044951/forestdiffusion_model.joblib') diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/forest-c2-1382-20260505_045141.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/forest-c2-1382-20260505_045141.csv new file mode 100644 index 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b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/test.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/train.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/val.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/train_20260505_044951.log b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/train_20260505_044951.log new file mode 100644 index 0000000000000000000000000000000000000000..67ce01eb65a4fadcddaa5fda4bf6e7ab96612449 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_044951/train_20260505_044951.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9a7c8d9934017c6d3659a61b7edefb87d5d94cf6dff3ffc3ba673df6c951b7 +size 431 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_X_host.npy b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..2c631eb05814d7aa116816e9799b9c52f8c0e566 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:260745c93bbaaa167bd761bd653a194f7b25b477b3929a24e343741a3c4fa280 +size 38824 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_gen.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa9d6ee91911f4aa207e3a11a9a9da128dcaf42 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(1382)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/forest-c2-1382-20260505_045714.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_meta_host.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..be467ac019d08a03e8ba5c0d436f59170db78786 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["buying", "maint", "doors", "persons", "lug_boot", "safety", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_train.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9b2ad1f025b4a2d8366588aafaf28605b051b5a2 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=6 " + f"n_estimators=10 duplicate_K=3 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=6, n_estimators=10, duplicate_K=3, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/forestdiffusion_model.joblib') diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/forest-c2-1382-20260505_045714.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/forest-c2-1382-20260505_045714.csv new file mode 100644 index 0000000000000000000000000000000000000000..d89a90a40629ab2e862d026e782b81c79b932297 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/forest-c2-1382-20260505_045714.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4225d81003c8b72fa216e26e6dbe4fcdba5ce058b59b3a0fee001d6966c1c5ed +size 58201 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/forestdiffusion_model.joblib b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..0aaadeca882b0bd1f83d395e667b3941ea495624 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bcdb8180cb3e9d5eaadfc09a77d5ff1ff107313081807bbe2238623ed50fff94 +size 1322726 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/gen_20260505_045714.log b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/gen_20260505_045714.log new file mode 100644 index 0000000000000000000000000000000000000000..f2f37ecbc7ffefdaf91879cbb1bfeab8c949d9a4 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/gen_20260505_045714.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa31ca2293047e22412fe8606e893d35145638fa58c9382a76527476e40dd21b +size 293 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/input_snapshot.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..24ba74f272fb2c967af52a4ad4947955f45646ea --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/models_fd/model.joblib b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..0aaadeca882b0bd1f83d395e667b3941ea495624 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bcdb8180cb3e9d5eaadfc09a77d5ff1ff107313081807bbe2238623ed50fff94 +size 1322726 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/public_gate_report.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/staged_input_manifest.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..86047349940836c0a061834fe91216a46b1bd97b --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + 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"dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "forestdiffusion", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/models_fd/model.joblib", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/forest-c2-1382-20260505_045714.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "FORESTDIFFUSION_DUPLICATE_K": "3", + "FORESTDIFFUSION_MAX_DEPTH": "3", + "FORESTDIFFUSION_MAX_TRAIN_ROWS": "10000", + "FORESTDIFFUSION_N_ESTIMATORS": "10", + "FORESTDIFFUSION_N_JOBS": "1", + "FORESTDIFFUSION_N_T": "6" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/runtime_result.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..6d70926b55c6621c98e8e058f1af0a9ac1ee8d0a --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "forestdiffusion", + "run_id": "forest-c2-20260505_045157", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/forest-c2-1382-20260505_045714.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/models_fd/model.joblib" + }, + "timings": { + "train": { + "started_at": "2026-05-05T04:51:57", + "ended_at": "2026-05-05T04:57:14", + "duration_sec": 316.955 + }, + "generate": { + "started_at": "2026-05-05T04:57:14", + "ended_at": "2026-05-05T04:57:17", + "duration_sec": 2.816 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/adapter_report.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..de5e43506c1ed6734e3767b27b5ad3ac181a0dad --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/adapter_transforms_applied.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/model_input_manifest.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..93fa2cabb9d2e1e33e8cd036659b427edfe08345 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "forestdiffusion", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045157/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/staged_features.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/test.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/train.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/val.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/train_20260505_045157.log b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/train_20260505_045157.log new file mode 100644 index 0000000000000000000000000000000000000000..f157801bab13ad67ab553305d253241b09eb7a2b --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045157/train_20260505_045157.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f4a949d653b6a86edf103dd468583f5e7458e752df8594059037c95ab5851f2 +size 432 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_X_host.npy b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..2c631eb05814d7aa116816e9799b9c52f8c0e566 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:260745c93bbaaa167bd761bd653a194f7b25b477b3929a24e343741a3c4fa280 +size 38824 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_gen.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..aef7e91dbd18f5b508935808fbd5b18f1e4c13dd --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(1382)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/forest-c2-1382-20260505_050632.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_meta_host.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..be467ac019d08a03e8ba5c0d436f59170db78786 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["buying", "maint", "doors", "persons", "lug_boot", "safety", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_train.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..066be05c04b9454efa1910225deb9632a31a5fef --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=8 " + f"n_estimators=10 duplicate_K=4 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=8, n_estimators=10, duplicate_K=4, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/forestdiffusion_model.joblib') diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/forest-c2-1382-20260505_050632.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/forest-c2-1382-20260505_050632.csv new file mode 100644 index 0000000000000000000000000000000000000000..2024b173eea4926316017cd9f9de69cd099cf234 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/forest-c2-1382-20260505_050632.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f43ecab16c7f88a98f8e49b8006a10a4c0307cac414effd57bbb97f67f959af9 +size 58265 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/forestdiffusion_model.joblib b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..252dbc75212fc2d0857fcc80e4f7f2a9054bfbcc --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:131fd1453c5ce54e95649b6dcab0d1290cc35cdfc369fe5838523a7721649df3 +size 2388868 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/gen_20260505_050632.log b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/gen_20260505_050632.log new file mode 100644 index 0000000000000000000000000000000000000000..ed461ef392bcf4757eee5611cf95242e2c9bfc0f --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/gen_20260505_050632.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee19b1205d03ca7a7813e03a4f2104b3f38bc89837335dcb851640fdaee32fdc +size 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/staged/forestdiffusion/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "FORESTDIFFUSION_DUPLICATE_K": "4", + 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+ } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_045730/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/staged_features.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/test.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/train.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/val.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/train_20260505_045730.log b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/train_20260505_045730.log new file mode 100644 index 0000000000000000000000000000000000000000..78bf25b1d4d4efdad97a26ff9ffb72d821d687bd --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_045730/train_20260505_045730.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f79f9b452f7c22caf1164d9312ae746565677810f387e3fda10d05964c37ade4 +size 432 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_X_host.npy b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..2c631eb05814d7aa116816e9799b9c52f8c0e566 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:260745c93bbaaa167bd761bd653a194f7b25b477b3929a24e343741a3c4fa280 +size 38824 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_gen.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..fa8d5a2afdd4d9516702bb36a9e5890d073e15cf --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(1382)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/forest-c2-1382-20260505_052347.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_meta_host.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..be467ac019d08a03e8ba5c0d436f59170db78786 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["buying", "maint", "doors", "persons", "lug_boot", "safety", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_train.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6f6c3d4e3ed7d5a7f7774fe1c639bbdc5f11e3f5 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=10 " + f"n_estimators=15 duplicate_K=5 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=10, n_estimators=15, duplicate_K=5, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/forestdiffusion_model.joblib') diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/forest-c2-1382-20260505_052347.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/forest-c2-1382-20260505_052347.csv new file mode 100644 index 0000000000000000000000000000000000000000..8f3ce4d11a60dc282d0d4584121bb8a7cba5a895 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/forest-c2-1382-20260505_052347.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:facfb16ae0476dff908cb7f16779d7e15bcdb636db6146b7217f6afcbc3ec4a6 +size 57878 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/forestdiffusion_model.joblib b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..cef00e12116f17e3661628234a1f4a9158a5392c --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49f48029400b8e2920df9ec95f52f8253fd5ed887615568b5b3436e4203bba33 +size 4275004 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/gen_20260505_052347.log b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/gen_20260505_052347.log new file mode 100644 index 0000000000000000000000000000000000000000..dd7a560c76223ee17fa27d56bcc227b260962e52 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/gen_20260505_052347.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:719999989388a1639c45cd83725e93a297ed6a1c116289197f3ff344d31bd5b0 +size 294 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/input_snapshot.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..24ba74f272fb2c967af52a4ad4947955f45646ea --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/models_fd/model.joblib b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..cef00e12116f17e3661628234a1f4a9158a5392c --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49f48029400b8e2920df9ec95f52f8253fd5ed887615568b5b3436e4203bba33 +size 4275004 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/public_gate_report.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/staged_input_manifest.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..60e2d05844ec0f8769641c32a3d5e1e0c372979f --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/test.csv", + "features_json": 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0000000000000000000000000000000000000000..299e04922fafd5c3b5792026e5947463a4bf0f07 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T05:06:48", + "dataset_id": "c2", + "model": "forestdiffusion", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "forestdiffusion", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/models_fd/model.joblib", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/forest-c2-1382-20260505_052347.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "FORESTDIFFUSION_DUPLICATE_K": "5", + "FORESTDIFFUSION_MAX_DEPTH": "4", + "FORESTDIFFUSION_MAX_TRAIN_ROWS": "30000", + "FORESTDIFFUSION_N_ESTIMATORS": "15", + "FORESTDIFFUSION_N_JOBS": "1", + "FORESTDIFFUSION_N_T": "10" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/runtime_result.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..1ee8366529f4752a669ca6200a6080ba31ed642c --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "forestdiffusion", + "run_id": "forest-c2-20260505_050648", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/forest-c2-1382-20260505_052347.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/models_fd/model.joblib" + }, + "timings": { + "train": { + "started_at": "2026-05-05T05:06:48", + "ended_at": "2026-05-05T05:23:47", + "duration_sec": 1018.679 + }, + "generate": { + "started_at": "2026-05-05T05:23:47", + "ended_at": "2026-05-05T05:23:51", + "duration_sec": 3.661 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/adapter_report.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..4dfc6f5816b64c133cc044d2fe865bad6c480698 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/adapter_transforms_applied.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/model_input_manifest.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..24040db334105f039110d80596e3adfba12c55b6 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "forestdiffusion", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + 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"nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_050648/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/staged_features.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/test.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/train.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/val.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/train_20260505_050648.log b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/train_20260505_050648.log new file mode 100644 index 0000000000000000000000000000000000000000..037ccf6a293552b0c364654b059fd94c4cddf349 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_050648/train_20260505_050648.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76339749da53196ffe091d0f0987b3be5d0fdbc982279b010394e8743340b139 +size 434 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_X_host.npy b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..2c631eb05814d7aa116816e9799b9c52f8c0e566 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:260745c93bbaaa167bd761bd653a194f7b25b477b3929a24e343741a3c4fa280 +size 38824 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_gen.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..cb7cc1d86621b2816568bb9f30e83e8b70b1c180 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_052404/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(1382)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_052404/forest-c2-1382-20260505_054609.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_meta_host.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..be467ac019d08a03e8ba5c0d436f59170db78786 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["buying", "maint", "doors", "persons", "lug_boot", "safety", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_train.py b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..15945893e0dadd4c7aaa3f988835707b3a5d23ee --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_052404/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_052404/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=10 " + f"n_estimators=20 duplicate_K=5 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=10, n_estimators=20, duplicate_K=5, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_052404/forestdiffusion_model.joblib') diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/forest-c2-1382-20260505_054609.csv b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/forest-c2-1382-20260505_054609.csv new file mode 100644 index 0000000000000000000000000000000000000000..269f15ae39675402a7e01b58f06d0f8675e98947 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/forest-c2-1382-20260505_054609.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b89d5dffaa00afa5cbc9342f6b497e507200e6f1fb7fd238c6791d42db2c2786 +size 58075 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/forestdiffusion_model.joblib b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..ca975ff6bf3b4d9757d2abae1ce881753dc8faf0 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7d57f1e8a5ed680814e33d65f515d7c63baf7d15e4c737a1b95cfd994ad1507 +size 5606758 diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/gen_20260505_054609.log b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/gen_20260505_054609.log new file 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a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/run_config.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..e486fda9bc345cdf801d7ea2df02033996b6b657 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T05:24:04", + "dataset_id": "c2", + "model": "forestdiffusion", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_052404", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "forestdiffusion", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + 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newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/staged/forestdiffusion/adapter_report.json b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/staged/forestdiffusion/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..f2de4275e33d8234d6e3a5b3e3dd2f83814e4195 --- /dev/null +++ b/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/staged/forestdiffusion/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260505_052404/staged/forestdiffusion/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/forestdiffusion/forest-c2-20260505_052404/staged/forestdiffusion/adapter_transforms_applied.json 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--git a/hyperparameter/c2/realtabformer/rtf-c2-20260505_021413/train_20260505_021413.log b/hyperparameter/c2/realtabformer/rtf-c2-20260505_021413/train_20260505_021413.log new file mode 100644 index 0000000000000000000000000000000000000000..78da13f3bf209655260ad3131a4e7c8e2ee09ffa --- /dev/null +++ b/hyperparameter/c2/realtabformer/rtf-c2-20260505_021413/train_20260505_021413.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7c56cd1b048b3e1a67bc475282f9fbd1e26c09fad1f8e0aff53bef958b05004 +size 54842 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/_tabbyflow_gen.py b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..12e50a1e56f6d8391bed9cb95f3d37f559471f77 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/tabular_bundle/pipeline_c2" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_c2/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/tabbyflow-c2-1382-20260504_235548.csv") diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/_tabbyflow_train.py b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..646d7b24e08c8cb437d4f45c091168ad13b8323f --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/tabular_bundle/pipeline_c2" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "100" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/gen_20260504_235548.log b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/gen_20260504_235548.log new file mode 100644 index 0000000000000000000000000000000000000000..c603561175753da1ba0b090f006185f9b1fad987 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/gen_20260504_235548.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36cb08e3b055829024e1eb6561fa74f687d4486fdabb40dd5ace6332eefcb81e +size 3414 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/input_snapshot.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..89c9d5855c86fb753a25070ff5c19d45d1e2fb9e --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + 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"sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/models_tabbyflow/trained.pt b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/models_tabbyflow/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..f063a878c470d9d40f79db79881596e1e497dffd --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/models_tabbyflow/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adb734fec12f2251befd371dca69e481b19464aded2a6823105a01b4be6ddbe5 +size 40 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + 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b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..65a57d920ab0c01f197f928c00341bc02a13ffce --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + 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0000000000000000000000000000000000000000..84103edb1cc74f44b2deae1a8bcda593c016ba25 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/run_config.json @@ -0,0 +1,42 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T23:53:39", + "dataset_id": "c2", + "model": "tabbyflow", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabbyflow", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 100, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/models_tabbyflow/trained.pt", + "output_csv": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235339/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABBYFLOW_GPUS": "device=0", + "EFVFM_EVAL_NUM_SAMPLES": "512", + "EFVFM_SAMPLE_BATCH_SIZE": "64" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/runtime_result.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..90a922e2fea7cb8a3267345199d88bd7b0e5c7dc --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235339/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabbyflow", + "run_id": "tabbyflow-c2-20260504_235339", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + 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0000000000000000000000000000000000000000..b72e43c40f6e082054c58e431722ee8000c8699a --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/tabular_bundle/pipeline_c2" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_c2/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/tabbyflow-c2-1382-20260504_235957.csv") diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/_tabbyflow_train.py b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..80d6bcd905a2552418254e7993a13c8ef028f2cf --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/tabular_bundle/pipeline_c2" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "200" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/gen_20260504_235957.log b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/gen_20260504_235957.log new file mode 100644 index 0000000000000000000000000000000000000000..a0b69479648d2901ea3d252c4d6c5a4a73622792 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/gen_20260504_235957.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a718b5b364468e64a6997ea6ef81c859ebd9a0a4d2659bc8657888de3e24e16 +size 3415 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/input_snapshot.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..89c9d5855c86fb753a25070ff5c19d45d1e2fb9e --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": 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a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/public_gate/public_gate_report.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c2b719fb665f2e4d0601de776a83d7c2cfa780a4 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + 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"unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/run_config.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..1d4e458f6ce8733250da1064530fd79153a824c2 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/run_config.json @@ -0,0 +1,42 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T23:56:18", + "dataset_id": "c2", + "model": "tabbyflow", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabbyflow", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 200, + "output_dir": null, + "model_dir": null, + "work_dir": 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"train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260504_235618/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABBYFLOW_GPUS": "device=0", + "EFVFM_EVAL_NUM_SAMPLES": "512", + "EFVFM_SAMPLE_BATCH_SIZE": "64" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/runtime_result.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..785dd8f7950da266c23a6a4f452546d8cfe51933 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": 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b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260504_235618/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git 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0000000000000000000000000000000000000000..2abd72581e9acd7bfd6ee9562649a40561fb1a8b --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/tabular_bundle/pipeline_c2" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_c2/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/tabbyflow-c2-1382-20260505_000530.csv") diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/_tabbyflow_train.py b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2defabb416384856808baa4bb09f230a248b9641 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/tabular_bundle/pipeline_c2" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "300" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/gen_20260505_000530.log b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/gen_20260505_000530.log new file mode 100644 index 0000000000000000000000000000000000000000..b274b9f9c41b7fa0875e8354059ecac90c84b619 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/gen_20260505_000530.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3ca6eb4fc7317dca32459c01f9ad1f442316a4fe41ee5cce0307d7e4d72d1ba +size 3414 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/input_snapshot.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..89c9d5855c86fb753a25070ff5c19d45d1e2fb9e --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": 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a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/public_gate/public_gate_report.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9972e638ad6d10333a2f472a7919910862216c88 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + 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"unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/run_config.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..d0db1aa7120455b3539cd1873de5711ea5fa4e32 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/run_config.json @@ -0,0 +1,42 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T00:00:25", + "dataset_id": "c2", + "model": "tabbyflow", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabbyflow", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 300, + "output_dir": null, + "model_dir": null, + "work_dir": 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"train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000025/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABBYFLOW_GPUS": "device=0", + "EFVFM_EVAL_NUM_SAMPLES": "1024", + "EFVFM_SAMPLE_BATCH_SIZE": "64" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/runtime_result.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..5db0fbea92dd31921c26777395ca3fe95265ba72 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000025/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": 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0000000000000000000000000000000000000000..3507f97db77a1db3d0af3fb60ccc136ccf2bb58c --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602/tabular_bundle/pipeline_c2" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_c2/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602/tabbyflow-c2-1382-20260505_001419.csv") diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/_tabbyflow_train.py b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..42671040dd84d2038199e5336a3d73e888bedf14 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602/tabular_bundle/pipeline_c2" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/gen_20260505_001419.log b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/gen_20260505_001419.log new file mode 100644 index 0000000000000000000000000000000000000000..388010eb37dfdf9ed2e5b148a9429cc5750402d2 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/gen_20260505_001419.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:38c44d8bcf44873b92c94c2e141c6a068fd4c9dfec03aa085aedd7485ee383b4 +size 3414 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/input_snapshot.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..89c9d5855c86fb753a25070ff5c19d45d1e2fb9e --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": 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"missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": 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a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/public_gate/public_gate_report.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..efe1f5b40d60b4574c0bc5534b058b0bcbfa2ad6 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/run_config.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..950ba8c85933b65786c1c238f65085f09bd77b2a --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_000602/run_config.json @@ -0,0 +1,42 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T00:06:02", + "dataset_id": "c2", + "model": "tabbyflow", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_000602", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabbyflow", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 500, + "output_dir": null, + "model_dir": null, + "work_dir": 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0000000000000000000000000000000000000000..07cbf4700884c5d256587b9c6cc1dac664149862 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabular_bundle/pipeline_c2" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_c2/adapter_efvfm/model_700.pt", + "--num_samples_to_generate", str(int(1382)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabbyflow-c2-1382-20260505_002755.csv") diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/_tabbyflow_train.py b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4f9317b434aaf67a794726f47eb34cc4b57e1ba2 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabular_bundle/pipeline_c2" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "700" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/gen_20260505_002755.log b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/gen_20260505_002755.log new file mode 100644 index 0000000000000000000000000000000000000000..041cb9b4de94140da3e491299736fb85e2ac3095 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/gen_20260505_002755.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05ba45ce45592cb71a64741c3eed27546f78a286351c265f648edc2c81d4ca87 +size 3415 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/input_snapshot.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..89c9d5855c86fb753a25070ff5c19d45d1e2fb9e --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": 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"missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": 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a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/public_gate/public_gate_report.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a991314393841feeb2d38bcb06b12de9f9883ce1 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + 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"unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/run_config.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..2ece9feb0996015dcefc3febd4c949df84fc924c --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/run_config.json @@ -0,0 +1,42 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T00:14:51", + "dataset_id": "c2", + "model": "tabbyflow", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabbyflow", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 700, + "output_dir": null, + "model_dir": null, + "work_dir": 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"train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABBYFLOW_GPUS": "device=0", + "EFVFM_EVAL_NUM_SAMPLES": "2048", + "EFVFM_SAMPLE_BATCH_SIZE": "128" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/runtime_result.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7aba8336651e37f8ab63c3287dd77f8923ed8453 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": 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a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/tabbyflow/model_input_manifest.json b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/tabbyflow/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0f0768e93a2ffcf65cc4cd22da8a0d1a9cc03c6c --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/staged/tabbyflow/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabbyflow", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": 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+ "exp_name": "adapter_efvfm", + "dataname": "pipeline_c2", + "steps": 700 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabular_bundle/pipeline_c2/X_cat_test.npy b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabular_bundle/pipeline_c2/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabular_bundle/pipeline_c2/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabular_bundle/pipeline_c2/X_cat_train.npy b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabular_bundle/pipeline_c2/X_cat_train.npy new file mode 100644 index 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b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/tabular_bundle/pipeline_c2/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/train_20260505_001451.log b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/train_20260505_001451.log new file mode 100644 index 0000000000000000000000000000000000000000..1ec0deae1d0a8743ecebcc2e0096369d06b5ad0d --- /dev/null +++ b/hyperparameter/c2/tabbyflow/tabbyflow-c2-20260505_001451/train_20260505_001451.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f641d8956fe65f8dc173332fa731269c1df925e8c9bd76f177a46dae459d30c1 +size 397667 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_sample_r0.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..acd07a0b4e4bcf12e9a8c3908b4d5a6367d1d0d6 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1382 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_train.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ddb041209cab17e11561f35f7bc9132dfe91f27a --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..b32437de5ec87ecfb85758bc6c686c27a4499f45 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..d2a6930b9108b5e2a8f747ee1a781a87b6bc55bb --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/y_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/gen_20260504_181140_r0.log b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/gen_20260504_181140_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..9fcd131940297336c95b3df388afad1eccd0f03f --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/gen_20260504_181140_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:699ec388503c3fef971e172974cee64500a765cff8855575df673d28450f3420 +size 25662 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/input_snapshot.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..772021148f055243c89ddfd9b6fa388ee33d05ea --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..862303f0519083c21dea3de3dfc30aaabc4a90fd --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ab974a88850d8041e57dd6341c43e40877629a6e0adaf17a6152e0165922ed3 +size 16885 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_unnorm.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..b648311a8f6441d68bdba8e8f5737657ea79a9e1 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06a6d4b433422299123e5388de04c0166e6143ab0788c38fe47f40da1c9c727d +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..d2a6930b9108b5e2a8f747ee1a781a87b6bc55bb --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/loss.csv b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..d5c7da855b8ef5835b2dcde545c19bbb7c72d227 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57f250e7354878e53460575baff2b9f2824f988cc8ff11b40b4c980408373c4f +size 505 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/model.pt b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..127c529e5a524f3d4d02fdfd5c84087cabb70040 --- /dev/null +++ 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0000000000000000000000000000000000000000..aa9afff4d061333b18828274b1072d1b884f162c --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b1a722d369027a22bed702e3539c6fefa976741570c4c0a2f8546e2978c3d7 +size 11184 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + 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"parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + 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a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/train_20260504_181103.log b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/train_20260504_181103.log new file mode 100644 index 0000000000000000000000000000000000000000..d1bb741fbeb6f2fcd03950e3432f561861e3193f --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181103/train_20260504_181103.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14cf68fa12bf8a9e4d7cc59a892456d54b976c676e71e3d6d92486ff58ee32d0 +size 766 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_sample_r0.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..9a94d2a41b2dcf189c488b2c4912a7dadb8e35ee --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1382 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_train.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9930b28e955dff462191f2525b3a0fa738c7ca34 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1f89e2e3f7a297157b784a249a11249eeb27c9d0 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..6d926ececa1d9852eded91a9d1abf795c2056384 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/y_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/gen_20260504_181327_r0.log b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/gen_20260504_181327_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..877963b0fe6be73beadf40653b39c241200f7327 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/gen_20260504_181327_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b93d275fe4edebae555a0b578fb14998a7b5164eb5c79abe5b9d996cf4d671a9 +size 126462 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/input_snapshot.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..772021148f055243c89ddfd9b6fa388ee33d05ea --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b8ce5b95bc8251728c835c81edbb5e6a6185d9c9 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e77f34c91b60cbce837540a3e08097ecb343703ce3d0246710819ef447e1d54 +size 16885 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_unnorm.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..7ea825ed64e038b9ddc5863f265a1cd55ca73819 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c3584fe9ca4a1b527e84e477f6fd5695011f2223445495240a83d02ecb2075e +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..6d926ececa1d9852eded91a9d1abf795c2056384 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/loss.csv b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..5f996a25328bdaab56049c4f49f7567d240eace0 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35ec4ea19e9311190f8c430e41ae865decb12a205a13591d7f3d008db462406e +size 1251 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/model.pt b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..b80bbad06939fc0ead1b779678d99d473f5b2074 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5354cbe787b33e8d9ec39f348ab76a760a7bcf5359143c2cb10cb84c72c79c0 +size 566934 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/model_ema.pt b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/model_ema.pt new file mode 100644 index 0000000000000000000000000000000000000000..348701ed22a7b54c87bcf068ba681a3b7d763b59 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a111267f57c5e1493761867d6869283240e2c0499acd84e32bf616126181ca68 +size 567778 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/y_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..aa9afff4d061333b18828274b1072d1b884f162c --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b1a722d369027a22bed702e3539c6fefa976741570c4c0a2f8546e2978c3d7 +size 11184 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + 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"2026-05-04T18:14:13", + "duration_sec": 45.511 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/staged_features.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/test.csv b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/train.csv b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ 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"example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv new file mode 100644 index 0000000000000000000000000000000000000000..a735ab2dad04a579c9f5d5388a4fa8ac2c1ece91 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ef7b04421aefbad10890e4ab781f979634cee232355d0c0fa801837604385a7 +size 38745 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/train_20260504_181207.log b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/train_20260504_181207.log new file mode 100644 index 0000000000000000000000000000000000000000..1a69546b543fcdb5aad504909aefe195ec2cdd07 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181207/train_20260504_181207.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c43a64728eefff6ad798ad80865649a2403c1ec2aeda34c198141392ff04fb7a +size 1078 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/_tabddpm_sample_r0.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..5a16b29418ff5da85d9d6ffe0a450311be1c247e --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1382 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/config_sample_20260504_181619_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/tabddpm-c2-1382-20260504_181619.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/tabddpm-c2-1382-20260504_181619.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/_tabddpm_train.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d4834766323d6d9515c49b424744ab120378239f --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..2ec35ddb65f513901010a60b35f6ad75a88213dd --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 8000 +lr = 0.0005 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/config_sample_20260504_181619_r0.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/config_sample_20260504_181619_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..d552d0b7eb6a370ebb22fe960c5759ecfd13c09f --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/config_sample_20260504_181619_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 8000 +lr = 0.0005 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/y_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/gen_20260504_181619_r0.log b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/gen_20260504_181619_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..db01ea644b3b0792f6c2c4b77262ae9acad186be --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/gen_20260504_181619_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:83317c90c84b3a89b520ba38e3b60a65d80ab88d319c545dfa9eff3c6ad87c86 +size 231462 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/input_snapshot.json 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"cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..dcf14c2ad4bf0b84d6d4f891dfcf9c4465a5bfd1 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:034eff6755597ddf2cea32aeeebc6aea7abe0bb2ecd1296540a156a1fd7b15d3 +size 16885 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/X_cat_unnorm.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..56d962ff9d5a69c1c9bcd4e4b751b0ed993b00fb --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36d38a17ec627db9cf06f610863f510f60290c0ad1f074cbc733f3fa695c8e78 +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..d552d0b7eb6a370ebb22fe960c5759ecfd13c09f --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181426/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 8000 +lr = 0.0005 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/loss.csv b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..aba398c650e6aff07d73d9cbdd0e42751eb972f8 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11089dc4d22b4e0c33be6b1d0af38e72761aa5c755fae8fc3614cb11aa41d4a2 +size 1993 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/model.pt b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..86eb4523a4f131d04169ecafc52d18a86c0a03cf --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d52da738ac55be05eb734ccf926e7e51252ead859e1010257617d98403aeba61 +size 566934 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/model_ema.pt b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/model_ema.pt new file mode 100644 index 0000000000000000000000000000000000000000..72e3148f4e325d8c4f3840f455957e65f976a752 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f352a5b68f4112987abd797ca4d890e774dbec50c511370bc209e6376d854293 +size 567778 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/y_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..aa9afff4d061333b18828274b1072d1b884f162c --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b1a722d369027a22bed702e3539c6fefa976741570c4c0a2f8546e2978c3d7 +size 11184 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + 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a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/tabddpm-c2-1382-20260504_181619.csv b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/tabddpm-c2-1382-20260504_181619.csv new file mode 100644 index 0000000000000000000000000000000000000000..6b41078af153f0ede97fef3d916ea8850f6bf4a9 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/tabddpm-c2-1382-20260504_181619.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ca2259e7e505d17ea935caa55b18c4527a22c776e4c9e9a83b76280bc1a0764c +size 38745 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/train_20260504_181426.log b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/train_20260504_181426.log new file mode 100644 index 0000000000000000000000000000000000000000..b7b50a4d71afa241143b375960d2c6c7caf568d4 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181426/train_20260504_181426.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7356520fcdab117b252867a1778157511aaf0ff67df6339f5519fb8d395f1135 +size 1389 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/_tabddpm_sample_r0.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..6d9a374682ff30bdb5d588e45d4284f4aa270951 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1382 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/config_sample_20260504_182010_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/tabddpm-c2-1382-20260504_182010.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/tabddpm-c2-1382-20260504_182010.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/_tabddpm_train.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..dec896327b2d1773db061836a5353586f89d63c6 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..165148ade1be7bc62b1a072d61032b198169fa20 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 10000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/config_sample_20260504_182010_r0.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/config_sample_20260504_182010_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..f2d1b4d66e9c68954b947c156a5d889dbafc9dbf --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/config_sample_20260504_182010_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 10000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/data/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/data/info.json 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+parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 10000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/output/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/output/info.json new file mode 100644 index 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a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/_tabddpm_sample_r0.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..4ad1c4ed13237dbf738e182030a0d2ff5d707e16 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1382 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/config_sample_20260504_182502_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/tabddpm-c2-1382-20260504_182502.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/tabddpm-c2-1382-20260504_182502.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/_tabddpm_train.py b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c70d5237ceaee08993960717f5a3765c2b836593 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..41b4af4edebd638815e97326eef1d5251a1e2fb3 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 15000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 64 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/config_sample_20260504_182502_r0.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/config_sample_20260504_182502_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..bac8e3b718ef6088faf5ed2aebb811815a1d1168 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/config_sample_20260504_182502_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 15000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 64 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/data/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/data/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/data/info.json new file mode 100644 index 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+1,36 @@ +{ + "dataset_id": "c2", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/X_cat_train.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..3f83bec78234d1459a4d98455434f862d2670ee0 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4c88a54d5cd1488a7c42eb7f8ebf78082d55e3d10f3237eb1d8a3b48a73469d +size 16885 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/X_cat_unnorm.npy b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..bbaf76999f9bdd14610605c235bba3b52291dc6d --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97564e99e98681836a5663b938896f89a802fcf8bfa59167fa52b0c83eb6748d +size 66464 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/config.toml b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..bac8e3b718ef6088faf5ed2aebb811815a1d1168 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_182138/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 15000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 64 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/info.json b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/loss.csv b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..1c96baa2d276b2f13210ee831a63e0bd30c93d28 --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:25c503d9708cca256ea209475660a1ba7160a604acf047482a5ae2305e55fdff +size 3794 diff --git a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/model.pt b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/output/model.pt new file mode 100644 index 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a/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/train_20260504_182138.log b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/train_20260504_182138.log new file mode 100644 index 0000000000000000000000000000000000000000..4ea692f141c46798da60b7cc869ae0e7de6008aa --- /dev/null +++ b/hyperparameter/c2/tabddpm/tabddpm-c2-20260504_182138/train_20260504_182138.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9030650113e49b0e1eb037c5f21d6dd7797712efef6b578a5fe505867ebf443b +size 2154 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/_tabdiff_gen.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..5db0a28dd6dd3d6becf14a0ed9b2f82b72fe9e32 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191431/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_c2/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191431/tabdiff-c2-1382-20260504_191604.csv") diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/_tabdiff_train.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..afec795f9ec015b99b725a8c095ef372950fc46f --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191431/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "100" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/gen_20260504_191604.log b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/gen_20260504_191604.log new file mode 100644 index 0000000000000000000000000000000000000000..1f6f0e5dd61d6b18dc5f824316c224f1a98f12d1 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/gen_20260504_191604.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab3a5509999584817b44889c4c489569f83601d7961819af04e647f90b362741 +size 4472 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/input_snapshot.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..6d83542090e9720c485d72abe1bb75a800175917 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191431/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": 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0000000000000000000000000000000000000000..1e23f78f0891bbc9ec624e83da429b584572200b --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191626/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_c2/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191626/tabdiff-c2-1382-20260504_191940.csv") diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/_tabdiff_train.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..246107262f47ba53ca9a7e947c57c8ba77ceb75f --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191626/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "200" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/gen_20260504_191940.log b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/gen_20260504_191940.log new file mode 100644 index 0000000000000000000000000000000000000000..4dbc995a9a3fe2072bbc830baeb572c507730870 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/gen_20260504_191940.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d138a4370f64234cee2d205acca77cb5e08e962d0d49de46c23d45a737e66c1f +size 4471 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/input_snapshot.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..6d83542090e9720c485d72abe1bb75a800175917 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": 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+ ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/run_config.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..e6b80ba3ba33e5e366fd1be204f4d2326e20f6d0 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/run_config.json @@ -0,0 +1,40 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T19:16:26", + "dataset_id": "c2", + "model": "tabdiff", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191626", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabdiff", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 200, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191626/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_191626/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABDIFF_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/runtime_result.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..b6d979665fd419a78050126d337d07768f6157fe --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "run_id": "tabdiff-c2-20260504_191626", + "public_gate_status": "pass", + "adapter_ready_status": 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index 0000000000000000000000000000000000000000..276f957073732952c72cf5347d7cd58792b0d2d4 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_191626/train_20260504_191626.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76ac106931baa3ba497b4273e673ac7ba4bbab7892c7a816a73443f1f2325e29 +size 118270 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/_tabdiff_gen.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..2195108fab9f21e680540f0c7fd1d972c3e6cb2b --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192001/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_c2/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192001/tabdiff-c2-1382-20260504_192715.csv") diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/_tabdiff_train.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f2aad5ba6c9c473e6f8d286dfccd86f0c82929ee --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192001/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/gen_20260504_192715.log b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/gen_20260504_192715.log new file mode 100644 index 0000000000000000000000000000000000000000..5d02791b6a5b2cd34dd383ed12a41d09684bf971 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/gen_20260504_192715.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5dba8ada80f0dc2ded12748062c1142497be7eed805a5ed3f1307bd9852f8d87 +size 4472 diff --git 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{ + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/public_gate/public_gate_report.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ff86dc95aad598a724fe76786cbfecb962b083b3 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192001/staged/tabdiff/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192001/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192001/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABDIFF_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/runtime_result.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..ba2fa678fdcc2f45417b328e324c05d74646aff1 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "run_id": "tabdiff-c2-20260504_192001", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192001/tabdiff-c2-1382-20260504_192715.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192001/models_tabdiff/trained.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T19:20:01", + "ended_at": "2026-05-04T19:27:15", + "duration_sec": 433.993 + }, + "generate": { + "started_at": "2026-05-04T19:27:15", + "ended_at": "2026-05-04T19:27:25", + "duration_sec": 9.515 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/staged/public/staged_features.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": 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b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/tabular_bundle/pipeline_c2/info.json @@ -0,0 +1,88 @@ +{ + "name": "pipeline_c2", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "test_num": 1382, + "val_num": 1382, + "train_num": 1382, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "categorical" + }, + "1": { + "sdtype": "categorical" + }, + "2": { + "sdtype": "categorical" + }, + "3": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "categorical" + } + } + }, + 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new file mode 100644 index 0000000000000000000000000000000000000000..52d07898ca67294f361b06779da01d7421d61724 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192001/train_20260504_192001.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8defbbfe8abb45222d212752be027472d58859ad7bdacc43a57e6651a10d7876 +size 286838 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/_tabdiff_gen.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..7005d3e34d475d802e281c1eec77a995c24eea94 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192738/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_c2/adapter_learnable/model_800.pt", + "--num_samples_to_generate", str(int(1382)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192738/tabdiff-c2-1382-20260504_193847.csv") diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/_tabdiff_train.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b3f375c1da225ef1e64f6405ee4eb4d7dd8238a8 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192738/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "800" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/gen_20260504_193847.log b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/gen_20260504_193847.log new file mode 100644 index 0000000000000000000000000000000000000000..6bd514f078980397ed1a5dea9badf103475a2bd2 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/gen_20260504_193847.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1c2f4c2819d3b3e46b5dca01f3bd95d8a2423b49b38cb99ef5e75eb6d355863 +size 4471 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/input_snapshot.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..6d83542090e9720c485d72abe1bb75a800175917 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + 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"PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6b7ee5a0945ab2967fb4a9fffe4bd39265f093ec --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192738/staged/tabdiff/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192738/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192738/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABDIFF_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/runtime_result.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..386b462282e8a19eaf531ad3a44d14bbdb3553e1 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "run_id": "tabdiff-c2-20260504_192738", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192738/tabdiff-c2-1382-20260504_193847.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_192738/models_tabdiff/trained.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T19:27:38", + "ended_at": "2026-05-04T19:38:47", + "duration_sec": 669.526 + }, + "generate": { + "started_at": "2026-05-04T19:38:47", + "ended_at": "2026-05-04T19:38:57", + "duration_sec": 9.277 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/staged/public/staged_features.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": 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a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/staged/tabdiff/model_input_manifest.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/staged/tabdiff/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..665b2ef2853d658b70caac2ec9f04843422d2fee --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/staged/tabdiff/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + 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b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/tabular_bundle/pipeline_c2/info.json @@ -0,0 +1,88 @@ +{ + "name": "pipeline_c2", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "test_num": 1382, + "val_num": 1382, + "train_num": 1382, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "categorical" + }, + "1": { + "sdtype": "categorical" + }, + "2": { + "sdtype": "categorical" + }, + "3": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "categorical" + } + } + }, + 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new file mode 100644 index 0000000000000000000000000000000000000000..9e7f4afaea7d155bdab04b4867ff5940d7a9dbab --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_192738/train_20260504_192738.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4488f2f8aad0faaae8c589e31ff733464ab6eab0b3c522278ff3e1032b8540d +size 456790 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/_tabdiff_gen.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..0c9628151accaae3e7c9978d7cae2da0431a4408 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_c2/adapter_learnable/model_800.pt", + "--num_samples_to_generate", str(int(1382)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/tabdiff-c2-1382-20260504_195126.csv") diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/_tabdiff_train.py b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..30a00cadda625d22d0aa054345e44318f939b4a2 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "1000" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/gen_20260504_195126.log b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/gen_20260504_195126.log new file mode 100644 index 0000000000000000000000000000000000000000..862390c55c122027b715333008ae0b197abcbc4d --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/gen_20260504_195126.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:08a2dbf554146f1e896a1d88d90cbe9dd40dc65825bc6847dab472c58489b962 +size 4474 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/input_snapshot.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..6d83542090e9720c485d72abe1bb75a800175917 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/models_tabdiff/trained.pt b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/models_tabdiff/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc319a55a9c7152a1137f968b6fa1c735125ad11 --- /dev/null +++ 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0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/public_gate_report.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0cfc75dbe9b02a3b448f5561d2fcc54a745dfc24 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/run_config.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..d2ff0d9976c4be670fa44aa41deacaea7ab8edda --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/run_config.json @@ -0,0 +1,40 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T19:39:09", + "dataset_id": "c2", + "model": "tabdiff", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabdiff", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 1000, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/models_tabdiff/trained.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/tabdiff-c2-1382-20260504_195126.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/staged/tabdiff/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABDIFF_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/runtime_result.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..9e62341614ac9453508e1b9aaa64937189283e33 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "run_id": "tabdiff-c2-20260504_193909", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/tabdiff-c2-1382-20260504_195126.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260504_193909/models_tabdiff/trained.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T19:39:09", + "ended_at": "2026-05-04T19:51:26", + "duration_sec": 737.237 + }, + "generate": { + "started_at": "2026-05-04T19:51:26", + "ended_at": "2026-05-04T19:51:35", + "duration_sec": 9.185 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/staged_features.json b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/test.csv b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/train.csv b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/staged/public/train.csv @@ -0,0 +1,3 @@ +version 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new file mode 100644 index 0000000000000000000000000000000000000000..0027b65d041a1c1cb7ab2acca95fb5815f71e3ce --- /dev/null +++ b/hyperparameter/c2/tabdiff/tabdiff-c2-20260504_193909/train_20260504_193909.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f009b816f291b79d55799f1f0eaea1b9edadfd28a65eb8e2b4fc79e44715be2 +size 572262 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/_tabpfgen_generate.py b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..de2c9f95c0e5382f3aada6551621d085c53b9cb7 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/train.csv") +target_col = "class" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(1382) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen-c2-1382-20260505_025616.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen-c2-1382-20260505_025616.csv") diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/gen_20260505_025616.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/gen_20260505_025616.log new file mode 100644 index 0000000000000000000000000000000000000000..506db7827d711baa97aed013f1ba6e59108cd6a6 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/gen_20260505_025616.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f5d458b4b59998c2c42f537e6b26bac862be64d1921b1a4fadee21c85c8f64b +size 5240 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/input_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0dfc898784526759fbfe4fcdd1aa60dc4d52829f --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/public_gate_report.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4422c19d4747acf36ff9b215b2176e24c3d003e8 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/run_config.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..2be29c773307fa0bb9a5dea09ed34b80e299d781 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T02:56:16", + "dataset_id": "c2", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen-c2-1382-20260505_025616.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "512", + "TABPFGEN_GEN_CHUNK_ROWS": "64" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/runtime_result.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..1a37119f2d533aeba59f4ee1a331db10aa3f3a6e --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "run_id": "tabpfgen-c2-20260505_025616", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen-c2-1382-20260505_025616.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616" + }, + "timings": { + "train": { + "started_at": "2026-05-05T02:56:16", + "ended_at": "2026-05-05T02:56:16", + "duration_sec": 0.005 + }, + "generate": { + "started_at": "2026-05-05T02:56:16", + "ended_at": "2026-05-05T02:57:09", + "duration_sec": 52.103 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/staged_features.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/test.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/train.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/val.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/adapter_report.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..5520c2d800e07b27c8134fd0c2fafd3319d4a1ab --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/adapter_transforms_applied.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/model_input_manifest.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4490a14a79ea9a8c93f28e93217c7278eea42bed --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen-c2-1382-20260505_025616.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen-c2-1382-20260505_025616.csv new file mode 100644 index 0000000000000000000000000000000000000000..12f0063b75045ce6640f0214aa9fdfa268e8b983 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen-c2-1382-20260505_025616.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:70e48c6eab10154c876f2a5b0c57ac997ae9a72ddab6f20b09b2e26b7653f358 +size 41593 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen_meta.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..78707752c9e53caf2bbd6ae505406727d0d130e1 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025616/staged/public/staged_features.json", + "target_col": "class", + "is_classification": true, + "task_type": "classification", + "n_rows": 1382, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/train_20260505_025616.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/train_20260505_025616.log new file mode 100644 index 0000000000000000000000000000000000000000..451773e1baef686744c3581b3a7847fb2e511c75 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025616/train_20260505_025616.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9090392c04c0e75d9a7c201bf6f51c126d1bb2a6edb304986c71d6dbaf8ac088 +size 595 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/_tabpfgen_generate.py b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d9f0b7d9d50bec01afbb54ac06aeaa79676eedbd --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/train.csv") +target_col = "class" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(1382) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/tabpfgen-c2-1382-20260505_025721.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/tabpfgen-c2-1382-20260505_025721.csv") diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/gen_20260505_025721.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/gen_20260505_025721.log new file mode 100644 index 0000000000000000000000000000000000000000..7eb61c8ad5c79fecf0fd4d83df6163f786948269 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/gen_20260505_025721.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73fb3b3c86762383f2e81791adc6ee51511d4cd11ba9211cbfbc399e9f88654b +size 5240 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/input_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0dfc898784526759fbfe4fcdd1aa60dc4d52829f --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/public_gate_report.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a529d86ed57322c9be930772f5905d0080a014bc --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": 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"2026-05-05T02:58:13", + "duration_sec": 52.309 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/staged_features.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + 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3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/tabpfgen-c2-1382-20260505_025721.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/tabpfgen-c2-1382-20260505_025721.csv new file mode 100644 index 0000000000000000000000000000000000000000..e804e7b433b8d5bab27ee490c2afcfb1b1ca20b5 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/tabpfgen-c2-1382-20260505_025721.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64e0904eb3f6d3ed59f3b018d298ed93eabfc8e11b3dfd67f35dbea8cfdfc799 +size 41865 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/tabpfgen_meta.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..c86e47a84d9bcfff7acc9bcd94bf49367bb936b1 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025721/staged/public/staged_features.json", + "target_col": "class", + "is_classification": true, + "task_type": "classification", + "n_rows": 1382, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/train_20260505_025721.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/train_20260505_025721.log new file mode 100644 index 0000000000000000000000000000000000000000..46b070dfb4fb22f69603c329808bb1cc25b03c2b --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025721/train_20260505_025721.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb3fc7aeb3c9879e8f1df3f581842107c40765cefe2f4d100b40562c8fbc968c +size 595 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/_tabpfgen_generate.py b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..cefa5e4bca570bd59c9df22af56af24e1962effa --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/train.csv") +target_col = "class" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(1382) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen-c2-1382-20260505_025826.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen-c2-1382-20260505_025826.csv") diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/gen_20260505_025826.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/gen_20260505_025826.log new file mode 100644 index 0000000000000000000000000000000000000000..f3fe9b766d7c8092638ad786be486234e7e6bc7c --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/gen_20260505_025826.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd37470a04e5b77192013393ca5743e0dfaacf234072d5c2bdc4e8d5d8e400ec +size 3226 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/input_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0dfc898784526759fbfe4fcdd1aa60dc4d52829f --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/public_gate_report.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..d29613b74f5e1b3654f4f7db0e787f8d36745e07 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/run_config.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..ad76a1dc9e9165dc2fefa135f4e805bd908e736d --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T02:58:26", + "dataset_id": "c2", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen-c2-1382-20260505_025826.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "2048", + "TABPFGEN_GEN_CHUNK_ROWS": "128" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/runtime_result.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..b70a77adcfb66e185a820492205d89d7068a1c5d --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "run_id": "tabpfgen-c2-20260505_025826", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen-c2-1382-20260505_025826.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826" + }, + "timings": { + "train": { + "started_at": "2026-05-05T02:58:26", + "ended_at": "2026-05-05T02:58:26", + "duration_sec": 0.006 + }, + "generate": { + "started_at": "2026-05-05T02:58:26", + "ended_at": "2026-05-05T02:59:03", + "duration_sec": 36.813 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/staged_features.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/test.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/train.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/val.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/val.csv @@ -0,0 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a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/tabpfgen/adapter_transforms_applied.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/tabpfgen/model_input_manifest.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bd271520fae1c040ee47a22f8b76b8ec5dc00142 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen-c2-1382-20260505_025826.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen-c2-1382-20260505_025826.csv new file mode 100644 index 0000000000000000000000000000000000000000..198484020f50a7840c1606232400f517c25d8524 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen-c2-1382-20260505_025826.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b49af73dcfe0980b658726ad1d61f45776ec5edec34acf5c5a4babe5f81ab04 +size 41922 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen_meta.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..379909575d33e46b14d38ee02a2e83dc9d20638e --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025826/staged/public/staged_features.json", + "target_col": "class", + "is_classification": true, + "task_type": "classification", + "n_rows": 1382, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/train_20260505_025826.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/train_20260505_025826.log new file mode 100644 index 0000000000000000000000000000000000000000..d69b2eb3373cf63bead7d239e8b70d1fe4140ede --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025826/train_20260505_025826.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a028185dfc74f2ea36397ef2db6c996b532a53ac12d317c0881382d013164fd3 +size 595 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/_tabpfgen_generate.py b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8748ba0e6815090d562de114475a5089e7fb0c03 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025915/staged/public/train.csv") +target_col = "class" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(1382) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025915/tabpfgen-c2-1382-20260505_025915.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_025915/tabpfgen-c2-1382-20260505_025915.csv") diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/gen_20260505_025915.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/gen_20260505_025915.log new file mode 100644 index 0000000000000000000000000000000000000000..55f78faaa6b375bc97ecceb1697587199adca01b --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/gen_20260505_025915.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f10d6172925223664b5cfe31493d3399422ab369b1702feb3f77793633057509 +size 3088 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/input_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0dfc898784526759fbfe4fcdd1aa60dc4d52829f --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_025915/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": 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--git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/_tabpfgen_generate.py b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b9bedcbf68105de818d2bb7e816df1cae41d1b65 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/train.csv") +target_col = "class" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(1382) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen-c2-1382-20260505_030003.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen-c2-1382-20260505_030003.csv") diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/gen_20260505_030003.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/gen_20260505_030003.log new file mode 100644 index 0000000000000000000000000000000000000000..a598f7f7e156669780d590f2cd39a97a1786c788 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/gen_20260505_030003.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c89b6bed7c19b7b0a1370fc5740dccc0a448d2f0da2b81e17eccf9f13a9feb4c +size 2300 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/input_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0dfc898784526759fbfe4fcdd1aa60dc4d52829f --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/public_gate_report.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/staged_input_manifest.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2f2475bedb81e03b89e105b84a2979354486384a --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/run_config.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..ab78c4c3eef088098d795641f8a1780bdadb9ba2 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T03:00:03", + "dataset_id": "c2", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen-c2-1382-20260505_030003.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "4096", + "TABPFGEN_GEN_CHUNK_ROWS": "256" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/runtime_result.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..35290a137c19aa4db20d650352da94f8fdf94aad --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "run_id": "tabpfgen-c2-20260505_030003", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen-c2-1382-20260505_030003.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003" + }, + "timings": { + "train": { + "started_at": "2026-05-05T03:00:03", + "ended_at": "2026-05-05T03:00:03", + "duration_sec": 0.005 + }, + "generate": { + "started_at": "2026-05-05T03:00:03", + "ended_at": "2026-05-05T03:00:31", + "duration_sec": 27.774 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/staged_features.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/test.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/train.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/val.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/adapter_report.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..6e565ce36cd2105039e2ba18b89946ad462b2ce9 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/adapter_transforms_applied.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/model_input_manifest.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..611a735cef8cc8c901181abb3d4e9e1bf4543114 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen-c2-1382-20260505_030003.csv b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen-c2-1382-20260505_030003.csv new file mode 100644 index 0000000000000000000000000000000000000000..2e31286c2c377a4f028464a3ebf677fe308c8ace --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen-c2-1382-20260505_030003.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b16561b8e4059dd9013842fbadac01d06c3221f936ea6b27ce903f2892b68c4c +size 41839 diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen_meta.json b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..5c594b1d0c42c9d68e769d261142c205e0400ee2 --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260505_030003/staged/public/staged_features.json", + "target_col": "class", + "is_classification": true, + "task_type": "classification", + "n_rows": 1382, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/train_20260505_030003.log b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/train_20260505_030003.log new file mode 100644 index 0000000000000000000000000000000000000000..155381857f2f1503a3d7a2d97566067927227aae --- /dev/null +++ b/hyperparameter/c2/tabpfgen/tabpfgen-c2-20260505_030003/train_20260505_030003.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5335decd332c06794b9bda9beb03da3bbe452871f976e811792d967664be0fc +size 595 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/_tabsyn_sample.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..c2c4a30796d960af7a74dd56ac99535d415f96ff --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035" +dataname = "tabsyn_c2" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035/tabsyn-c2-1382-20260505_024152.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 1382 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/_tabsyn_train.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..80aa063e8f1181e8dde6233f6fd671fff1d0c413 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035" +dataname = "tabsyn_c2" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/data/tabsyn_c2/X_cat_test.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/data/tabsyn_c2/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/data/tabsyn_c2/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e9f5e299905cfd50c63688fb7b818aeec0d802b14338f705ec09d39826ac9c2 +size 55408 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/data/tabsyn_c2/X_cat_train.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/data/tabsyn_c2/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/data/tabsyn_c2/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e9f5e299905cfd50c63688fb7b818aeec0d802b14338f705ec09d39826ac9c2 +size 55408 diff --git 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"is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/test.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/train.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/val.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/adapter_report.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..b98b3f7663e766773b5a0af0594797a8a5e79a75 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/adapter_transforms_applied.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/model_input_manifest.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1239ba6aacfbad6bf1eef27716c330ed11033218 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabsyn", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_015035/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/synthetic/tabsyn_c2/real.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/synthetic/tabsyn_c2/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/synthetic/tabsyn_c2/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/synthetic/tabsyn_c2/test.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/synthetic/tabsyn_c2/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/synthetic/tabsyn_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/tabsyn-c2-1382-20260505_024152.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/tabsyn-c2-1382-20260505_024152.csv new file mode 100644 index 0000000000000000000000000000000000000000..a0af24c3589511abaf2e6af061738017abafc099 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/tabsyn-c2-1382-20260505_024152.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:047d9d2bc16dd492a4ab72e0d8157a5be44b06a5c5a98934338c9d31e9bb12d0 +size 22167 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/train_20260505_015035.log b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/train_20260505_015035.log new file mode 100644 index 0000000000000000000000000000000000000000..46a1214874878fc8b3f16f145f953104e96f39ee --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_015035/train_20260505_015035.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6bb460b04e0be175103f0aa2c52a9e8f0183172a2757f633c725d2813b40046f +size 3409562 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/_tabsyn_sample.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..8ff2791e43bd2a9162da61187ca72b4f40f28bc6 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211" +dataname = "tabsyn_c2" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211/tabsyn-c2-1382-20260505_033334.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 1382 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/_tabsyn_train.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..096bd6126a9bceecc70e00067da147e4c6a7a502 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211" +dataname = "tabsyn_c2" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_cat_test.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e9f5e299905cfd50c63688fb7b818aeec0d802b14338f705ec09d39826ac9c2 +size 55408 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_cat_train.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e9f5e299905cfd50c63688fb7b818aeec0d802b14338f705ec09d39826ac9c2 +size 55408 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_num_test.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..9a71edeab7a22756c9090743b5d0fde204acfb75 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f937731a3e7785676e9f861e44eddab69c323327e03bdd1f6e8755c5321538fb +size 5656 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_num_train.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/data/tabsyn_c2/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9a71edeab7a22756c9090743b5d0fde204acfb75 --- /dev/null +++ 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0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/public/val.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/adapter_report.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..8a2469425cc5aa313ef2cb797c51b5e457aa911b --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/adapter_transforms_applied.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/model_input_manifest.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ca1883fa5f2f2d183257bf468dbc7a21cb962635 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabsyn", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_024211/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/synthetic/tabsyn_c2/real.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/synthetic/tabsyn_c2/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/synthetic/tabsyn_c2/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/synthetic/tabsyn_c2/test.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/synthetic/tabsyn_c2/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/synthetic/tabsyn_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/tabsyn-c2-1382-20260505_033334.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/tabsyn-c2-1382-20260505_033334.csv new file mode 100644 index 0000000000000000000000000000000000000000..2564a875cd3a0f746128e7a67e7727cbf79a7d72 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/tabsyn-c2-1382-20260505_033334.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4afcc6ad475fc0c9f4424af406729ac947702abcc36958da6e9c2988daf1a0af +size 22367 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/train_20260505_024211.log b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/train_20260505_024211.log new file mode 100644 index 0000000000000000000000000000000000000000..a5f1f9e0d88ced645228a74ccdcebda4c9cbb755 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_024211/train_20260505_024211.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7a31789748b30674925ce3734c17c8baa525dbc9858a93c3086e5b2a6f3dc4c2 +size 3277227 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/_tabsyn_sample.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..7bbc22ef8f2fbd35bb965701b8bd41cdea1b92b6 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121" +dataname = "tabsyn_c2" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121/tabsyn-c2-1382-20260505_045652.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 1382 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/_tabsyn_train.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e7185f5bbc879a5ef475ec4513b4007b92e5c91b --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121" +dataname = "tabsyn_c2" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_cat_test.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e9f5e299905cfd50c63688fb7b818aeec0d802b14338f705ec09d39826ac9c2 +size 55408 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_cat_train.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e9f5e299905cfd50c63688fb7b818aeec0d802b14338f705ec09d39826ac9c2 +size 55408 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_num_test.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..9a71edeab7a22756c9090743b5d0fde204acfb75 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f937731a3e7785676e9f861e44eddab69c323327e03bdd1f6e8755c5321538fb +size 5656 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_num_train.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9a71edeab7a22756c9090743b5d0fde204acfb75 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f937731a3e7785676e9f861e44eddab69c323327e03bdd1f6e8755c5321538fb +size 5656 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/info.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3d85c74a88f8b29c5054c0455ce2f895b74cc726 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/data/tabsyn_c2/info.json @@ -0,0 +1,89 @@ +{ + "name": "tabsyn_c2", + "task_type": "multiclass", + "n_num_features": 1, + "n_cat_features": 5, + "train_size": 1382, + "num_col_idx": [ + 0 + ], + "cat_col_idx": [ + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "train_num": 1382, + "test_num": 1382, 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+++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/staged/tabsyn/model_input_manifest.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9a464259825f6d2dd8e0b77fab5180524d7ddabb --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabsyn", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_040121/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/synthetic/tabsyn_c2/real.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/synthetic/tabsyn_c2/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/synthetic/tabsyn_c2/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/synthetic/tabsyn_c2/test.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/synthetic/tabsyn_c2/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/synthetic/tabsyn_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/tabsyn-c2-1382-20260505_045652.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/tabsyn-c2-1382-20260505_045652.csv new file mode 100644 index 0000000000000000000000000000000000000000..b8bbfd0083cbd7a3f4ab8bd2bd9404ad0aa4da9c --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/tabsyn-c2-1382-20260505_045652.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:916d159c212638bde2d9d6eea6f128f238a97e640d35315a05787333b4786ce5 +size 22233 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/train_20260505_040121.log b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/train_20260505_040121.log new file mode 100644 index 0000000000000000000000000000000000000000..c00629ebfeb3ec6135156bc2b840926e6bb5e45a --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_040121/train_20260505_040121.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:932c8e0cd49f8bc34ff7216444fa250e13ce09c77dbb271b5a4cd20a31b6de6c +size 3472789 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/_tabsyn_sample.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..02017d3663f5009274466fac6ff54b2e1103d93c --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714" +dataname = "tabsyn_c2" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714/tabsyn-c2-1382-20260505_054901.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 1382 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/_tabsyn_train.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2ba6e891f6b5357a6a88b2f4f7da17a3fd863daa --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714" +dataname = "tabsyn_c2" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/X_cat_test.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e9f5e299905cfd50c63688fb7b818aeec0d802b14338f705ec09d39826ac9c2 +size 55408 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/X_cat_train.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f937731a3e7785676e9f861e44eddab69c323327e03bdd1f6e8755c5321538fb +size 5656 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/info.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3d85c74a88f8b29c5054c0455ce2f895b74cc726 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/info.json @@ -0,0 +1,89 @@ +{ + "name": "tabsyn_c2", + "task_type": "multiclass", + "n_num_features": 1, + "n_cat_features": 5, + "train_size": 1382, + "num_col_idx": [ + 0 + ], + "cat_col_idx": [ + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "train_num": 1382, + "test_num": 1382, + "header": 0, + "file_type": "csv", + "data_path": "data/tabsyn_c2/train.csv", + "test_path": null, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "inverse_idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "idx_name_mapping": { + "0": "buying", + "1": "maint", + "2": "doors", + "3": "persons", + "4": "lug_boot", + "5": "safety", + "6": "class" + }, + "n_classes": 4, + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "categorical" + }, + "2": { + "sdtype": "categorical" + }, + "3": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "categorical" + } + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/data/tabsyn_c2/test.csv 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"example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_045714/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/synthetic/tabsyn_c2/real.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/synthetic/tabsyn_c2/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/synthetic/tabsyn_c2/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/synthetic/tabsyn_c2/test.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/synthetic/tabsyn_c2/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/synthetic/tabsyn_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/tabsyn-c2-1382-20260505_054901.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/tabsyn-c2-1382-20260505_054901.csv new file mode 100644 index 0000000000000000000000000000000000000000..75daaafb8a02d1750fb23f2f7a31b1e41cbb1b18 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/tabsyn-c2-1382-20260505_054901.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de5f89d466e850988e9d8909c3fd1827c59bb049fc324a71783841369351ff6d +size 22167 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/train_20260505_045714.log b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/train_20260505_045714.log new file mode 100644 index 0000000000000000000000000000000000000000..f54a1c1debf5560850dd5af246fafc66541c0e6c --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_045714/train_20260505_045714.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd591df45cf1a8cf7290d8653075f59693d07c136143d7180835f753c1e5f25e +size 3483759 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/_tabsyn_sample.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..5b14163f868fedcf0fb550a5592022405335a2a4 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921" +dataname = "tabsyn_c2" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921/tabsyn-c2-1382-20260505_063927.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 1382 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/_tabsyn_train.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5c42b4144994214fe61c4a80ba488e8f59fea31a --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921" +dataname = "tabsyn_c2" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git 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b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/data/tabsyn_c2/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f937731a3e7785676e9f861e44eddab69c323327e03bdd1f6e8755c5321538fb +size 5656 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/data/tabsyn_c2/info.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/data/tabsyn_c2/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3d85c74a88f8b29c5054c0455ce2f895b74cc726 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/data/tabsyn_c2/info.json @@ -0,0 +1,89 @@ +{ + "name": "tabsyn_c2", + "task_type": "multiclass", + "n_num_features": 1, + "n_cat_features": 5, + "train_size": 1382, + "num_col_idx": [ + 0 + ], + "cat_col_idx": [ + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "train_num": 1382, + "test_num": 1382, 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"unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_054921/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/synthetic/tabsyn_c2/real.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/synthetic/tabsyn_c2/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/synthetic/tabsyn_c2/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/synthetic/tabsyn_c2/test.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/synthetic/tabsyn_c2/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/synthetic/tabsyn_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/tabsyn-c2-1382-20260505_063927.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/tabsyn-c2-1382-20260505_063927.csv new file mode 100644 index 0000000000000000000000000000000000000000..15d754a25d4cce795984891d97252c96cdde01ee --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/tabsyn-c2-1382-20260505_063927.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e73d6b17f3a5ed84bb3455f7ede61e2bb9a9f5ff5a02ce1f29f8a98282d656e5 +size 22318 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/train_20260505_054921.log b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/train_20260505_054921.log new file mode 100644 index 0000000000000000000000000000000000000000..9dd4b9c10ad1411de274b6c36a7cb0bf7ff2434f --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_054921/train_20260505_054921.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d00df65524a806894d084023f8163d57075b99b881d7b36c420192b8affdc3d0 +size 3323505 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/_tabsyn_sample.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..d8667a220d4dd7428cc4d949c98200d9fc0539da --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_063947" +dataname = "tabsyn_c2" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_063947/tabsyn-c2-1382-20260505_073149.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 1382 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/_tabsyn_train.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..385b5d49ecf5cceea6058bd82a1b6fd605ffb7c7 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_063947" +dataname = "tabsyn_c2" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git 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b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/data/tabsyn_c2/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f937731a3e7785676e9f861e44eddab69c323327e03bdd1f6e8755c5321538fb +size 5656 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/data/tabsyn_c2/info.json b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/data/tabsyn_c2/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3d85c74a88f8b29c5054c0455ce2f895b74cc726 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/data/tabsyn_c2/info.json @@ -0,0 +1,89 @@ +{ + "name": "tabsyn_c2", + "task_type": "multiclass", + "n_num_features": 1, + "n_cat_features": 5, + "train_size": 1382, + "num_col_idx": [ + 0 + ], + "cat_col_idx": [ + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "train_num": 1382, + "test_num": 1382, 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--- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/synthetic/tabsyn_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/tabsyn-c2-1382-20260505_073149.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/tabsyn-c2-1382-20260505_073149.csv new file mode 100644 index 0000000000000000000000000000000000000000..14ad410f314fa3f40c4af03e485f7e91ac44ff31 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/tabsyn-c2-1382-20260505_073149.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:589891572566e3b4c42868c6db370ae22d67b2394e55abc3096155fff55e0dbe +size 22179 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/train_20260505_063947.log b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/train_20260505_063947.log new file mode 100644 index 0000000000000000000000000000000000000000..89b93fabcc5aba9f14677b4a859ad1708d1d438e --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_063947/train_20260505_063947.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43b007fce1b6e49dff5fd4e9a82e86233cf4122f45c02e471cca1b14204a7b66 +size 3359651 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/_tabsyn_sample.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..c20a3c08a531ea3ee97bdd701b72298a4d71bad8 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_073207" +dataname = "tabsyn_c2" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_073207/tabsyn-c2-1382-20260505_082340.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 1382 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/_tabsyn_train.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..579cd77cd6788f87748348f2f889e19ff9e3ee5e --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_073207" +dataname = "tabsyn_c2" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git 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--- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/synthetic/tabsyn_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/tabsyn-c2-1382-20260505_082340.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/tabsyn-c2-1382-20260505_082340.csv new file mode 100644 index 0000000000000000000000000000000000000000..0bf85e53194075de365d61fffc59d553741b1259 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/tabsyn-c2-1382-20260505_082340.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26ecff342275929b3130a925a8ebf55cde1233a0866e4cd3f72f8a38d2c47efa +size 22279 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/train_20260505_073207.log b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/train_20260505_073207.log new file mode 100644 index 0000000000000000000000000000000000000000..5e29a1849d82c5d60acdf14edb400593add44009 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_073207/train_20260505_073207.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5c36160287c5cf6466f102ba4fbb9208802e2899eba5d61615ff4f4ddf4594a +size 3307375 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/_tabsyn_sample.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..3715e94075aafbd45f9b16eb6d8bb719a51f8a2e --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_173210" +dataname = "tabsyn_c2_tabsyn_c2_20260505_173210" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_173210/tabsyn-c2-64-20260505_173227.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 64 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/_tabsyn_train.py b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9ce9942d4f0df5281a787269ea632dcfa1686fd5 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260505_173210" +dataname = "tabsyn_c2_tabsyn_c2_20260505_173210" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/data/tabsyn_c2_tabsyn_c2_20260505_173210/X_cat_test.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/data/tabsyn_c2_tabsyn_c2_20260505_173210/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/data/tabsyn_c2_tabsyn_c2_20260505_173210/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e9f5e299905cfd50c63688fb7b818aeec0d802b14338f705ec09d39826ac9c2 +size 55408 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/data/tabsyn_c2_tabsyn_c2_20260505_173210/X_cat_train.npy b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/data/tabsyn_c2_tabsyn_c2_20260505_173210/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ 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"tabsyn_c2_tabsyn_c2_20260505_173210", + "task_type": "multiclass", + "n_num_features": 1, + "n_cat_features": 5, + "train_size": 1382, + "num_col_idx": [ + 0 + ], + "cat_col_idx": [ + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "train_num": 1382, + "test_num": 1382, + "header": 0, + "file_type": "csv", + "data_path": "data/tabsyn_c2_tabsyn_c2_20260505_173210/train.csv", + "test_path": null, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "inverse_idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "idx_name_mapping": { + "0": "buying", + "1": "maint", + "2": "doors", + "3": "persons", + "4": "lug_boot", + "5": "safety", + "6": "class" + }, + "n_classes": 4, + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + 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b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + 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a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/synthetic/tabsyn_c2_tabsyn_c2_20260505_173210/real.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/synthetic/tabsyn_c2_tabsyn_c2_20260505_173210/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/synthetic/tabsyn_c2_tabsyn_c2_20260505_173210/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/synthetic/tabsyn_c2_tabsyn_c2_20260505_173210/test.csv b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/synthetic/tabsyn_c2_tabsyn_c2_20260505_173210/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ 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0000000000000000000000000000000000000000..ea9e778f5061980e1f18411967ad271ef66d27e1 --- /dev/null +++ b/hyperparameter/c2/tabsyn/tabsyn-c2-20260505_173210/train_20260505_173211.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76b0fd64507e36c2cc1e247c9f6c3176972f877c57015d29425042c14f28e09f +size 4164 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ba56b67e501be1a02f9df11b17b5be278a4fbaf4 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8ca6db5febbe87084a63b4625ce50a43245d7d --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/gen_20260504_173024.log b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/gen_20260504_173024.log new file mode 100644 index 0000000000000000000000000000000000000000..53d08cd059dddaa7aff8bae5458dce3fa0b5ab7e --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/gen_20260504_173024.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c250d16c78bb78776dfd39677e43fa301524a14766e56dfb776757716b41ad3c +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/models_300epochs/train_20260504_173008.log b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/models_300epochs/train_20260504_173008.log new file mode 100644 index 0000000000000000000000000000000000000000..4f1bc8a0c8aa1472b5b5a147d542eb71dee6b421 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/models_300epochs/train_20260504_173008.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1c352ed86bd6646a32996cf14ef89e36f1c70492c504f33136461af759d97f8 +size 531 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..601624c018769994626fca7acae79f71da20202b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version 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+ }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..34b8b885d16936dabc4bfa6ae10d9902d0dcbe1e --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/run_config.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..7335c4ba5feedfd103bfd4c81cc612041c2e4e81 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/run_config.json @@ -0,0 +1,40 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:30:08", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/runtime_result.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..22be744ff183e559b6ad1a680776340fac9c465e --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_173008", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:30:08", + "ended_at": "2026-05-04T17:30:24", + "duration_sec": 15.956 + }, + "generate": { + "started_at": "2026-05-04T17:30:24", + "ended_at": "2026-05-04T17:30:30", + "duration_sec": 5.259 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..4242764ed766368329eb87e61673e22e2b5b6af8 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_transforms_applied.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f04fd5c7b319044a938f125aa99765abe6589a9c --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv new file mode 100644 index 0000000000000000000000000000000000000000..0facc9d2d09dd192dcf6aaf22fef2e2ec709388b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5c13329eb5fa57144da4e37e2c72a488103b21bb242e3711339b259178094e71 +size 41572 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174013/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..3286ed3675bc2e6dab9822c66720b2afe05caa4d --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174013/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..907e254de6937dd18f07ac7af235f221f36bb32a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174013/gen_20260504_174029.log b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/gen_20260504_174029.log new file mode 100644 index 0000000000000000000000000000000000000000..a1b1e990c2280e5c69b5bb23bc9d3b47e6938193 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/gen_20260504_174029.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56d72e0efac984702b081bb48d7cb946ea3691388616e818ff414e6e231d5453 +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174013/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174013/models_300epochs/train_20260504_174013.log b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/models_300epochs/train_20260504_174013.log new file mode 100644 index 0000000000000000000000000000000000000000..bad1b646c923e3b9a8e91ad353281c3cc4607689 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/models_300epochs/train_20260504_174013.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b25cd4019839ccaca05dcab067fe07c9a8293c777a3e34ded19ab97c8e49e17 +size 620 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..08961776a4f44dbaf6745845c6423443d3fbed2b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version 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at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..54301febcfdbada05a2cc11f3d8302fc4c95abfa --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..975f46af9bc9246f533df0df46150305c2b5913d --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/gen_20260504_174104.log b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/gen_20260504_174104.log new file mode 100644 index 0000000000000000000000000000000000000000..03b14e21e7e199bc2f26331ac5842f0dc60facd4 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/gen_20260504_174104.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:084af1bcbd34d4f755a09dd9ffbc4cce57069fbb7bbb8c8eff2ae22b9d3e7a7a +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/models_300epochs/train_20260504_174047.log b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/models_300epochs/train_20260504_174047.log new file mode 100644 index 0000000000000000000000000000000000000000..dcae6ce961bfa13c10a2b5cf75c07f435593295b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/models_300epochs/train_20260504_174047.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:30a727c05f092232ce9b947d1ef769919085dd8c762c2eae3492cb27950a2802 +size 620 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..037655d3c719ce686f316a15170097bcd4082671 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e86ebfc162121c60718157c90bdb1625acba8d2b170c086a6eaa4a3f9adc47eb +size 344812 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + 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b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": 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+ { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/run_config.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..eb699b6178b4fec97d70cffefc82f29ebab21fa4 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:40:47", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "128,128", + "TVAE_DECOMPRESS_DIMS": "128,128", + "TVAE_EMBEDDING_DIM": "64", + "TVAE_EPOCHS": "200" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/runtime_result.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..a409396bf2280e0bf0083aa37a0cbacc3c2a0c17 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_174047", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": 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false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv new file mode 100644 index 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file mode 100644 index 0000000000000000000000000000000000000000..06f4c95c62f7be8ab33f2428fbbeb6007bd51879 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_transforms_applied.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ee377a50c2a2c26beaa41219a40cdf526ae72890 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv new file mode 100644 index 0000000000000000000000000000000000000000..22155eb14eab4a0343f73faaeff5b279cb5b8e30 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f3d50a2af8fc7f73f7995213dac76d9a43b9b386a2008e9b0c6dc02b8446f65 +size 41450 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174047/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174047/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174121/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ec162da85d8047a163fdaf4dcb2a5ad436a24fed --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174121/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..68cb971e55e8481a522d1968d1b464386b46ef56 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174121/gen_20260504_174137.log b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/gen_20260504_174137.log new file mode 100644 index 0000000000000000000000000000000000000000..88ffae8c24be5d2edfc2409b11cb081adb30f462 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/gen_20260504_174137.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e53e01455da4782d481bbd50839033f0111faaf92c485276e3cbd8c32d56679 +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174121/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv new file mode 100644 index 0000000000000000000000000000000000000000..0737ba5dfe37b5d7ec7bc0bc5c69c9ede6a47216 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b2dc7b4aac308837c0d16c501f82943b483cde999544304161ff98921ed6e4c +size 41505 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174121/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174121/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d80d25210e0289b817b08633cb84481dad9fa72c --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..26e132045e4721046a67aaab210cdd192059dbd4 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/gen_20260504_174210.log b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/gen_20260504_174210.log new file mode 100644 index 0000000000000000000000000000000000000000..63123e59ac40ced95634de8d57657fd11d935320 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/gen_20260504_174210.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9295c44c01b1fd97e9c805d7a481d72fcd0e87d969e89140bc0d39af40dfc04a +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/models_300epochs/train_20260504_174154.log b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/models_300epochs/train_20260504_174154.log new file mode 100644 index 0000000000000000000000000000000000000000..18eba14510752169d46511d24b9fa056d4d48a17 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/models_300epochs/train_20260504_174154.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:545125874fecb8ddd76af96a5ee5e06601127a0dd2e37ce07f49191222e99dc3 +size 620 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..8a29d8a55e3370ac0603b39a5784954180c743de --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c313f03bd4c66656f8c97200f3e68fa34fe32d3c2b0f3e6faccdd71d3f96f276 +size 344876 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + 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b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3b36c44b28c91fc89b7acfc0f2b620ed715caaf6 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/val.csv", + 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null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/run_config.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..d7c6834b0a8e47bb7e6f56b9fd086655f8441bce --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:41:54", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/runtime_result.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..731b3fb75f31150c5c810369fca9d9fb0a47b65e --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_174154", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + 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--- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/test.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/val.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..189b83c6cb1392d0d91b7025744650bbf87dfb55 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_transforms_applied.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3b4bcd1074c6e0d6cc900926febe83d6ece42b52 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv new file mode 100644 index 0000000000000000000000000000000000000000..c83688d2010e78aeb0ecc98529fdd18e33c714d5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94ee321408fa4dd67c768ba7f1522bec0d6ad27f49ecc50eb179d2ae844dd3f5 +size 41485 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174154/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174154/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174228/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..bc004c641ef3108ef8d5f082c796ac82f8020042 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174228/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bb08e002da231ddadd43ffcfd0f08856e725b5fb --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174228/gen_20260504_174245.log b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/gen_20260504_174245.log new file mode 100644 index 0000000000000000000000000000000000000000..8c9691faa417502fa8ab6fd539913322f6ab883f --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/gen_20260504_174245.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52660e570f2b1798f41853f45ac28d1c585f75b473dc0bafb63cea59cf9cd40d +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174228/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/input_snapshot.json @@ -0,0 +1,36 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv new file mode 100644 index 0000000000000000000000000000000000000000..1eef2401674d55c788c8457160db07fb3ca8f4e4 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed8d81c7986042bdc6d46d880cf4b8ecbb4ab544eed3754733fc1ee8424db47d +size 41641 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_174228/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_174228/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8104879488e8e7e39b0f1ce35e55b7498a71c718 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..cb7ef14cbfef58c7485e0f2c428a4de8aa54f4f7 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/gen_20260504_175041.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/gen_20260504_175041.log new file mode 100644 index 0000000000000000000000000000000000000000..fc18445fff6eeef90a8c0c788069fa1fc492dd49 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/gen_20260504_175041.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a01f4b4e5969a83ef737504dd36e3b52b491552504978f2029b415c1d0899625 +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/models_300epochs/train_20260504_175027.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/models_300epochs/train_20260504_175027.log new file mode 100644 index 0000000000000000000000000000000000000000..75d081b6b67b71d98c32792a120f034523c9ed61 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/models_300epochs/train_20260504_175027.log @@ -0,0 +1,3 @@ +version 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b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/public_gate/public_gate_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/public_gate/staged_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2d0396d7a163d0e0292af99bc6584ce15b374c93 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/run_config.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..3a2d617b169ad852ac43f72a1c4b7cfccfa33466 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:50:27", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:50:27", + "ended_at": "2026-05-04T17:50:41", + "duration_sec": 13.546 + }, + "generate": { + "started_at": "2026-05-04T17:50:41", + "ended_at": "2026-05-04T17:50:46", + "duration_sec": 5.172 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/staged_features.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/test.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/val.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/adapter_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..fd315eb1068f5c43dc5617cab4ab1fee932da6f2 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/adapter_transforms_applied.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/model_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..afcd3f42f324ce459307b455f3e2a3bb649e77e5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv new file mode 100644 index 0000000000000000000000000000000000000000..52de855ad6d8d3bf281e75cf4eed61b1ebb11e07 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c35716ec2b3678e33bbe0beeafcc0fdc08b695166f0faa60a359cce73fe16eed +size 42288 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175027/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175027/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9bdfcbea9bd89c4db99013044747122d14480916 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..16748e54eb9a1ae5a6f60d11a4f52397f67560f6 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/gen_20260504_175116.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/gen_20260504_175116.log new file mode 100644 index 0000000000000000000000000000000000000000..d49ada4499e260e32bafa422e2d1facee36f56f7 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/gen_20260504_175116.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7273add7930ed5939cafe3d43ea1602d7885408ec8627e3f03bf7ca8aae8d86a +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/models_300epochs/train_20260504_175100.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/models_300epochs/train_20260504_175100.log new file mode 100644 index 0000000000000000000000000000000000000000..f4b363f0e40c680a056ef1415fbb43824b139ec1 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/models_300epochs/train_20260504_175100.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13d7f892be66207b52dd8124e5bbe5442b4e8fcd0668d9a502ea70381c6bc846 +size 619 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c1d147d3d16f2d818036c6c7e229480c733a81f1 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05b303a596195c046df86ed9e9dd4aeac7f0072cc58d433b73a2cbafe0c93e12 +size 319852 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a1dd8f289c4edf67a2cd2fb29e1d242f70efeb9a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/run_config.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..9d5bf1b03dd5312dc8f0b6b480c2d8333e42ff85 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:51:00", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "128,128", + "TVAE_DECOMPRESS_DIMS": "128,128", + "TVAE_EMBEDDING_DIM": "64", + "TVAE_EPOCHS": "200" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/runtime_result.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..498a194563371d3471c9dc07442f9249c2fb375e --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_175059", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:51:00", + "ended_at": "2026-05-04T17:51:16", + "duration_sec": 16.884 + }, + "generate": { + "started_at": "2026-05-04T17:51:16", + "ended_at": "2026-05-04T17:51:21", + "duration_sec": 5.026 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..f39e1ab317ffaaa0c489f1d152d3541f687bec20 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_transforms_applied.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a3d86b3bcd4548705a5cc753afef3a6d4838e19e --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv new file mode 100644 index 0000000000000000000000000000000000000000..ed1aaacefa064433627b31272a6a0570e43c431a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31e5e3a91a0418044bc61b09e258d72356c52e332b3e614c0699f5f2962fe56c +size 41725 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c5f94d959d7eeaa29f9d882a3763d81f02da8b2e --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..091e65996ed24436d0d0cec26d6e97b2d8a731cb --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/gen_20260504_175151.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/gen_20260504_175151.log new file mode 100644 index 0000000000000000000000000000000000000000..7eadd238040dc89416ea5fb86cf52da087bf5a71 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/gen_20260504_175151.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40511260bd9eb00dcc35cfc95b37da03e79a617b156ee312d5e0a42d69f03ec5 +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/models_300epochs/train_20260504_175134.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/models_300epochs/train_20260504_175134.log new file mode 100644 index 0000000000000000000000000000000000000000..75f36e6e8bc72d6ec568cd3e2d86064d800a9dfe --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/models_300epochs/train_20260504_175134.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7d33708ecbb6ef5a07bd3355c804b12ee37c50055d190d2d2034a51d3b6c95e +size 619 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..32e8acd1b1837e47a3c1a4545a9b1765d6996774 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:61cb2ff2172d59d7221cfda8f98a518f8db67166a7fc0011342913cc46d7170d +size 620844 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/public_gate_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/staged_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..954ee901c436870528834dbd01f691a15d3d9dc7 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "500", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "300" + } +} \ No newline at end of file diff --git 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"2026-05-04T17:51:34", + "ended_at": "2026-05-04T17:51:51", + "duration_sec": 16.259 + }, + "generate": { + "started_at": "2026-05-04T17:51:51", + "ended_at": "2026-05-04T17:51:56", + "duration_sec": 5.045 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/staged/public/staged_features.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": 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\ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/staged/tvae/model_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3d40ff2b935773ff96bb0fad06628e8a7fd619a9 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv new file mode 100644 index 0000000000000000000000000000000000000000..f4b76c08fd9e79d194664a18efca7c2da7973dba --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd6e35a64f02e454c60026dced645a026e8ff462523a2befdd87293700edee0e +size 41542 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..15ffa754523d7c1b3a6284549e839113f4d69d20 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e493362becc793f8b983b6bd6bfc45c3b7fd1422 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/gen_20260504_175232.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/gen_20260504_175232.log new file mode 100644 index 0000000000000000000000000000000000000000..e96d2b0bb30e659460d8b3287452895d64ac6b35 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/gen_20260504_175232.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8ff2fdb44fa6e18bdeb7130618b3ebdd47cd7c82320e407c9cad2778a38258b +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/input_snapshot.json 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"profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/models_300epochs/train_20260504_175208.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/models_300epochs/train_20260504_175208.log new file mode 100644 index 0000000000000000000000000000000000000000..bd12ed223dd5717571a179c201d36a613606c8c5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/models_300epochs/train_20260504_175208.log @@ -0,0 +1,3 @@ +version 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b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/public_gate/staged_input_manifest.json 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b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:52:08", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/runtime_result.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..2571fd685f2ca86d9026a5aeb4b2d5d2135271e0 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_175208", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:52:08", + "ended_at": "2026-05-04T17:52:32", + "duration_sec": 24.013 + }, + "generate": { + "started_at": "2026-05-04T17:52:32", + "ended_at": "2026-05-04T17:52:37", + "duration_sec": 5.103 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + 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+size 5176 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_report.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..dd934cb8736896aea413f6c1ee693f46b6a150f2 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_transforms_applied.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b2dfb70e860332f2d4729abbb0e5335fa3f2fa58 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv new file mode 100644 index 0000000000000000000000000000000000000000..f87119bdc86f96e711c84b1e0e548aef02373279 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfa0ee0f5edf291d4a1440b73cc43eb64a1bef6799cdf66405b3384a12031f3f +size 41049 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175208/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175208/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/_tvae_generate.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e07148c1ba4ddca992b0f7e20cf0df0dcdeb69c0 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/_tvae_train.py b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c032455229e3f6f5d8909e0b2a91cfe61ca5b48b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/gen_20260504_175313.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/gen_20260504_175313.log new file mode 100644 index 0000000000000000000000000000000000000000..aba55b7de57d2847ab08ab93541dc182fd99c817 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/gen_20260504_175313.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6fd3a537a0edd5fbdc6f85ba4458aafb0b13c7a9e94d15ec3e50395e2b28182b +size 404 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/input_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/models_300epochs/train_20260504_175249.log b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/models_300epochs/train_20260504_175249.log new file mode 100644 index 0000000000000000000000000000000000000000..4d871338efdc74e0d6149b21cbb781486f4d93bb --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/models_300epochs/train_20260504_175249.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36225451e64c28e079e1ffc5a02a1ff4755ba438ebe0f77dc8c5e87cafc124df +size 619 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c83492b85c2b473e1c5456cd62dd742606c08223 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6784c6b8e4a0d3d2654f738df5614002347068fc0f6512daf0970b15d29c6106 +size 806572 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/public_gate/normalized_schema_snapshot.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + 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b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..75e186f4667b8837057d5c45d32abccf2e5dd0e4 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/run_config.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..fc3643ef5c96749e6ef0e5aacb3ae517516134be --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:52:49", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "256", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/runtime_result.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..0cb85f7daa3338b4f0cbada27e90ac334c284ed3 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_175249", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:52:49", + "ended_at": "2026-05-04T17:53:13", + "duration_sec": 23.837 + }, + "generate": { + "started_at": "2026-05-04T17:53:13", + "ended_at": "2026-05-04T17:53:18", + "duration_sec": 5.187 + } + } +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/val.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/public/val.csv new file mode 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_transforms_applied.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ff76871e41b31780ffa015bfa09409fab9868e57 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae3153f5e356d1b61c8b9d640e35d36e141387c2 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef368b14d1cd6f881c56fcba6683fbb15c133cf8ab7331cea2484b675b53bfc6 +size 41456 diff --git a/hyperparameter/c2/tvae/tvae-c2-20260504_175249/tvae_metadata.json b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/hyperparameter/c2/tvae/tvae-c2-20260504_175249/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/_arf_generate.py b/hyperparameter/m4/arf/arf-m4-20260504_205305/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ad9ee45eaa6fc3dc9697444e3c5032b419f656d8 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(2217) +c_csv = "/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm4' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf-m4-2217-20260504_205312.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf-m4-2217-20260504_205312.csv") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/_arf_train.py b/hyperparameter/m4/arf/arf-m4-20260504_205305/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6349cf1812f1b0bd19d36bc67e0847e7f33a5180 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf_model.pkl") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/arf-m4-2217-20260504_205312.csv b/hyperparameter/m4/arf/arf-m4-20260504_205305/arf-m4-2217-20260504_205312.csv new file mode 100644 index 0000000000000000000000000000000000000000..12a45f7ba37448b064da8ca0866716081a45176f --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/arf-m4-2217-20260504_205312.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5a419cc6c0acab6c1b567675a366402bd8b1f3004e4c98a653dbcaa3390de9c9 +size 191830 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/arf_model.pkl b/hyperparameter/m4/arf/arf-m4-20260504_205305/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..59f679da7c159e590d20d730f71305232878e75b --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9198c0944cd4115e4f6ed7fa9889c4f75460942eff750d4c47408882442beca5 +size 1831127 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/gen_20260504_205312.log b/hyperparameter/m4/arf/arf-m4-20260504_205305/gen_20260504_205312.log new file mode 100644 index 0000000000000000000000000000000000000000..1571c4aa8cd0b4ea4a1cbe16c5bfa000146c8df8 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/gen_20260504_205312.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5a903f3a546d8ede07b3c99a669e4629c17741f274500f615347f8bf39b7e2d +size 1667 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/input_snapshot.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a3fec1973e249527909ae104dcdbe1f735528632 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + 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"impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/public_gate_report.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/staged_input_manifest.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bbe6712f445b93c75a373b8e1ebbbb64a7fde0f6 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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"impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/run_config.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..cb287ce04d6be9af7420a8b34132ddedd147c5f8 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:53:05", + "dataset_id": "m4", + "model": "arf", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "arf", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf-m4-2217-20260504_205312.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/arf/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "ARF_CLIP_QUANTILE_HIGH": "0.999", + "ARF_CLIP_QUANTILE_LOW": "0.001", + "ARF_DELTA": "0", + "ARF_EARLY_STOP": "true", + "ARF_MAX_ITERS": "5", + "ARF_MIN_NODE_SIZE": "7", + "ARF_NUM_TREES": "10" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/runtime_result.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..32c9b46796d654b848dc43a23e0431d9bf242386 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "arf", + "run_id": "arf-m4-20260504_205305", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf-m4-2217-20260504_205312.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:53:05", + "ended_at": "2026-05-04T20:53:12", + "duration_sec": 7.262 + }, + "generate": { + "started_at": "2026-05-04T20:53:12", + "ended_at": "2026-05-04T20:53:15", + "duration_sec": 2.737 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/adapter_report.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3e5f7973678a6614f62a538e6e24380f4ed83176 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/adapter_transforms_applied.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/model_input_manifest.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ebfed85262aff22fc32c03e2790b1afdb8f731c3 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/arf/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "arf", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205305/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/staged_features.json b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/test.csv b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/train.csv b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/val.csv b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205305/train_20260504_205305.log b/hyperparameter/m4/arf/arf-m4-20260504_205305/train_20260504_205305.log new file mode 100644 index 0000000000000000000000000000000000000000..0a84693518d430c46923e90173ac86ed2ad3d124 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205305/train_20260504_205305.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2955db49632aa3572510d2bb0b8e0a2ec2ea5973a03b1f33758e883ccaad5776 +size 999 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205329/_arf_generate.py b/hyperparameter/m4/arf/arf-m4-20260504_205329/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..11d4e0a20cfbeee12cb5109a75e84e1b2bea09ca --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205329/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(2217) +c_csv = "/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205329/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205329/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm4' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205329/arf-m4-2217-20260504_205339.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205329/arf-m4-2217-20260504_205339.csv") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205329/_arf_train.py b/hyperparameter/m4/arf/arf-m4-20260504_205329/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..90db425f2be8314dcc36e3b8b9cfd902e2e23b6e --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205329/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205329/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205329/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205329/arf_model.pkl") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205329/arf-m4-2217-20260504_205339.csv b/hyperparameter/m4/arf/arf-m4-20260504_205329/arf-m4-2217-20260504_205339.csv new file mode 100644 index 0000000000000000000000000000000000000000..d687c2135435d2715d2c27241139962f3b343b56 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205329/arf-m4-2217-20260504_205339.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d72aa6b7180580e1d1b07a085671da0433ea9c0bb0a9713477f1a49b4a7b3d13 +size 191017 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205329/arf_model.pkl b/hyperparameter/m4/arf/arf-m4-20260504_205329/arf_model.pkl new file mode 100644 index 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/dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205329/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205329/train_20260504_205329.log b/hyperparameter/m4/arf/arf-m4-20260504_205329/train_20260504_205329.log new file mode 100644 index 0000000000000000000000000000000000000000..63603a17ecca9d1719620cac67ce41866ba39c11 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205329/train_20260504_205329.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e3d7c33623752ce79385f065cb70bad3f52d7b8a1cd8594641d6a0f0c3e5b336 +size 576 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/_arf_generate.py b/hyperparameter/m4/arf/arf-m4-20260504_205355/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..302a720e1ba1b3a3683750e2c9f916538103c27b --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(2217) +c_csv = "/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm4' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf-m4-2217-20260504_205409.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf-m4-2217-20260504_205409.csv") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/_arf_train.py b/hyperparameter/m4/arf/arf-m4-20260504_205355/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..359ff84ff37fe643f569df6d7860c8cc220d7efa --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf_model.pkl") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/arf-m4-2217-20260504_205409.csv b/hyperparameter/m4/arf/arf-m4-20260504_205355/arf-m4-2217-20260504_205409.csv new file mode 100644 index 0000000000000000000000000000000000000000..bba514e30ba81046bfba52e2d5f90bd9a2563f95 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/arf-m4-2217-20260504_205409.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8aa6c55bb900a11e799751616373f0d749384740bd0efcfe57e7eb0d33ac9c5 +size 188337 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/arf_model.pkl b/hyperparameter/m4/arf/arf-m4-20260504_205355/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..68bfcb728589fcf085c1a9ab6017fa676189d7a7 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e75fed15baa2c44022000e54cc6b851d5f30eebd3f7ae3701aba438cceee78ba +size 15175634 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/gen_20260504_205409.log b/hyperparameter/m4/arf/arf-m4-20260504_205355/gen_20260504_205409.log new file mode 100644 index 0000000000000000000000000000000000000000..5f4ef03d4a2640ad876d3e5158dcccf232cadc40 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/gen_20260504_205409.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5e3295b9af829068a3e40179724bbe3741c27f0e9f3e31076d683e82a140e203 +size 3563 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/input_snapshot.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a3fec1973e249527909ae104dcdbe1f735528632 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": 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a/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/public_gate_report.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/staged_input_manifest.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6d3f93c104e271cc1f6cf7361919703235b8c625 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/train.csv", + "val_csv": 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"1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/run_config.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..099e77ec2b0c76a1a8214bf2c8161373c0f58bb4 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:53:55", + "dataset_id": "m4", + "model": "arf", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "arf", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf-m4-2217-20260504_205409.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/arf/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "ARF_CLIP_QUANTILE_HIGH": "0.999", + "ARF_CLIP_QUANTILE_LOW": "0.001", + "ARF_DELTA": "0", + "ARF_EARLY_STOP": "true", + "ARF_MAX_ITERS": "10", + "ARF_MIN_NODE_SIZE": "3", + "ARF_NUM_TREES": "50" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/runtime_result.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..06f192895e5f1dfe4981af3dfd34350bb8a5cbc1 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "arf", + "run_id": "arf-m4-20260504_205355", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf-m4-2217-20260504_205409.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:53:55", + "ended_at": "2026-05-04T20:54:09", + "duration_sec": 13.2 + }, + "generate": { + "started_at": "2026-05-04T20:54:09", + "ended_at": "2026-05-04T20:54:12", + "duration_sec": 3.003 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/adapter_report.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..6af95cb1d3a5ced14622c21c89ffb5e1ffcc5853 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/adapter_transforms_applied.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/model_input_manifest.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..eb345f1736001d9a9c1cc8b3cb799b290ad7ef35 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/arf/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "arf", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205355/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/staged_features.json b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/test.csv b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/train.csv b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/val.csv b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205355/train_20260504_205355.log b/hyperparameter/m4/arf/arf-m4-20260504_205355/train_20260504_205355.log new file mode 100644 index 0000000000000000000000000000000000000000..e614e604a76ae6a554f5efdda63acee673ee6a55 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205355/train_20260504_205355.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a1b6818431b1ace9254e69e5dec5eee854f9541507f504bdd47e64a5e8b39a9f +size 576 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/_arf_generate.py b/hyperparameter/m4/arf/arf-m4-20260504_205426/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..21eed4cb2434b7b1c66193680df4bc46689ef14a --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(2217) +c_csv = "/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm4' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf-m4-2217-20260504_205447.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf-m4-2217-20260504_205447.csv") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/_arf_train.py b/hyperparameter/m4/arf/arf-m4-20260504_205426/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0e5b3236a535f8ac2f54359e817a73eb9d30e99d --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf_model.pkl") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/arf-m4-2217-20260504_205447.csv b/hyperparameter/m4/arf/arf-m4-20260504_205426/arf-m4-2217-20260504_205447.csv new file mode 100644 index 0000000000000000000000000000000000000000..af4f4cf7df4f233b9bf303533c3d238c90e9f609 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/arf-m4-2217-20260504_205447.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a0182b20bcee272c6f3856af4ff12bce9efd4a04170be459f9c666907fdd0c0f +size 187690 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/arf_model.pkl b/hyperparameter/m4/arf/arf-m4-20260504_205426/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..33cdf01598000ab0c9b01305fa2564b31199c75c --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e768c97019cd1ac75fd9e0f385d9c3000d36bafab0bc35b5317a733a853a540 +size 30797854 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/gen_20260504_205447.log b/hyperparameter/m4/arf/arf-m4-20260504_205426/gen_20260504_205447.log new file mode 100644 index 0000000000000000000000000000000000000000..eb2c43dd880f874672c87157cba19cb001d2f336 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/gen_20260504_205447.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb567a8804cce6fda0eadcda15db2c165cc300c6f1e35b5f85f6718e3cef4f62 +size 1667 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/input_snapshot.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a3fec1973e249527909ae104dcdbe1f735528632 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/public_gate_report.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/staged_input_manifest.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3caf5ea926f17140ac469c7462e8cf6e2ee35d39 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": 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"impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/run_config.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..bbfbcaae0e7274d25d58b22fba211c1a0bc2581b --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:54:26", + "dataset_id": "m4", + "model": "arf", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "arf", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf-m4-2217-20260504_205447.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/public_gate/staged_input_manifest.json", + "model_manifest": 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0000000000000000000000000000000000000000..2ea2182a785b9131587efb6961b82de235e00d7f --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "arf", + "run_id": "arf-m4-20260504_205426", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf-m4-2217-20260504_205447.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:54:26", + "ended_at": "2026-05-04T20:54:47", + "duration_sec": 21.466 + }, + "generate": { + "started_at": "2026-05-04T20:54:47", + "ended_at": "2026-05-04T20:54:50", + "duration_sec": 2.827 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/adapter_report.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..319e4bd0695a7fd28ff705e602c6f5d48e00592e --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/adapter_transforms_applied.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/model_input_manifest.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bf932b608826989beaa254c01dbff2ec1ab859a8 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/arf/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "arf", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + 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"missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205426/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/staged_features.json b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/test.csv b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/train.csv b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/val.csv b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205426/train_20260504_205426.log b/hyperparameter/m4/arf/arf-m4-20260504_205426/train_20260504_205426.log new file mode 100644 index 0000000000000000000000000000000000000000..08f83648f12f2af09e37b3149ae1a7f780f89983 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205426/train_20260504_205426.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00842fae2957aac634a982d90db5e350bb482f71618b1255231a98bc16ecf0d9 +size 578 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205503/_arf_generate.py b/hyperparameter/m4/arf/arf-m4-20260504_205503/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ed465e68b35f899ae78da01701cb7da7cdcc2470 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205503/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(2217) +c_csv = "/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205503/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205503/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm4' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205503/arf-m4-2217-20260504_205543.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205503/arf-m4-2217-20260504_205543.csv") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205503/_arf_train.py b/hyperparameter/m4/arf/arf-m4-20260504_205503/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..272d2f63aec86e89fcd147f59af6208e4f786c11 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205503/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205503/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205503/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260504_205503/arf_model.pkl") diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205503/arf-m4-2217-20260504_205543.csv b/hyperparameter/m4/arf/arf-m4-20260504_205503/arf-m4-2217-20260504_205543.csv new file mode 100644 index 0000000000000000000000000000000000000000..e35078a86093917140b05b44df6dcce09d4559f9 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205503/arf-m4-2217-20260504_205543.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72ea395dc38ed8caf8807293d5f906dcd0821b3a2899b8513e650936dfb0705f +size 90083 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205503/arf_model.pkl b/hyperparameter/m4/arf/arf-m4-20260504_205503/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..d17deb9da445d7fac83df0167c6d12c27907c6b8 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205503/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b9201d48c1ac513e0c1050763e8345f0e3d2372810fa94fec980e79504d6584 +size 64537508 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205503/gen_20260504_205543.log b/hyperparameter/m4/arf/arf-m4-20260504_205503/gen_20260504_205543.log new file mode 100644 index 0000000000000000000000000000000000000000..43901bf537aa623bc60e73e9cd4e5d8d00aac953 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205503/gen_20260504_205543.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05c002f2400a647615393b18ba7dfd7071471b807585e039e886654550f1cb82 +size 691 diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205503/input_snapshot.json b/hyperparameter/m4/arf/arf-m4-20260504_205503/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a3fec1973e249527909ae104dcdbe1f735528632 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205503/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/arf/arf-m4-20260504_205503/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/arf/arf-m4-20260504_205503/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/arf/arf-m4-20260504_205503/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + 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0000000000000000000000000000000000000000..8c15fdc8c08bf9894ddddb91cdfd385dc34ec4f1 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(2217) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet-m4-2217-20260504_205607.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet-m4-2217-20260504_205607.csv") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/_bayesnet_train.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..92df9e083983af73a8c82ee883db3ac159f0b762 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet-m4-2217-20260504_205607.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet-m4-2217-20260504_205607.csv new file mode 100644 index 0000000000000000000000000000000000000000..b1422d0d3670a43f536805293468794789645630 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet-m4-2217-20260504_205607.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5d9612db6e193b80e988525721f3861d9ffb17ce04b0c416a03227d35506398 +size 207261 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_coltypes.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..295bb70445b63eea5719ae7c1f72bcdf61849c8d --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..350018ced7d0189da2a5d24ce4d62ae9f7536292 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a0bc0873a86281b86c0d9147024d7b1a25ed19ce6c6bbba5ba0511741705255 +size 4247 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/const_cols.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/gen_20260504_205607.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/gen_20260504_205607.log new file mode 100644 index 0000000000000000000000000000000000000000..ec04f1ff6829e79efddcc61ccc96528fd3df4b47 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/gen_20260504_205607.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96d2a251d8a493681634da08c884f09b9670a1776ffbdc4a71dda46f3d2aa116 +size 3661 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/input_snapshot.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..80a11fda595a83a7857ca988e84c480002e80ddc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/public_gate_report.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/staged_input_manifest.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5893e405762ce0a9ebd422698e775d4479f062da --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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+ "model": "bayesnet", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet-m4-2217-20260504_205607.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BAYESNET_DISABLE_AUTO_CAPS": "1", + "BAYESNET_EDGE_WEIGHTS_FN": "mutual_info", + "BAYESNET_ESTIMATOR_TYPE": "chow-liu", + "BAYESNET_FIT_ROWS": "50000", + "BAYESNET_MAX_BINS": "4", + "BAYESNET_MAX_CAT_LEVELS": "32", + "BAYESNET_N_JOBS": "1", + "BAYESNET_STRUCT_ROWS": "10000" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/runtime_result.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7b9995f6ae1faa8f1a074c131d62dc10490f4331 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "run_id": "bayesnet-m4-20260504_205559", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet-m4-2217-20260504_205607.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:55:59", + "ended_at": "2026-05-04T20:56:07", + "duration_sec": 7.615 + }, + "generate": { + "started_at": "2026-05-04T20:56:07", + "ended_at": "2026-05-04T20:56:15", + "duration_sec": 8.007 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/adapter_report.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..c2e83633aab32a15a82e6be96ee55f0b3aa6321e --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/adapter_transforms_applied.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/model_input_manifest.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4d0595f1df21db7c3985b7963e9181a694c60e95 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205559/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/staged_features.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + 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b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/train_20260504_205559.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/train_20260504_205559.log new file mode 100644 index 0000000000000000000000000000000000000000..a7c012b3fb51a8c33d8fa9012c9ae5964ca44a1e --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205559/train_20260504_205559.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c45adcabd4e5caa4a96c27f5fdae029bbf52024fe8d2b0738d84ef9be8d03d4a +size 3851 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/_bayesnet_generate.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ae4287a8b9e86f5a2e4a0415e77b2456ca76b202 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(2217) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet-m4-2217-20260504_205636.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet-m4-2217-20260504_205636.csv") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/_bayesnet_train.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7b5793228015cf1a7056604d023ea6af3653b819 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet-m4-2217-20260504_205636.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet-m4-2217-20260504_205636.csv new file mode 100644 index 0000000000000000000000000000000000000000..a1b439c3183a1cb808a55618359db932b3e40522 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet-m4-2217-20260504_205636.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc9d211b795a6bd7032af7e12a7490994f9fcd9a3f7bef68aca63952bc710359 +size 207044 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_coltypes.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..295bb70445b63eea5719ae7c1f72bcdf61849c8d --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..279c544a228de35c86995bc924aeb3ce918bfc3b --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85b9ea706eb7d6e707bf6a8d121b9b09bc705dbdac97841bc5902237f6e6834b +size 4655 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/const_cols.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/gen_20260504_205636.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/gen_20260504_205636.log new file mode 100644 index 0000000000000000000000000000000000000000..7e5a001681022b5f311225c7605665289181837e --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/gen_20260504_205636.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:22c5f1bff719947d3866628bdbb7532c82c8e73de8c6a7d49a84e9382259fa0a +size 3661 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/input_snapshot.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..80a11fda595a83a7857ca988e84c480002e80ddc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/public_gate_report.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/staged_input_manifest.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..39542a5c2cd3a702610efa658ab87549fa06964f --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/run_config.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..a71111ead143f271c99eca8cf7b7ed6ef8c6e556 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/run_config.json @@ -0,0 +1,47 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:56:29", + "dataset_id": "m4", + "model": "bayesnet", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "bayesnet", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet-m4-2217-20260504_205636.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BAYESNET_DISABLE_AUTO_CAPS": "1", + "BAYESNET_EDGE_WEIGHTS_FN": "mutual_info", + "BAYESNET_ESTIMATOR_TYPE": "chow-liu", + "BAYESNET_FIT_ROWS": "80000", + "BAYESNET_MAX_BINS": "5", + "BAYESNET_MAX_CAT_LEVELS": "64", + "BAYESNET_N_JOBS": "1", + "BAYESNET_STRUCT_ROWS": "20000" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/runtime_result.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..35186937a3970e10a8339b8040b443307a8b1294 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "run_id": "bayesnet-m4-20260504_205629", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet-m4-2217-20260504_205636.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:56:29", + "ended_at": "2026-05-04T20:56:36", + "duration_sec": 7.305 + }, + "generate": { + "started_at": "2026-05-04T20:56:36", + "ended_at": "2026-05-04T20:56:44", + "duration_sec": 7.95 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/adapter_report.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..7de59bafab49ea59eef870004a3f317f6b5d95bc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/adapter_transforms_applied.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/model_input_manifest.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..03cecbbf38848586c57c667a5518eb16f7988f2c --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205629/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/staged_features.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/test.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/test.csv @@ -0,0 +1,3 @@ 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b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/train_20260504_205629.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/train_20260504_205629.log new file mode 100644 index 0000000000000000000000000000000000000000..1bacfd2d5116cc3da2219cc0ef63627edb322abb --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205629/train_20260504_205629.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:16676da7bfd4384fbd25b88bce18888b3b16cdb4fd7175725106d01d5eaa97d4 +size 3851 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/_bayesnet_generate.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9e34d4a2783460163ce53c62f68c0ef06071e75a --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(2217) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet-m4-2217-20260504_205706.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet-m4-2217-20260504_205706.csv") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/_bayesnet_train.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a1936af6fea5cfda05039b4180eb0078786bfe10 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet-m4-2217-20260504_205706.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet-m4-2217-20260504_205706.csv new file mode 100644 index 0000000000000000000000000000000000000000..6e177403c40369f7eae5c364123eafa70b11b285 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet-m4-2217-20260504_205706.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:051a3f1de1b35303b823ec33a0a8fa0fc357570ebeb3110a516996e23c669fc7 +size 207056 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_coltypes.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..295bb70445b63eea5719ae7c1f72bcdf61849c8d --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..129ec19fa36887418d8d1fa2a659a7facd9daf5a --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31322881b7c0265a1f6a7e0bc3773213e8d15790d091f376279098278f7c5a82 +size 6098 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/const_cols.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/gen_20260504_205706.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/gen_20260504_205706.log new file mode 100644 index 0000000000000000000000000000000000000000..f4ce296a7371bd392c02647777f85912f02eff3c --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/gen_20260504_205706.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a718c10c0d013c4c0e5f1f0e3b302d7a19467a910cd1ffeddec8aa216bc3def5 +size 3661 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/input_snapshot.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..80a11fda595a83a7857ca988e84c480002e80ddc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/public_gate_report.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/staged_input_manifest.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ed37262f0a2c52da06a1a62bab91e6a74ac8178e --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/run_config.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..4e7939aa7cf04a7aff9e9d6badc551af1e84cc7b --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/run_config.json @@ -0,0 +1,47 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T20:56:58", + "dataset_id": "m4", + "model": "bayesnet", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "bayesnet", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet-m4-2217-20260504_205706.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BAYESNET_DISABLE_AUTO_CAPS": "1", + "BAYESNET_EDGE_WEIGHTS_FN": "mutual_info", + "BAYESNET_ESTIMATOR_TYPE": "chow-liu", + "BAYESNET_FIT_ROWS": "120000", + "BAYESNET_MAX_BINS": "8", + "BAYESNET_MAX_CAT_LEVELS": "128", + "BAYESNET_N_JOBS": "1", + "BAYESNET_STRUCT_ROWS": "25000" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/runtime_result.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..0350216edeea111c12c64ec31595d2ffddf46886 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "run_id": "bayesnet-m4-20260504_205658", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet-m4-2217-20260504_205706.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:56:58", + "ended_at": "2026-05-04T20:57:06", + "duration_sec": 7.918 + }, + "generate": { + "started_at": "2026-05-04T20:57:06", + "ended_at": "2026-05-04T20:57:13", + "duration_sec": 7.565 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/adapter_report.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..36f1fafd27243251a77acd81e74802d020d278c4 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/adapter_transforms_applied.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/model_input_manifest.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b93ba544cb55d97c9e0519f050dead76f5ebfc73 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205658/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/staged_features.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/test.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/train.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/val.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/train_20260504_205658.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/train_20260504_205658.log new file mode 100644 index 0000000000000000000000000000000000000000..6e877a4040799b7586d1540618c07749363bd3e8 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205658/train_20260504_205658.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:842cc7eedb2f4376cb9f041fbc400a2163718f8b1cf65f0489063dba6c7c33e2 +size 3853 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/_bayesnet_generate.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..71e7485e66b732e9ed2d6084e61f072cea5b2049 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(2217) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet-m4-2217-20260504_205735.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet-m4-2217-20260504_205735.csv") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/_bayesnet_train.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fa2f6896ca8c945632e960f4aee83dbce46e1274 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet-m4-2217-20260504_205735.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet-m4-2217-20260504_205735.csv new file mode 100644 index 0000000000000000000000000000000000000000..2270da63d9e6ee65aebccfe199387355254c39fa --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet-m4-2217-20260504_205735.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a81b9d6efb8599576ceb153a27c73d0285f4e47e5d7b2c7550e957aabbf0e703 +size 207113 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_coltypes.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..295bb70445b63eea5719ae7c1f72bcdf61849c8d --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..129ec19fa36887418d8d1fa2a659a7facd9daf5a --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31322881b7c0265a1f6a7e0bc3773213e8d15790d091f376279098278f7c5a82 +size 6098 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/const_cols.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/gen_20260504_205735.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/gen_20260504_205735.log new file mode 100644 index 0000000000000000000000000000000000000000..dda98367c8f277a845dc7afa64c78db7693c5a57 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/gen_20260504_205735.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3af3cc3154bf64b37b920b84a86c5772af0f2eff01ddd18e84c87448dd4736c +size 3661 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/input_snapshot.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..80a11fda595a83a7857ca988e84c480002e80ddc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": 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"c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + 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"21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/public_gate_report.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/staged_input_manifest.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7221ca85f5f73da4cdbb4405d82b2c8a54205467 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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+ "model": "bayesnet", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet-m4-2217-20260504_205735.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BAYESNET_DISABLE_AUTO_CAPS": "1", + "BAYESNET_EDGE_WEIGHTS_FN": "adjusted_mutual_info", + "BAYESNET_ESTIMATOR_TYPE": "chow-liu", + "BAYESNET_FIT_ROWS": "120000", + "BAYESNET_MAX_BINS": "8", + "BAYESNET_MAX_CAT_LEVELS": "64", + "BAYESNET_N_JOBS": "1", + "BAYESNET_STRUCT_ROWS": "25000" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/runtime_result.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..98ba8f7458300f4cde8abb0ee714039c682c7198 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "run_id": "bayesnet-m4-20260504_205727", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet-m4-2217-20260504_205735.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:57:27", + "ended_at": "2026-05-04T20:57:35", + "duration_sec": 7.932 + }, + "generate": { + "started_at": "2026-05-04T20:57:35", + "ended_at": "2026-05-04T20:57:42", + "duration_sec": 7.322 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/adapter_report.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..ade69028d19d58de670d1bb3f5ec59b586e0c391 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/adapter_transforms_applied.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/model_input_manifest.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..678f46a660c3df9d7689fbf67ebb30c9df3cd66f --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205727/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/staged_features.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/test.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/train.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/val.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/train_20260504_205727.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/train_20260504_205727.log new file mode 100644 index 0000000000000000000000000000000000000000..0e48deaae1dd0495b184bce9f07dd1e826163be4 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205727/train_20260504_205727.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab55c3f003c1b6bfdbe343730de023ece61d9a6fede4975cf888a4e7c438f86c +size 3861 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/_bayesnet_generate.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..375613ee36c0eb4e6d5342e7be6eaa838b1185d5 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(2217) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet-m4-2217-20260504_205803.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet-m4-2217-20260504_205803.csv") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/_bayesnet_train.py b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8e6a1243780fc6cce188c35f3c293263cbba5a6f --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet_model.pkl") diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet-m4-2217-20260504_205803.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet-m4-2217-20260504_205803.csv new file mode 100644 index 0000000000000000000000000000000000000000..3ae7bcba921b8798105ba8d9742cab7d62f751f3 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/bayesnet-m4-2217-20260504_205803.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260504_205755/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/staged_features.json b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/test.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/train.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/val.csv b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/train_20260504_205755.log b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/train_20260504_205755.log new file mode 100644 index 0000000000000000000000000000000000000000..70852fed45c01312d145882533e68727c55bb794 --- /dev/null +++ b/hyperparameter/m4/bayesnet/bayesnet-m4-20260504_205755/train_20260504_205755.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0cc4b16acc295190761a677a600e61292a2c451dd5e5158929c35c55e3195a0f +size 3863 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_generate.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..563f6bea6aa8d6a1a16da4850ba8a90c0cfc96a1 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_train.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..90d9736982b710db248ddd882246ba9562d061c8 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=8, + generator_dim=(16, 16), + discriminator_dim=(16, 16), + batch_size=32, + pac=1, + epochs=50, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv new file mode 100644 index 0000000000000000000000000000000000000000..5c047df18e289162d7342334349e940f451dc505 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9d5b1fd2a8f637e700f3164b1aa7833a9eeba0084237d900006fbf35829a1009 +size 135963 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/ctgan_metadata.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/gen_20260504_162609.log b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/gen_20260504_162609.log new file mode 100644 index 0000000000000000000000000000000000000000..4dec0f96e5a80b73bcae9d62fe3758231d7ad60d --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/gen_20260504_162609.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:631cad7bb802b05edec1212c5654bc3cf2b87f637cb37bb0269c684a86a0a78b +size 297 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/input_snapshot.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..74d60aada6757cb50756979c746af4977a01a3d5 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0fcbc1eca52b300da8de0000993e73b4d58d44408c58f5e94824b040a9312f97 +size 280461 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/train_20260504_162455.log b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/train_20260504_162455.log new file mode 100644 index 0000000000000000000000000000000000000000..b862c69bf2d88bf63062508f45c2c94c1b1f0a63 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/train_20260504_162455.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:421ee609e428857108a8ef1840b7d88d9b1a05532c27ba8b20235b9b582126f8 +size 10928 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..de8fec055242a76a46100b112369488b7a1a3b54 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + 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+ "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/run_config.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..65b52b73614d057167e888ab36f2fa74e106b161 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:24:55", + "dataset_id": "m4", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "32", + "CTGAN_DEFAULT_EPOCHS": "50", + "CTGAN_DISCRIMINATOR_DIMS": "16,16", + "CTGAN_EMBEDDING_DIM": "8", + "CTGAN_GENERATOR_DIMS": "16,16", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/runtime_result.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..68cb13745d1370732ba3f4f2b3936fde950009e4 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "run_id": "ctgan-m4-20260504_162455", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:24:55", + "ended_at": "2026-05-04T16:26:09", + "duration_sec": 74.098 + }, + "generate": { + "started_at": "2026-05-04T16:26:09", + "ended_at": "2026-05-04T16:26:15", + "duration_sec": 5.73 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_report.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..b9d0293168cdd6f75a7982799521ddd525f21883 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..113d24a0e03711e2cee69ee4ebfa63e6f8632a90 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + 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"example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_generate.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..cdef9a9fbb89b21fb93106f5cba82484dc209f91 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_train.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..641f6347bdb1791cfb4d69ab521cbb669f678005 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=64, + pac=1, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv new file mode 100644 index 0000000000000000000000000000000000000000..324aa30fc8c04f9c70c6622270c46f790870065b --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4ad246572c9acdde64b136e255d90992eee5bac1408041de15f542602d5f7c5 +size 135924 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/ctgan_metadata.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/ctgan_metadata.json new file 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/test.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/val.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162628/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_generate.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..56154cfced3a7c24b394769111b46be23741c05f --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_train.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b461b498a3b3fe0aa2e514b9cbe2736069c01a87 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=128, + pac=1, + epochs=150, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv new file mode 100644 index 0000000000000000000000000000000000000000..50880bc8444a2e81313982a10d35af8dd5ecc7c4 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:384120f54add6d314fafdc5938da22645316ce9201f4847812c063015d105e86 +size 135501 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/ctgan_metadata.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/gen_20260504_162901.log b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/gen_20260504_162901.log new file mode 100644 index 0000000000000000000000000000000000000000..2c8740823b955b36adbc1e5f820b0819bea3b9bf --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/gen_20260504_162901.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53c84f13415a31957dfe62320cf0a57fc913c8b4622039b492c928ea4ee09517 +size 297 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/input_snapshot.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": 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a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..217f090ca1b40d37dd79db4d92718f4b8ade52d1 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5f0ddb51de8e66d1bb73ba247bb8d3781b7eb35a4fbd45c3e76624bbf2adb18 +size 339811 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/train_20260504_162802.log b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/train_20260504_162802.log new file mode 100644 index 0000000000000000000000000000000000000000..c61563e139d4cee1fd2b83fbc0d7e3c8fdf9d1a4 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/train_20260504_162802.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:621ab279edd3d52657cce8c48d67b29b97b292b746ad59317fcc87329dbf9f98 +size 29629 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + 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"21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a7acbe7288ea212067cd072ed988c4511e9ae1c5 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": 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+ "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "128", + "CTGAN_DEFAULT_EPOCHS": "150", + "CTGAN_DISCRIMINATOR_DIMS": "64,64", + "CTGAN_EMBEDDING_DIM": "32", + "CTGAN_GENERATOR_DIMS": "64,64", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/runtime_result.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..1d742290655adc88c195d0a4d1430817f337102b --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "run_id": "ctgan-m4-20260504_162802", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:28:02", + "ended_at": "2026-05-04T16:29:01", + "duration_sec": 58.433 + }, + "generate": { + "started_at": "2026-05-04T16:29:01", + "ended_at": "2026-05-04T16:29:07", + "duration_sec": 6.047 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_report.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..c3029518606f6993d8f6e8c961a0afd76cf5989b --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b322f966a05e8955544dcb79ac90ae5da186b160 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + 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"role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_generate.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..1c6869a3fe44882e4389b3896ee6dfd20585ca25 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_train.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1b7c20d0b22c8b49a0901ad3141ca60eb2badbdf --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=80, + pac=5, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv new file mode 100644 index 0000000000000000000000000000000000000000..3e72d45b8b8b9317621e27073d49108074f9aef9 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c406571e96f9c06734e3b7b6d3ba3c06c0069415306215bf688fab81de6a3f9 +size 135713 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/ctgan_metadata.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/gen_20260504_163019.log b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/gen_20260504_163019.log new file mode 100644 index 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/test.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/val.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_162920/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_generate.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9bd1b474a2c4e3a5589188b211b0e6e39efb81f0 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_train.py b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a841856a406bbb3e09660cb7d1a6488fd8eb5858 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=100, + pac=10, + epochs=200, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt") \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv new file mode 100644 index 0000000000000000000000000000000000000000..02a4410b10356bd5136a2b9cbee8b59d63cb1cea --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7baf2650acdb58a6960bf3482afc14f2a59738fbe3de156152a55cc7636ab39e +size 136085 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/ctgan_metadata.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/gen_20260504_163207.log b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/gen_20260504_163207.log new file mode 100644 index 0000000000000000000000000000000000000000..1794c145cc281f95b00fe5f46b3d2dc1ff9bc4aa --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/gen_20260504_163207.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:843b04a29b1a7c84294fbdad7fafc0ad169db22ca2b2b9ddbd5813e245293903 +size 297 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/input_snapshot.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": 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a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..60c1b8863a50d55ea3f90ff4f64bcf1822d472ff --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f7587270ae8535607fd4804d5bf9cf09d651bba7376264c2365fce4e6ce00c6 +size 341411 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/train_20260504_163038.log b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/train_20260504_163038.log new file mode 100644 index 0000000000000000000000000000000000000000..de0ac05ad9d6cff23800812ff6879cb55324c8bb --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/train_20260504_163038.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:393ba9d05340b1971bf7f5b702d02a8fee4ed5303a31bd46c1803bf4d6c4ddb6 +size 39009 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b816cabf716c275363c4a5a402d22bf38a2108aa --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": 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+ "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "100", + "CTGAN_DEFAULT_EPOCHS": "200", + "CTGAN_DISCRIMINATOR_DIMS": "64,64", + "CTGAN_EMBEDDING_DIM": "32", + "CTGAN_GENERATOR_DIMS": "64,64", + "CTGAN_PAC": "10" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/runtime_result.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7db01fc69ae2c574a3aebf602eb5ef1cb959b9e5 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "run_id": "ctgan-m4-20260504_163038", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:30:38", + "ended_at": "2026-05-04T16:32:07", + "duration_sec": 88.927 + }, + "generate": { + "started_at": "2026-05-04T16:32:07", + "ended_at": "2026-05-04T16:32:13", + "duration_sec": 5.981 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_report.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..c66b2d5d18b0bd9500d67f443aedbdf219804a82 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..033c4ca1a435c6ec97f441f3886476742ec4be6d --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + 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"role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_X_host.npy b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..adf7790ed3b5f3fbf003c878147469425b445ed1 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2075e38ffa201390419d8ba927a30948410d3bae79c47b2d376e0991c42ba455 +size 62204 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_gen.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..8461195844e9e59608fb1e081ec03cfac6a974e2 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(2217)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/forest-m4-2217-20260505_054728.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_meta_host.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..ee3e9bf77c9008274d3ca5b068e49b142b0bb624 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["age", "sex", "bmi", "children", "smoker", "region", "charges"], "cat_indexes": [1, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_train.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ebf266bca658e6ad6b9587f0adc8437183b95240 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=4 " + f"n_estimators=5 duplicate_K=2 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=4, n_estimators=5, duplicate_K=2, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/forestdiffusion_model.joblib') diff --git 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054626/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/staged_features.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/test.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/train.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/val.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/train_20260505_054626.log b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/train_20260505_054626.log new file mode 100644 index 0000000000000000000000000000000000000000..f17ddaae773256b16279e9fafb985a56a819affd --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054626/train_20260505_054626.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a44365bed894ecdfa5cf1a9a34ee88e79e8f9259a640ccb6676c613e915fc3ea +size 429 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_X_host.npy b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..adf7790ed3b5f3fbf003c878147469425b445ed1 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2075e38ffa201390419d8ba927a30948410d3bae79c47b2d376e0991c42ba455 +size 62204 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_gen.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..ec29d9182bf47aae67025a93411974f2ea4f0ea1 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(2217)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/forest-m4-2217-20260505_055051.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_meta_host.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..ee3e9bf77c9008274d3ca5b068e49b142b0bb624 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["age", "sex", "bmi", "children", "smoker", "region", "charges"], "cat_indexes": [1, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_train.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..726c140c6f57df4672d2b930c63be1f8e1d524b7 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=6 " + f"n_estimators=10 duplicate_K=3 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=6, n_estimators=10, duplicate_K=3, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/forestdiffusion_model.joblib') diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/forest-m4-2217-20260505_055051.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/forest-m4-2217-20260505_055051.csv new file mode 100644 index 0000000000000000000000000000000000000000..3924e39a733eca910ccd18ae7e7f9adcc22fb122 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/forest-m4-2217-20260505_055051.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37b229a926f7785325bd550af1f8c02b0d2d019131bd98c5400e9162bbeea484 +size 189165 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/forestdiffusion_model.joblib b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..14a52b73e6ed89af2eef7cebaef6389ca59a32a6 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13a2f7204a678be2500735b9fd0e20452bb11d5e248cdc8bfe788d241f51548a +size 814659 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/gen_20260505_055051.log b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/gen_20260505_055051.log new file mode 100644 index 0000000000000000000000000000000000000000..1ca3752a10acf013b0d98b93b25a616a0deabe9e --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/gen_20260505_055051.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f72bb35a28dbe32d45f6e806d3b96725efb97624b5ebc0cbc4c50caed39f6dd +size 294 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/input_snapshot.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5daa6d8c1d862349e305d0a5fcbd26d5361342b8 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": 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0000000000000000000000000000000000000000..14a52b73e6ed89af2eef7cebaef6389ca59a32a6 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13a2f7204a678be2500735b9fd0e20452bb11d5e248cdc8bfe788d241f51548a +size 814659 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/public_gate_report.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/staged_input_manifest.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/staged_input_manifest.json new file mode 100644 index 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T05:47:44", + "dataset_id": "m4", + "model": "forestdiffusion", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "forestdiffusion", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/models_fd/model.joblib", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/forest-m4-2217-20260505_055051.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "FORESTDIFFUSION_DUPLICATE_K": "3", + "FORESTDIFFUSION_MAX_DEPTH": "3", + "FORESTDIFFUSION_MAX_TRAIN_ROWS": "10000", + "FORESTDIFFUSION_N_ESTIMATORS": "10", + "FORESTDIFFUSION_N_JOBS": "1", + "FORESTDIFFUSION_N_T": "6" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/runtime_result.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..144a82bd289edfc01ee6c1878919c67ae06d669b --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "run_id": "forest-m4-20260505_054744", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/forest-m4-2217-20260505_055051.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/models_fd/model.joblib" + }, + "timings": { + "train": { + "started_at": "2026-05-05T05:47:44", + "ended_at": "2026-05-05T05:50:51", + "duration_sec": 187.055 + }, + "generate": { + "started_at": "2026-05-05T05:50:51", + "ended_at": "2026-05-05T05:50:53", + "duration_sec": 2.588 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/adapter_report.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2acd68d5a73c40329a5ea713047ceabbd8fdeb7b --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/adapter_transforms_applied.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/model_input_manifest.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..243f830d9964ab103894472c2890f0efd25265bf --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + 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"features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_054744/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/staged_features.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/test.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/train.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/val.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/train_20260505_054744.log b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/train_20260505_054744.log new file mode 100644 index 0000000000000000000000000000000000000000..95eaccf65be0368ae4d3edc99486b2911370292e --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_054744/train_20260505_054744.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57b840526d48358c1582d13530566c2af48d0b25f6967deeb5d11ff2fd7f7323 +size 432 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_X_host.npy b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..adf7790ed3b5f3fbf003c878147469425b445ed1 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2075e38ffa201390419d8ba927a30948410d3bae79c47b2d376e0991c42ba455 +size 62204 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_gen.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..0978243eacf9bdf1d3f7d3779b4d94495a13b768 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(2217)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/forest-m4-2217-20260505_055615.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_meta_host.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..ee3e9bf77c9008274d3ca5b068e49b142b0bb624 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["age", "sex", "bmi", "children", "smoker", "region", "charges"], "cat_indexes": [1, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_train.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c0c9213e8aa4b1efa235273dbf14de7e5aa4acc6 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=8 " + f"n_estimators=10 duplicate_K=4 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=8, n_estimators=10, duplicate_K=4, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/forestdiffusion_model.joblib') diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/forest-m4-2217-20260505_055615.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/forest-m4-2217-20260505_055615.csv new file mode 100644 index 0000000000000000000000000000000000000000..8e191d23480389076db00bc5a3588e52da0114eb --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/forest-m4-2217-20260505_055615.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0501d6252a94d5911d0c4ff4883d8b1380a49752983353aa11bff646a18c975a +size 187113 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/forestdiffusion_model.joblib b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..237a0703fad8a3958d86fc2c7ce5c1f14ec1c0f3 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b93f7a2673cefc1403f537d8290dc7ba39b8a469d6168fa4a796f56160f7d06 +size 1417649 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/gen_20260505_055615.log b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/gen_20260505_055615.log new file mode 100644 index 0000000000000000000000000000000000000000..f8408040492d6f1383c92e81a83d1691872a8233 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/gen_20260505_055615.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2e7548839d21cfa2dbac8dfa3c4bb8c01b3b0270766c38749a43ea99f8e9d73 +size 294 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/input_snapshot.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5daa6d8c1d862349e305d0a5fcbd26d5361342b8 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": 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--git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/public_gate/staged_input_manifest.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bcc9d2ce93c2ea02e1cfb177369295fda7322da6 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/val.csv", + "test_csv": 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a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/run_config.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..3a3843d1b18b3d4ba4fbf3f8861801acc5f4a0a1 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T05:51:06", + "dataset_id": "m4", + "model": "forestdiffusion", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "forestdiffusion", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + 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newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/adapter_report.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..b85a1e0a8ed93860dec8e3c6f1b91ce854f9249b --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/adapter_transforms_applied.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/model_input_manifest.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..33a3592430346dffb120bcf06d0f75c69ae83827 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "target_column": "charges", + "task_type": 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"19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055106/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/staged_features.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/test.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/train.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/val.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/train_20260505_055106.log b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/train_20260505_055106.log new file mode 100644 index 0000000000000000000000000000000000000000..421e55cf5321fec30228ae829052197ea3d2a666 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055106/train_20260505_055106.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bbf2ab745381460c98036bf7818b001f232f4949bf9985b6ab827734fe96ca08 +size 432 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_X_host.npy b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..adf7790ed3b5f3fbf003c878147469425b445ed1 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2075e38ffa201390419d8ba927a30948410d3bae79c47b2d376e0991c42ba455 +size 62204 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_gen.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..59f0e9e8c87f91430676cd76b03a5e12c8230108 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(2217)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/forest-m4-2217-20260505_060555.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_meta_host.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..ee3e9bf77c9008274d3ca5b068e49b142b0bb624 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["age", "sex", "bmi", "children", "smoker", "region", "charges"], "cat_indexes": [1, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_train.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a07f4f3e637bf5c4f9404513f027eda9379462f7 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=10 " + f"n_estimators=15 duplicate_K=5 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=10, n_estimators=15, duplicate_K=5, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/forestdiffusion_model.joblib') diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/forest-m4-2217-20260505_060555.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/forest-m4-2217-20260505_060555.csv new file mode 100644 index 0000000000000000000000000000000000000000..3b2b5502c3bc9e5f6dcfa4d2b311fba30cafa54b --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/forest-m4-2217-20260505_060555.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc829e9136d92981a008f8bf8770f071b49b668134f99618b8d8456173b2e93a +size 186501 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/forestdiffusion_model.joblib b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..a392076595ea1f3d5443b0f3c1c6ed6b3a2259fa --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0973579ce4b168a5c3070cfc42cf92799f2569c082cdceafbda7502739ad42ad +size 2489015 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/gen_20260505_060555.log b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/gen_20260505_060555.log new file mode 100644 index 0000000000000000000000000000000000000000..0affbbd4be9f889ba2d2a02de846767f0902ea89 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/gen_20260505_060555.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:10387f2403cf2921be75d4937c21c3277b08d1e18f024703143cc0c24eee40a3 +size 294 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/input_snapshot.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5daa6d8c1d862349e305d0a5fcbd26d5361342b8 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/models_fd/model.joblib b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..a392076595ea1f3d5443b0f3c1c6ed6b3a2259fa --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0973579ce4b168a5c3070cfc42cf92799f2569c082cdceafbda7502739ad42ad +size 2489015 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/public_gate_report.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/staged_input_manifest.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b70d623867c3d02e3a84bdba58ad86081d9e6fa4 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": 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"impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + 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"dataset_source_resolved": "new", + "cli_args": { + "model": "forestdiffusion", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/models_fd/model.joblib", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/forest-m4-2217-20260505_060555.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "FORESTDIFFUSION_DUPLICATE_K": "5", + "FORESTDIFFUSION_MAX_DEPTH": "4", + "FORESTDIFFUSION_MAX_TRAIN_ROWS": "30000", + "FORESTDIFFUSION_N_ESTIMATORS": "15", + "FORESTDIFFUSION_N_JOBS": "1", + "FORESTDIFFUSION_N_T": "10" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/runtime_result.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..c9cfd0f11389b84c90fed0e0413b55b53ca9dc6a --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "run_id": "forest-m4-20260505_055631", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/forest-m4-2217-20260505_060555.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/models_fd/model.joblib" + }, + "timings": { + "train": { + "started_at": "2026-05-05T05:56:31", + "ended_at": "2026-05-05T06:05:55", + "duration_sec": 564.211 + }, + "generate": { + "started_at": "2026-05-05T06:05:55", + "ended_at": "2026-05-05T06:05:58", + "duration_sec": 3.31 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/adapter_report.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e6728b4eb334cac1f17929181746bd50ca622ebd --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/adapter_transforms_applied.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/model_input_manifest.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4cb1542cfefabe1b87e7f26863bf879cca9122eb --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_055631/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/staged_features.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/test.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/train.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/val.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/train_20260505_055631.log b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/train_20260505_055631.log new file mode 100644 index 0000000000000000000000000000000000000000..b1772766627efdff4be8087874271fc809749d3d --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_055631/train_20260505_055631.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35ca1e9bc9c8fbb639965343d712fcffdacbbe1ff0ac3a36e1a22792bfae340e +size 433 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_X_host.npy b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..adf7790ed3b5f3fbf003c878147469425b445ed1 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2075e38ffa201390419d8ba927a30948410d3bae79c47b2d376e0991c42ba455 +size 62204 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_gen.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..6ea867dbab1f017db76d718371cb29e6e3492bc7 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(2217)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/forest-m4-2217-20260505_061917.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_meta_host.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..ee3e9bf77c9008274d3ca5b068e49b142b0bb624 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["age", "sex", "bmi", "children", "smoker", "region", "charges"], "cat_indexes": [1, 4, 5]} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_train.py b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1a6b951eb07d411ba74e67092be9bad1170e2ba6 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=10 " + f"n_estimators=20 duplicate_K=5 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=10, n_estimators=20, duplicate_K=5, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/forestdiffusion_model.joblib') diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/forest-m4-2217-20260505_061917.csv b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/forest-m4-2217-20260505_061917.csv new file mode 100644 index 0000000000000000000000000000000000000000..db26d53de9bf182692ad5091744898aabcd9b725 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/forest-m4-2217-20260505_061917.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e6714b0b78e847f690be0fb4b2a9b2b5e4bce59ed0abbd7db16aeebea22c70e1 +size 187196 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/forestdiffusion_model.joblib b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..6df11815d9a78efa31cefd879afe97a8fddd435b --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3d447a75b202d17e4efd4b09605fc4f1575c142283d0228a87633c19dfa9cbaa +size 3244997 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/gen_20260505_061917.log b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/gen_20260505_061917.log new file mode 100644 index 0000000000000000000000000000000000000000..1576bc3ec54e015b7159fc946fd9e0a815cc9825 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/gen_20260505_061917.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:483c59b52c74aeae5a432eccc298fc8fc5769576a38edd132f21e7e0778e3de0 +size 294 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/input_snapshot.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5daa6d8c1d862349e305d0a5fcbd26d5361342b8 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/models_fd/model.joblib b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..6df11815d9a78efa31cefd879afe97a8fddd435b --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3d447a75b202d17e4efd4b09605fc4f1575c142283d0228a87633c19dfa9cbaa +size 3244997 diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/public_gate_report.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/staged_input_manifest.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9007b95eb02cc77bc006af6fdd1ecdd015553f56 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/run_config.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..7cc1190b01aa122d99255999c34a6c05f3c2373e --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T06:06:14", + "dataset_id": "m4", + "model": "forestdiffusion", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "forestdiffusion", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/models_fd/model.joblib", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/forest-m4-2217-20260505_061917.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/staged/forestdiffusion/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260505_060614/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "FORESTDIFFUSION_DUPLICATE_K": "5", + "FORESTDIFFUSION_MAX_DEPTH": "4", + "FORESTDIFFUSION_MAX_TRAIN_ROWS": "50000", + "FORESTDIFFUSION_N_ESTIMATORS": "20", + "FORESTDIFFUSION_N_JOBS": "1", + "FORESTDIFFUSION_N_T": "10" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/runtime_result.json b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..1978ddd0adafd9ad5a8532e55ebc3399c55b9514 --- /dev/null +++ b/hyperparameter/m4/forestdiffusion/forest-m4-20260505_060614/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "run_id": "forest-m4-20260505_060614", + 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/dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_002825/tabular_bundle/pipeline_m4" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m4/adapter_efvfm/model_100.pt", + "--num_samples_to_generate", str(int(2217)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_002825/tabbyflow-m4-2217-20260505_003023.csv") diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_train.py b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8c74a2194b73d6dfb20f494bb7f7de1d2e616ff6 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_002825/tabular_bundle/pipeline_m4" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "100" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/gen_20260505_003023.log b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/gen_20260505_003023.log new file mode 100644 index 0000000000000000000000000000000000000000..3aa4f47fb0215250d022dbcc157a4c1eeec39dcc --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/gen_20260505_003023.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ff00cc474b015a53ab657d830e3523511cf89aa66dc64522d232be3ad411379 +size 3570 diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/input_snapshot.json b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a8e6cea3720f9c9515d0f6647d3de14221c42d45 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": 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a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/public_gate/public_gate_report.json b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + 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b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_002825/train_20260505_002825.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6cb417c854a539dea3cf859cb1308733ffbbe89af4218bb64ef63d56cb814a1f +size 62288 diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_gen.py b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..0a5ed2fcf2a49dd28edbd6b08de6fba2b1f3300c --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003100/tabular_bundle/pipeline_m4" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m4/adapter_efvfm/model_200.pt", + "--num_samples_to_generate", str(int(2217)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003100/tabbyflow-m4-2217-20260505_003434.csv") diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_train.py b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7369e4e838bbdaaac506bdcaba17882ed8cee658 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003100/tabular_bundle/pipeline_m4" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "200" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/gen_20260505_003434.log b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/gen_20260505_003434.log new file mode 100644 index 0000000000000000000000000000000000000000..0681dbc10d0151a138c1c0f9a0630a5cc1a5c7e5 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/gen_20260505_003434.log @@ -0,0 +1,3 @@ +version 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b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/train_20260505_003100.log new file mode 100644 index 0000000000000000000000000000000000000000..8e14a1a843cac89f950965d5f0b75c3b927cfb7a --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003100/train_20260505_003100.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:898c3e3a4b1640dbebc34a5ed18fdde07aee68759cd96c0c63be18442e29116e +size 118658 diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_gen.py b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..d98a7b934607c36eb22a776e92df322a392ddfa1 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m4/adapter_efvfm/model_300.pt", + "--num_samples_to_generate", str(int(2217)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabbyflow-m4-2217-20260505_004055.csv") diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6218a0f02f6a9586b6b53dd56df0b58092541475 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "300" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/gen_20260505_004055.log b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/gen_20260505_004055.log new file mode 100644 index 0000000000000000000000000000000000000000..5acce39e217334420d18441f8bb2fd1d2cfb4ffd --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/gen_20260505_004055.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:28b24fc237445e5f99c53a4fd52b4c1d366b66cc7574aae7383cad03c2035040 +size 3570 diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/input_snapshot.json b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a8e6cea3720f9c9515d0f6647d3de14221c42d45 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": 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/dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f1a12f4988cdd82545a68c22fa6d2cc4117c9864 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/staged/public/test.csv", + 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b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..7dd97c79924138f6493c400d2c382e6d72f7aca0 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_004140/tabular_bundle/pipeline_m4" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m4/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(2217)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_004140/tabbyflow-m4-2217-20260505_005022.csv") diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_train.py b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fd64179df4109c651c643ca1e269b10fb088abad --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_004140/tabular_bundle/pipeline_m4" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/gen_20260505_005022.log b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/gen_20260505_005022.log new file mode 100644 index 0000000000000000000000000000000000000000..a6000f9d107b71b160fb0476f2593fb39560d869 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/gen_20260505_005022.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f866bc890d2f98868a950f6cb999cbbd0ad2c49d7d2efca56308e79bd2217521 +size 3571 diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/input_snapshot.json b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/input_snapshot.json new file mode 100644 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b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_004140/train_20260505_004140.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e17a74a6b23db3de74288dd7956ea0bbfa918690b3302d4d700b80be6897bf3 +size 288287 diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_gen.py b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..770507a8abd7485b410f385791c66ad771517811 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m4/adapter_efvfm/model_700.pt", + "--num_samples_to_generate", str(int(2217)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabbyflow-m4-2217-20260505_010225.csv") diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_train.py b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0ccdd159ae1979542da4684eb4dd6c46d57bcbe9 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "700" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/gen_20260505_010225.log b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/gen_20260505_010225.log new file mode 100644 index 0000000000000000000000000000000000000000..902609bac9a81482caad0884657c812ddda54f4a --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/gen_20260505_010225.log @@ -0,0 +1,3 @@ +version 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"PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/public_gate/staged_input_manifest.json new 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sha256:e031e2816aa79e4e5fab4c7fe470741eb2d287e493d74b9cfda68441799e07fa +size 60773 diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4/y_test.npy b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4/y_train.npy b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/tabular_bundle/pipeline_m4/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ 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b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/train_20260505_005055.log new file mode 100644 index 0000000000000000000000000000000000000000..a16ac0c7090e94c2a133c782c6d1ce600145cd14 --- /dev/null +++ b/hyperparameter/m4/tabbyflow/tabbyflow-m4-20260505_005055/train_20260505_005055.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3a020b2a3296dbf9970d8b341fe78f69935468eac9751b2ef398b16ca2a54d5 +size 404145 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/_tabddpm_sample_r0.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..da5962863d154916ec4c38c25279dd76a36b08fd --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 2217 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/config_sample_20260504_182808_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/tabddpm-m4-2217-20260504_182808.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/tabddpm-m4-2217-20260504_182808.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/_tabddpm_train.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b5d6bf71e10654cab9ca3552c8d76447ff3ca745 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/config.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1003b8ba7b51c17b77dd3469708ca3eff0d3256d --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/config_sample_20260504_182808_r0.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/config_sample_20260504_182808_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..54436c9e16db03b86c3ced56e841274fd8d2e5d5 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/config_sample_20260504_182808_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 2217 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/data/X_cat_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2f6b8b23f5768174aab4879e2a30371e9a3d0ead --- /dev/null +++ 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+ "dataset_id": "m4", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + 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null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/staged/tabddpm/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/staged/tabddpm/adapter_transforms_applied.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/staged/tabddpm/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/staged/tabddpm/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/staged/tabddpm/model_input_manifest.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ae1c727c7ab4296dc05ad8846d9e360ab8e83cef --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabddpm", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182738/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/tabddpm-m4-2217-20260504_182808.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/tabddpm-m4-2217-20260504_182808.csv new file mode 100644 index 0000000000000000000000000000000000000000..aeaeaa2a209111bd2dffed109a3a420b29d8f6b4 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/tabddpm-m4-2217-20260504_182808.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e2510f6c3edfc60e8881716835a35c681c33df9b6d98cdb819d5edbfbca3999 +size 102535 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/train_20260504_182738.log b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/train_20260504_182738.log new file mode 100644 index 0000000000000000000000000000000000000000..a8dd10a583a909b9ef233e741a3c12c74f2a0320 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182738/train_20260504_182738.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94ca6f955a055ce3d2f486a8129976e3c364a7360075e8e62767bbd99acce534 +size 3990 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/_tabddpm_sample_r0.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..e040ae031ad981805c4e1a846fc382f4549df904 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 2217 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/config_sample_20260504_182943_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/tabddpm-m4-2217-20260504_182943.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/tabddpm-m4-2217-20260504_182943.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/_tabddpm_train.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7ec1bcd2c5441b9e204fbe8d16e70a1ac6ecd4ef --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/config.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..44e64d78bdd1d2283361e8588ecaf40dfd9bc952 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/config_sample_20260504_182943_r0.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/config_sample_20260504_182943_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..dfafface9f55429eedcb0ad629dea1490723b3ef --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/config_sample_20260504_182943_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 2217 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/X_cat_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2f6b8b23f5768174aab4879e2a30371e9a3d0ead --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c91af7ac26382e392a70536d3dbb592c04ba5e4f495d1e3c364416b9ef704a07 +size 53336 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/X_num_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/info.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fdcf5357ff78dd47dfcca0748443a009323ebfba --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "bmi", + "children", + "sex", + "smoker", + "region", + "charges" + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/y_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/gen_20260504_182943_r0.log b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/gen_20260504_182943_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..d055e84540653e4024cac8bd01e1dd0ace054033 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/gen_20260504_182943_r0.log @@ -0,0 +1,3 @@ +version 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"2026-05-04T18:30:33", + "duration_sec": 49.259 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/staged_features.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/test.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/train.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/val.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/adapter_report.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..cb054430fddc2057418850a966412fa12ed62450 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/adapter_transforms_applied.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/model_input_manifest.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..08ee81bd166a7883e28ab5eda3775dc9b709fb9f --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabddpm", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_182836/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/tabddpm-m4-2217-20260504_182943.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/tabddpm-m4-2217-20260504_182943.csv new file mode 100644 index 0000000000000000000000000000000000000000..d7ab2b8fba9d59359cf0a15e1bb2a09d7879cfb7 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/tabddpm-m4-2217-20260504_182943.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:587d350f963f532d1da118bf30f3db84b96b33ef3b0aa2964451e3619cf42c34 +size 102447 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/train_20260504_182836.log b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/train_20260504_182836.log new file mode 100644 index 0000000000000000000000000000000000000000..5805600d0839b02cb945e9d6c9bcfe022c3e3fbf --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_182836/train_20260504_182836.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:269a870443ea417a64480f444213f892b702d5ef99b015923c86a2250370655b +size 9176 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/_tabddpm_sample_r0.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..cac4233685e536de2ddae2daf487aae2d0c5279d --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 2217 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/config_sample_20260504_183226_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/tabddpm-m4-2217-20260504_183226.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/tabddpm-m4-2217-20260504_183226.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/_tabddpm_train.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..13fe78a81c56f44d844019cdec4d5a6e29034dc4 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/config.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..d12cd0ee556c635e0f9fa68ebafe322040257c34 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 8000 +lr = 0.0005 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/config_sample_20260504_183226_r0.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/config_sample_20260504_183226_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..5df348143f765986ff4aeb11e1aebc772e33f5d8 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/config_sample_20260504_183226_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 8000 +lr = 0.0005 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 2217 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/data/X_cat_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2f6b8b23f5768174aab4879e2a30371e9a3d0ead --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabddpm", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/tabddpm-m4-2217-20260504_183226.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TABDDPM_GPUS": "device=3", + "TABDDPM_NUM_TIMESTEPS": "1000", + "TABDDPM_SAMPLE_BATCH_SIZE": "128", + "TABDDPM_STEPS_PER_EPOCH": "160", + "TABDDPM_TRAIN_BATCH_SIZE": "128", + "TABDDPM_TRAIN_LR": "0.0005" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/runtime_result.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..45854e4ebe52715ba2d84674bf11f482b87a4791 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabddpm", + "run_id": "tabddpm-m4-20260504_183046", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/tabddpm-m4-2217-20260504_183226.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046" + }, + "timings": { + "train": { + "started_at": "2026-05-04T18:30:46", + "ended_at": "2026-05-04T18:32:26", + "duration_sec": 100.304 + }, + "generate": { + "started_at": "2026-05-04T18:32:26", + "ended_at": "2026-05-04T18:33:53", + "duration_sec": 87.148 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/staged_features.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": 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a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/adapter_report.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..191804865aa9b55d71ace35dbb964eb5fd2d7d12 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/adapter_transforms_applied.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/model_input_manifest.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..dda46177fd245567974bbae5d6fc1301ccd545d7 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabddpm", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183046/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/tabddpm-m4-2217-20260504_183226.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/tabddpm-m4-2217-20260504_183226.csv new file mode 100644 index 0000000000000000000000000000000000000000..e0ec0b701d89dbb253ec6b05077dc34165e406e5 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/tabddpm-m4-2217-20260504_183226.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78b7e8ffc1d98e16a96fa551460fabbbbe63e92376d8053fa7ee861cc8fd977a +size 102397 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/train_20260504_183046.log b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/train_20260504_183046.log new file mode 100644 index 0000000000000000000000000000000000000000..99204b1743281e05387789a1eed6e1ed066b375a --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183046/train_20260504_183046.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb48235070f3224f6151f72a60329e0c136a80dceb12c6887440f22ced38866b +size 14370 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/_tabddpm_sample_r0.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..2b5b1f4e7674961a8b4decd569cf078a2fcb28bd --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 2217 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/config_sample_20260504_183600_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/tabddpm-m4-2217-20260504_183600.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/tabddpm-m4-2217-20260504_183600.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/_tabddpm_train.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..42d03b54b85746e1e8feee9a74c5f040ce070010 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/config.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..a47070259d7442c068a8d85160c38f4c3867479b --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 10000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/config_sample_20260504_183600_r0.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/config_sample_20260504_183600_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..090592d97cfe1f2572ba82dd1657071f865ec074 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/config_sample_20260504_183600_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 10000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 2217 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/data/X_cat_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2f6b8b23f5768174aab4879e2a30371e9a3d0ead --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c91af7ac26382e392a70536d3dbb592c04ba5e4f495d1e3c364416b9ef704a07 +size 53336 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/data/X_num_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 2217 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/output/info.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fdcf5357ff78dd47dfcca0748443a009323ebfba --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "bmi", + "children", + "sex", + "smoker", + "region", + "charges" + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/output/loss.csv 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newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/public_gate/public_gate_report.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": 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+ "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/run_config.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..04a8f7607268f2b5c20c5168b93472e8c62bb49c --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T18:34:06", + "dataset_id": "m4", + "model": "tabddpm", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabddpm", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/tabddpm-m4-2217-20260504_183600.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/staged/tabddpm/model_input_manifest.json", + "train_csv": 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b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabddpm", + "run_id": "tabddpm-m4-20260504_183406", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/tabddpm-m4-2217-20260504_183600.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406" + }, + "timings": { + "train": { + "started_at": "2026-05-04T18:34:06", + "ended_at": "2026-05-04T18:36:00", + "duration_sec": 113.875 + }, + "generate": { + "started_at": "2026-05-04T18:36:00", + "ended_at": "2026-05-04T18:37:27", + "duration_sec": 87.915 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/staged_features.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/test.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/train.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/train.csv @@ -0,0 +1,3 @@ +version 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a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/tabddpm/model_input_manifest.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..fd0cfcbe5ab608c10f8ada1dcf18063cef6110b6 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabddpm", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183406/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/tabddpm-m4-2217-20260504_183600.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/tabddpm-m4-2217-20260504_183600.csv new file mode 100644 index 0000000000000000000000000000000000000000..916a56500a96d55e69553678a9fbdd457d0f480e --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/tabddpm-m4-2217-20260504_183600.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2760b055214265d143df01127129d643ef2c1579ff6f9fa7ccde65245ef6a38 +size 102282 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/train_20260504_183406.log b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/train_20260504_183406.log new file mode 100644 index 0000000000000000000000000000000000000000..dde869f1a9e65959e53558d9190cd16dba550563 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183406/train_20260504_183406.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:debd6b2642babb77944591337bf38f4326c5ceb7a4153476fa6b7aba9cc20cab +size 17831 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/_tabddpm_sample_r0.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..10809adc3797cb0a5e3c20fc944eb007411ca43f --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 2217 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/config_sample_20260504_184027_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/tabddpm-m4-2217-20260504_184027.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/tabddpm-m4-2217-20260504_184027.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/_tabddpm_train.py b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..87afbb902a11937b7c80c6cca825c5aa9e633ac9 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/config.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..014e9f57634ed9262b84878748ffc4941b56e143 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 15000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 64 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/config_sample_20260504_184027_r0.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/config_sample_20260504_184027_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..a326a295a755e0ca753b6ec3b23040a85f79af8d --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/config_sample_20260504_184027_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 15000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 64 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 2217 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/data/X_cat_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2f6b8b23f5768174aab4879e2a30371e9a3d0ead --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c91af7ac26382e392a70536d3dbb592c04ba5e4f495d1e3c364416b9ef704a07 +size 53336 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/data/X_num_train.npy 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], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "bmi", + "children", + "sex", + "smoker", + "region", + "charges" + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/data/y_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/gen_20260504_184027_r0.log b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/gen_20260504_184027_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..da9577648e35153883318e80fa564a3531c80286 --- /dev/null +++ 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b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2f3edc6a63ac54192c74dcaa5ab5565b8402d029 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bfc747f038fbe0e633d472522da981c4ae502bd564c9ed379c2c01ddb1acab3b +size 13599 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/X_cat_unnorm.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..5f40751b426e9924bd877827a306cda0ba1ba051 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a6651d8f624e27289fc2c862db8ea8f7223c3db818b545f711715155ee8801d +size 53336 diff --git 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sha256:e498e45565f16e8f424bcd0cfda37ccef33168092cc4215c898121552254c25c +size 53336 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/config.toml b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..a326a295a755e0ca753b6ec3b23040a85f79af8d --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 15000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 64 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 2217 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/info.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fdcf5357ff78dd47dfcca0748443a009323ebfba --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "bmi", + "children", + "sex", + "smoker", + "region", + "charges" + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/loss.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..f6b093142ab814fe348af9b3088f8e1db297cf31 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b895ee0687b6a724c38eef92b313da8df263fe1f320bebd5aa9609e8982a56c +size 4753 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/model.pt b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..e6ab05f6e1286b0c881d18cb5d313ddfda45b47a --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4c7785bb7ae638d55100315233c324f94192a7155e6e916ee81aeecad3f16f4 +size 550673 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/model_ema.pt b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/model_ema.pt new file mode 100644 index 0000000000000000000000000000000000000000..9e60e992903bcc058fce3a61415de267c55c0eb4 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e8acc1768e8dcbf83dfe7f05a6179c7e2c96dbaacb13ba4fa29fca407e879a2 +size 551585 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/y_train.npy b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..73b8d6c3258989047d17afc0bc8c231f615e8ab6 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2ebaf2c1bb96a0e9e9032e11c3367eecbbb27dc5b5ef037fd4406551fcf68b7e +size 17864 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260504_183740/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/tabddpm-m4-2217-20260504_184027.csv b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/tabddpm-m4-2217-20260504_184027.csv new file mode 100644 index 0000000000000000000000000000000000000000..df6b06113626acc81ade807a8a3cc33208752c1a --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/tabddpm-m4-2217-20260504_184027.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ca7be730fb53548efec8cc8671b5fd51c264d48f9c2e695293fd4c34837ddc62 +size 102404 diff --git a/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/train_20260504_183740.log b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/train_20260504_183740.log new file mode 100644 index 0000000000000000000000000000000000000000..a290d096a52bc97a500649cea656b8a62042b0c9 --- /dev/null +++ b/hyperparameter/m4/tabddpm/tabddpm-m4-20260504_183740/train_20260504_183740.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54b12fbf4164fa17c4281eae2ea39296ae097f9f87b313724ab489c50f4b9f8b +size 26495 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/_tabdiff_gen.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..34181a2f9a08255977eab60ddcfec899c081bb9e --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m4/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(2217)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/tabdiff-m4-2217-20260504_195330.csv") diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/_tabdiff_train.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..812c966e6f8db3b40be8e5c6d03b9bf54d8e928f --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "100" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/gen_20260504_195330.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/gen_20260504_195330.log new file mode 100644 index 0000000000000000000000000000000000000000..1e8fe23ed4011a8842e59dae03c8723ffa619dc8 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/gen_20260504_195330.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c53a84cecb3fb61d4b513eeed23fbd5d5f451901e36e52981e1ab6746134890 +size 4557 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/input_snapshot.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..28cd3a4d75d18efdf20f8d2e0b931a2ecdef48e9 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/models_tabdiff/trained.pt b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/models_tabdiff/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc319a55a9c7152a1137f968b6fa1c735125ad11 --- /dev/null +++ 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"status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2689d5b2ca9f04c1956c4946f1b493a93ef11df2 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": 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+ "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + 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"dataset_source_resolved": "new", + "cli_args": { + "model": "tabdiff", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 100, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/models_tabdiff/trained.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/tabdiff-m4-2217-20260504_195330.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/staged/tabdiff/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TABDIFF_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/runtime_result.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..4689399fd35911672197f6622b403d9b468a23d0 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "run_id": "tabdiff-m4-20260504_195148", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/tabdiff-m4-2217-20260504_195330.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195148/models_tabdiff/trained.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T19:51:48", + "ended_at": "2026-05-04T19:53:30", + "duration_sec": 102.364 + }, + "generate": { + "started_at": "2026-05-04T19:53:30", + "ended_at": "2026-05-04T19:53:40", + "duration_sec": 9.583 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/staged/public/staged_features.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": 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/dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/tabular_bundle/pipeline_m4/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/train_20260504_195148.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/train_20260504_195148.log new file mode 100644 index 0000000000000000000000000000000000000000..29c8eb6daddbf5b908ff15a9d3ba56753e0b55fd --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195148/train_20260504_195148.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2eb025c85032cef748eb98c28214a0243fac7a18ac17fc4403178120444f93e4 +size 64000 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/_tabdiff_gen.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..eefebf2b2729435ea174a03cf601e16feb75bd0d --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195352/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m4/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(2217)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195352/tabdiff-m4-2217-20260504_195627.csv") diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/_tabdiff_train.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7a6169b97b8946d8a910da749c9902ec064e1718 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195352/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "200" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/gen_20260504_195627.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/gen_20260504_195627.log new file mode 100644 index 0000000000000000000000000000000000000000..7247b62a5a00222da3e6b91b6bb670b1acf8b36c --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/gen_20260504_195627.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:367003da7e1f414a1622333df41e08c12e50cbcb20d4f8da24cbc6cfcea7bafd +size 4555 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/input_snapshot.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..28cd3a4d75d18efdf20f8d2e0b931a2ecdef48e9 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": 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a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/public_gate/public_gate_report.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + 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index 0000000000000000000000000000000000000000..8a020f765d121be7d8a7e64e4de72d5dfe8457f7 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/staged/tabdiff/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195352/public_gate/staged_input_manifest.json", + "train_csv": 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+ "name": "pipeline_m4", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "test_num": 2217, + "val_num": 2217, + "train_num": 2217, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "bmi", + "children", + "sex", + "smoker", + "region", + "charges" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "2": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "3": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "numerical", + 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--- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/tabular_bundle/pipeline_m4/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/tabular_bundle/pipeline_m4/y_val.npy b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/tabular_bundle/pipeline_m4/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/tabular_bundle/pipeline_m4/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/train_20260504_195352.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/train_20260504_195352.log new file mode 100644 index 0000000000000000000000000000000000000000..ae3a052fe96d7ecf1f4345d6adb35efc65b30da1 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195352/train_20260504_195352.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:25d4897dae32e0ef4ab51ff6bb56308d34ff685bc2771e947040e48202b65901 +size 121024 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/_tabdiff_gen.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..9650c4e7ee69af0d206f41d1301d7aca0e48f1c8 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m4/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(2217)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/tabdiff-m4-2217-20260504_200522.csv") diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/_tabdiff_train.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6569ede081c60a013a52202d4411bbca1d0864db --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/gen_20260504_200522.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/gen_20260504_200522.log new file mode 100644 index 0000000000000000000000000000000000000000..b5d06a0cdafcf7bfd7e028c6a009b5f029ab908c --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/gen_20260504_200522.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:55f4f46b23684fcba9cfc9fdd8b9ead326867328b42191d215eb2fd279ec1671 +size 4715 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/input_snapshot.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..28cd3a4d75d18efdf20f8d2e0b931a2ecdef48e9 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/models_tabdiff/trained.pt b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/models_tabdiff/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc319a55a9c7152a1137f968b6fa1c735125ad11 --- /dev/null +++ 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"unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + 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"status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..26640e508fc879e1c6053afad406c973fd0f2d54 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/run_config.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..0768d40bf5b2e4e296e193157ab4dac215f18291 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/run_config.json @@ -0,0 +1,40 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T19:56:49", + "dataset_id": "m4", + "model": "tabdiff", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabdiff", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 500, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/models_tabdiff/trained.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/tabdiff-m4-2217-20260504_200522.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/staged/tabdiff/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TABDIFF_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/runtime_result.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..50e72f33fa63ca7a5f32fee7724088b8176b8f71 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "run_id": "tabdiff-m4-20260504_195649", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/tabdiff-m4-2217-20260504_200522.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_195649/models_tabdiff/trained.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T19:56:49", + "ended_at": "2026-05-04T20:05:22", + "duration_sec": 512.98 + }, + "generate": { + "started_at": "2026-05-04T20:05:22", + "ended_at": "2026-05-04T20:05:32", + "duration_sec": 9.324 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/staged_features.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_195649/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": 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0000000000000000000000000000000000000000..ed1909463c84a9ff6b59e57d3185aa988bb03979 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m4/adapter_learnable/model_800.pt", + "--num_samples_to_generate", str(int(2217)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/tabdiff-m4-2217-20260504_201541.csv") diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/_tabdiff_train.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8041e2fe642dbbfb4df7b7688d4c1c42d56f930d --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "800" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/gen_20260504_201541.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/gen_20260504_201541.log new file mode 100644 index 0000000000000000000000000000000000000000..2f670aeb99dc23d501ea56ff74df148ad5768648 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/gen_20260504_201541.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe41ce38a5e1f9983f47f8f1e33e478f932d264085ffc6ecc6a65a2e3c9d3578 +size 6893 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/input_snapshot.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..28cd3a4d75d18efdf20f8d2e0b931a2ecdef48e9 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": 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a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/public_gate/public_gate_report.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + 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index 0000000000000000000000000000000000000000..f9c50148c83e8c5629e3c273164efb330ba9c9ca --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/staged/tabdiff/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_200544/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/tabdiff-m4-2217-20260504_201541.csv 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+ "name": "pipeline_m4", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "test_num": 2217, + "val_num": 2217, + "train_num": 2217, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "bmi", + "children", + "sex", + "smoker", + "region", + "charges" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "2": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "3": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "numerical", + "computer_representation": "Float" + } + } + }, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "inverse_idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "idx_name_mapping": { + "0": "age", + "1": "bmi", + "2": "children", + "3": "sex", + "4": "smoker", + "5": "region", + "6": "charges" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4/real.csv b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..85ff7f4464fa73c1d58767739e7b259f9371a55b --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e031e2816aa79e4e5fab4c7fe470741eb2d287e493d74b9cfda68441799e07fa +size 60773 diff --git 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--- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4/y_val.npy b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/tabular_bundle/pipeline_m4/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/train_20260504_200544.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/train_20260504_200544.log new file mode 100644 index 0000000000000000000000000000000000000000..8a767e6fb681de6ac5764cdeb4ec44ce1d36ad10 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_200544/train_20260504_200544.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:078c01e8a0435d455a40b8861e45cf4d3985d3394f911ac66ca128eaf244ba00 +size 465876 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/_tabdiff_gen.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..28a66a04060caa38fb9dc0c2522206fa05d83deb --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m4/adapter_learnable/model_800.pt", + "--num_samples_to_generate", str(int(2217)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/tabdiff-m4-2217-20260504_203038.csv") diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/_tabdiff_train.py b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e89c9d5cae2d0d0a3ffad675ad4d3d6793a5bb4e --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "1000" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/gen_20260504_203038.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/gen_20260504_203038.log new file mode 100644 index 0000000000000000000000000000000000000000..33561038399d96140e26bbc6cfdc58e27a42152e --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/gen_20260504_203038.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e8ef2ac385ef21af82d3d19043b965c593520bcd5860d0c78eb4f0aadabfe92b +size 6653 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/input_snapshot.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..28cd3a4d75d18efdf20f8d2e0b931a2ecdef48e9 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/models_tabdiff/trained.pt b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/models_tabdiff/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc319a55a9c7152a1137f968b6fa1c735125ad11 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/models_tabdiff/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:306660b8aad4549267c68390b017e15ddb9bc06a02c80515eb72a97ae31a81eb +size 74 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/public_gate_report.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bd083900940a4bc1dd73c07143285d713cb3062d --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + 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"dataset_source_resolved": "new", + "cli_args": { + "model": "tabdiff", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": 1000, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/models_tabdiff/trained.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/tabdiff-m4-2217-20260504_203038.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/staged/tabdiff/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TABDIFF_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/runtime_result.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..b7f344194287eeeae4ec09486576da18742f35e9 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "run_id": "tabdiff-m4-20260504_201603", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/tabdiff-m4-2217-20260504_203038.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260504_201603/models_tabdiff/trained.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T20:16:03", + "ended_at": "2026-05-04T20:30:38", + "duration_sec": 874.805 + }, + "generate": { + "started_at": "2026-05-04T20:30:38", + "ended_at": "2026-05-04T20:30:53", + "duration_sec": 15.445 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/staged_features.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": 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b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/staged/tabdiff/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/staged/tabdiff/model_input_manifest.json b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/staged/tabdiff/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..63eb55125ced70325f757776bc878fd22bf02763 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/staged/tabdiff/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + 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/dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/tabular_bundle/pipeline_m4/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/train_20260504_201603.log b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/train_20260504_201603.log new file mode 100644 index 0000000000000000000000000000000000000000..e40b175a5490f4b94d292ca2f2546606784105e1 --- /dev/null +++ b/hyperparameter/m4/tabdiff/tabdiff-m4-20260504_201603/train_20260504_201603.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57a69d2c09c1c566bc693048eea1434b7a7e285051c436bd2348e14524718b0d +size 584154 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/_tabpfgen_generate.py b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d977c8cd6fa00b8209a876eef79c658b9c2d0a05 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/train.csv") +target_col = "charges" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv") diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/gen_20260505_030045.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/gen_20260505_030045.log new file mode 100644 index 0000000000000000000000000000000000000000..ef322b704a54bc27c5786dcd4a0c591448cf999c --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/gen_20260505_030045.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ef77b5880a636abd915bbe84664112d39942074f1573086a9915d465bb0124c +size 7438 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/input_snapshot.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e4ec9d0bc32394ab638c94bcf784b81478f2f39 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/public_gate_report.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ad15ab5c6258eb896ecdab89ef92bfa625af5fcc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/run_config.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..a769fe528440321f223090e4286300236ab864f6 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T03:00:45", + "dataset_id": "m4", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "512", + "TABPFGEN_GEN_CHUNK_ROWS": "64" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/runtime_result.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..35f3a9c173a282893b2aed2a544a619ae862b31f --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "run_id": "tabpfgen-m4-20260505_030045", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045" + }, + "timings": { + "train": { + "started_at": "2026-05-05T03:00:45", + "ended_at": "2026-05-05T03:00:45", + "duration_sec": 0.006 + }, + "generate": { + "started_at": "2026-05-05T03:00:45", + "ended_at": "2026-05-05T03:02:12", + "duration_sec": 86.543 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/staged_features.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/test.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/train.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ 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0000000000000000000000000000000000000000..1ad1fca86764972a29bff5248b5f1a760b158459 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/adapter_transforms_applied.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/model_input_manifest.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7823902e750e1cd4e7d76fb812a6e47dc0d2191d --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv new file mode 100644 index 0000000000000000000000000000000000000000..8a84cbca0d525984eab670a0d4dfb4937a09056e --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen-m4-2217-20260505_030045.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06ee24856ee7900cbc066749783deffbad62040b9ee4c5e3e6a5bb67b2ecbc0e +size 151356 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen_meta.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..28087051be0aa25f61613f4d3a0037395b12ac84 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030045/staged/public/staged_features.json", + "target_col": "charges", + "is_classification": false, + "task_type": "regression", + "n_rows": 2217, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/train_20260505_030045.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/train_20260505_030045.log new file mode 100644 index 0000000000000000000000000000000000000000..3f12688f516f759bd8b6584e3c114ea9e59febd9 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030045/train_20260505_030045.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ed6d0531de8f1146b0eb8ed6451f9bca5bb00c00712f7dae30e0d0d9800b118 +size 595 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/_tabpfgen_generate.py b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..3d2a00c619697d6910fb41674f66d7552108840c --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/train.csv") +target_col = "charges" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv") diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/gen_20260505_030225.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/gen_20260505_030225.log new file mode 100644 index 0000000000000000000000000000000000000000..0e5c7ca54079b38f3c50b055f614a8950b22c598 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/gen_20260505_030225.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b6580001583c1c0704b41554e91bc208af2e855556277b6d5542f51d26cd20c2 +size 7438 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/input_snapshot.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e4ec9d0bc32394ab638c94bcf784b81478f2f39 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/public_gate_report.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..e867cdb813df7ce34ab7084a15c90504e2110c9a --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/staged_features.json", + "public_gate_report": 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/run_config.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f44f7a87252cce199b1a35f15f963ecf38844217 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T03:02:25", + "dataset_id": "m4", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "1024", + "TABPFGEN_GEN_CHUNK_ROWS": "64" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/runtime_result.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..4b2340e7b27831795726f0d2a291cc990f27d72d --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "run_id": "tabpfgen-m4-20260505_030225", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225" + }, + "timings": { + "train": { + "started_at": "2026-05-05T03:02:25", + "ended_at": "2026-05-05T03:02:25", + "duration_sec": 0.007 + }, + "generate": { + "started_at": "2026-05-05T03:02:25", + "ended_at": "2026-05-05T03:03:50", + "duration_sec": 84.974 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/staged_features.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + 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"feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv new file mode 100644 index 0000000000000000000000000000000000000000..27169a4266fa8c6d9971afc15c4a9f6f100c1b5b --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen-m4-2217-20260505_030225.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4c0c13ac1cbf18b17e0667a942c83ea71fde2c166ef1e918bb89ba0fd4ce2df +size 151485 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen_meta.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..9fa876bf63685ed749c8891959f3b88373ae02ee --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030225/staged/public/staged_features.json", + "target_col": "charges", + "is_classification": false, + "task_type": "regression", + "n_rows": 2217, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/train_20260505_030225.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/train_20260505_030225.log new file mode 100644 index 0000000000000000000000000000000000000000..c7e45383d6da258cf12355c175b327638c1418eb --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030225/train_20260505_030225.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0e080b1b30975dc81ec4667ba1a26d54aa1e5166e8667083ad70b642286aad4 +size 595 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/_tabpfgen_generate.py b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ce27a9003d44421bc4724f5c380b2fe3fe5fa51f --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv") +target_col = "charges" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv") diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/gen_20260505_030403.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/gen_20260505_030403.log new file mode 100644 index 0000000000000000000000000000000000000000..8a99c95e77b5ad36a2a8505e9052a76a74cad371 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/gen_20260505_030403.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14041ed1dbffad8712f876083db1ad8631d5ab7fdc27a4074407bbc2c6508992 +size 4318 diff --git 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b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + 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+++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a6c44e22b1b80d2349123063686c65e7774de4c6 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + 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0000000000000000000000000000000000000000..66f4e03eaabac996bb15811c0524681b6d894c0e --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T03:04:03", + "dataset_id": "m4", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "2048", + "TABPFGEN_GEN_CHUNK_ROWS": "128" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/runtime_result.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..bd2d99c6ee06494ba64e58ac19511db30cfd03f9 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "run_id": "tabpfgen-m4-20260505_030403", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403" + }, + "timings": { + "train": { + "started_at": "2026-05-05T03:04:03", + "ended_at": "2026-05-05T03:04:03", + "duration_sec": 0.006 + }, + "generate": { + "started_at": "2026-05-05T03:04:03", + "ended_at": "2026-05-05T03:04:54", + "duration_sec": 51.333 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/staged_features.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/test.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/val.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/adapter_report.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..a9e055c0a17140f08e4fcc3ec44bd5241a632eb9 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/adapter_transforms_applied.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/model_input_manifest.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cb470149ef44eb01deaa99ecbea008d94872f0cc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv new file mode 100644 index 0000000000000000000000000000000000000000..3f0bda0329c0dd4d3ce7a033d21cc5a233de14b7 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen-m4-2217-20260505_030403.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a93a30366d886d4d5e1daebcc998a215f76054116d0609ceb3f138611385a2e +size 151426 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen_meta.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..0246edf8d7c82a8ff61b78c1f53a65d3ae041bad --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030403/staged/public/staged_features.json", + "target_col": "charges", + "is_classification": false, + "task_type": "regression", + "n_rows": 2217, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/train_20260505_030403.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/train_20260505_030403.log new file mode 100644 index 0000000000000000000000000000000000000000..479b10a82d028513deba8e1534774db861c3f466 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030403/train_20260505_030403.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce94face8378a92275ddc373d0661a1e7dcdc8224fe79a8c63183fc5e1eb8baf +size 595 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/_tabpfgen_generate.py b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d95448517ea1b87bb3e6cb50f664cad861a28ec --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030507/staged/public/train.csv") +target_col = "charges" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030507/tabpfgen-m4-2217-20260505_030507.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030507/tabpfgen-m4-2217-20260505_030507.csv") diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/gen_20260505_030507.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/gen_20260505_030507.log new file mode 100644 index 0000000000000000000000000000000000000000..e1111b7e02bbebeac34903ebd4c07fb40ed7e0f0 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/gen_20260505_030507.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11b5bd6221459a2f8271de20e576c341877f1aa773254c1af662ead62d34f067 +size 4318 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/input_snapshot.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e4ec9d0bc32394ab638c94bcf784b81478f2f39 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", 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0000000000000000000000000000000000000000..0fbeb0529f1bc6e9b22cb64123bf0f5b704d423e --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030507/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030507/staged/public/staged_features.json", + "target_col": "charges", + "is_classification": false, + "task_type": "regression", + "n_rows": 2217, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/train_20260505_030507.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/train_20260505_030507.log new file mode 100644 index 0000000000000000000000000000000000000000..2df481b0f139758ac3fcf492c85cff1e2969c6b3 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030507/train_20260505_030507.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0abd3df54883ee83344917f73cc7c77b976ea8a9fe4bbcab78bc0be4cf3e9d9 +size 595 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/_tabpfgen_generate.py b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..172e70f44599e4f4fc9a3bb8a0479bb23ff33237 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv") +target_col = "charges" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(2217) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_regression(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv") diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/gen_20260505_030613.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/gen_20260505_030613.log new file mode 100644 index 0000000000000000000000000000000000000000..0c66486eeff0b894593c8f5850e837b6d8bb807e --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/gen_20260505_030613.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6daf4f42db7dae3abc897663fde9b3764edff1c4d29e66c34e7ed39c7cee2244 +size 2649 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/input_snapshot.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e4ec9d0bc32394ab638c94bcf784b81478f2f39 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/public_gate_report.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/staged_input_manifest.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..371355ef62d2439cb4ea15fed87fb32751403653 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/run_config.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..4bd022257285cefd3ee41056984698faf94d60a2 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T03:06:13", + "dataset_id": "m4", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "4096", + "TABPFGEN_GEN_CHUNK_ROWS": "256" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/runtime_result.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..4a0897904effeeb83721710226093d38ba28ea23 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "run_id": "tabpfgen-m4-20260505_030613", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613" + }, + "timings": { + "train": { + "started_at": "2026-05-05T03:06:13", + "ended_at": "2026-05-05T03:06:13", + "duration_sec": 0.021 + }, + "generate": { + "started_at": "2026-05-05T03:06:13", + "ended_at": "2026-05-05T03:06:49", + "duration_sec": 36.791 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/staged_features.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/test.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/val.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/adapter_report.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..5d9a26ae90021abdc0cfebb7eafcbfa9b8de227a --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/adapter_transforms_applied.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/model_input_manifest.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4f844cd87e830f19e198f3d85cdd01853d748263 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv new file mode 100644 index 0000000000000000000000000000000000000000..1d66500db9d42df89e23364ef05300a1821eeef8 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen-m4-2217-20260505_030613.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2fc9c407f88eab84fad91349a1d1d5ed21bffbe1c1e2925d94c9c3c9fa65762e +size 151325 diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen_meta.json b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..e38df456175aa38b17f24a9b8e3343ec1d291d23 --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260505_030613/staged/public/staged_features.json", + "target_col": "charges", + "is_classification": false, + "task_type": "regression", + "n_rows": 2217, + "n_cols": 7 +} \ No newline at end of file diff --git a/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/train_20260505_030613.log b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/train_20260505_030613.log new file mode 100644 index 0000000000000000000000000000000000000000..a8ad9eebf29c42b01eed819dd01524cf420336ec --- /dev/null +++ b/hyperparameter/m4/tabpfgen/tabpfgen-m4-20260505_030613/train_20260505_030613.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14aea4672cf7c13dc2a5a28db1da48549b8cfba2404a3e71c4f829e2884de765 +size 595 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/_tabsyn_sample.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..cb869c2a3783fa7e7a4201e027a17f50964c32b5 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359" +dataname = "tabsyn_m4" +output_csv = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359/tabsyn-m4-2217-20260505_093640.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 2217 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/_tabsyn_train.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e1a899354189cd9d2db5ec9cb1cff89a6881ad28 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359" +dataname = "tabsyn_m4" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_cat_test.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_cat_train.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_num_test.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/data/tabsyn_m4/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git 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a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/test.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/train.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/val.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/adapter_report.json b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..42869da0358400da628815432b17f17ec190594d --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/adapter_transforms_applied.json b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/model_input_manifest.json b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..283df4bae83f66545dfe012b3710aeda296babb3 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabsyn", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_082359/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/synthetic/tabsyn_m4/real.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/synthetic/tabsyn_m4/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/synthetic/tabsyn_m4/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/synthetic/tabsyn_m4/test.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/synthetic/tabsyn_m4/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/synthetic/tabsyn_m4/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/tabsyn-m4-2217-20260505_093640.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/tabsyn-m4-2217-20260505_093640.csv new file mode 100644 index 0000000000000000000000000000000000000000..854ff5efe78943a4b8c2516f7df1d797e9af1f81 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/tabsyn-m4-2217-20260505_093640.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:292b0e32b46c48502eb96fdbdb958f17a727ab376e054e27d2df5b36f347f0f4 +size 83400 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/train_20260505_082359.log b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/train_20260505_082359.log new file mode 100644 index 0000000000000000000000000000000000000000..8ebfbaf8e4c0d2f750612ba9db5b31c023dfed70 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_082359/train_20260505_082359.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c8c880efe4a8fd4241f4b8d706056c471e4f2909a1f9445b8e00463c06a6767 +size 4086914 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/_tabsyn_sample.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..c56b2e52759e2abbe70ee8f6e4b33d07f13e2973 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735" +dataname = "tabsyn_m4_tabsyn_m4_20260505_182735" +output_csv = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735/tabsyn-m4-2217-20260505_182747.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 2217 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/_tabsyn_train.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e50dfe87b466ce7cecdd9e8755988b6b7932c421 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735" +dataname = "tabsyn_m4_tabsyn_m4_20260505_182735" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_cat_test.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_cat_train.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_num_test.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/data/tabsyn_m4_tabsyn_m4_20260505_182735/X_num_train.npy 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false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/test.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/train.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/val.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/adapter_report.json b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..43aa23f454356ff9c10937125b039d45869e2da9 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/adapter_transforms_applied.json b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/model_input_manifest.json b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f2542e13b153d951584f80f6ac4cf528883ff64a --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabsyn", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182735/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/synthetic/tabsyn_m4_tabsyn_m4_20260505_182735/real.csv b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/synthetic/tabsyn_m4_tabsyn_m4_20260505_182735/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/synthetic/tabsyn_m4_tabsyn_m4_20260505_182735/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/synthetic/tabsyn_m4_tabsyn_m4_20260505_182735/test.csv 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0000000000000000000000000000000000000000..4a8aa7a55b84d9dfbc61c4b7f436efc800dd0fc3 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/tabsyn_ckpt/vae/tabsyn_m4_tabsyn_m4_20260505_182735/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37057b1ef69b970040058bb64ded4c8df2677e4a973fc193352bc0a55f88048c +size 63903 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/tabsyn_ckpt/vae/tabsyn_m4_tabsyn_m4_20260505_182735/train_z.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/tabsyn_ckpt/vae/tabsyn_m4_tabsyn_m4_20260505_182735/train_z.npy new file mode 100644 index 0000000000000000000000000000000000000000..b9d1edf30601b9683768e107aa1cf2f60b067a29 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/tabsyn_ckpt/vae/tabsyn_m4_tabsyn_m4_20260505_182735/train_z.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea8ce6a56a22ecca5de32d8a49cf65af34a43f98cdc220bc0c8225dbf6ee6357 +size 283904 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/train_20260505_182735.log b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/train_20260505_182735.log new file mode 100644 index 0000000000000000000000000000000000000000..441decb0666a0e748719f8893258fed6becaf184 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182735/train_20260505_182735.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c26f6b547d256b10948ab1cf429b9ac2f1e56fedaa279e88f0ccab233453919 +size 5633 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/_tabsyn_sample.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..35b600800c7cd46e0f5092fadf6fc4ed744e366b --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182804" +dataname = "tabsyn_m4_tabsyn_m4_20260505_182804" +output_csv = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182804/tabsyn-m4-2217-20260505_182816.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 2217 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/_tabsyn_train.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..046025dddcf25370cecf83b99654cbb54f811c88 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182804" +dataname = "tabsyn_m4_tabsyn_m4_20260505_182804" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/data/tabsyn_m4_tabsyn_m4_20260505_182804/X_cat_test.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/data/tabsyn_m4_tabsyn_m4_20260505_182804/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/data/tabsyn_m4_tabsyn_m4_20260505_182804/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git 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b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/train_20260505_182804.log new file mode 100644 index 0000000000000000000000000000000000000000..cb80bb94075b1d04eec2e6073166e35757156760 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182804/train_20260505_182804.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d34bae85176347ce00119f0fad709584e0b7e7d59f6de88114cb270c3901aa39 +size 7626 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/_tabsyn_sample.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..f2fb14a08e45e6cc9b3ac34c6ad30186b485095b --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182833" +dataname = "tabsyn_m4_tabsyn_m4_20260505_182833" +output_csv = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182833/tabsyn-m4-2217-20260505_182846.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 2217 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/_tabsyn_train.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c3b8331fe2c35b39c458ffc175056fc9cbc5e6c8 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182833" +dataname = "tabsyn_m4_tabsyn_m4_20260505_182833" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/data/tabsyn_m4_tabsyn_m4_20260505_182833/X_cat_test.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/data/tabsyn_m4_tabsyn_m4_20260505_182833/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/data/tabsyn_m4_tabsyn_m4_20260505_182833/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/data/tabsyn_m4_tabsyn_m4_20260505_182833/X_cat_train.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182833/data/tabsyn_m4_tabsyn_m4_20260505_182833/X_cat_train.npy new 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0000000000000000000000000000000000000000..0bc6e87f45586e26ad0e763acbf5cb34a6143cb1 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182903/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182903" +dataname = "tabsyn_m4_tabsyn_m4_20260505_182903" +output_csv = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182903/tabsyn-m4-2217-20260505_182916.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 2217 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182903/_tabsyn_train.py b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182903/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ba4d48e60fe5a6532037587876cba19863ea149d --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182903/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_182903" +dataname = "tabsyn_m4_tabsyn_m4_20260505_182903" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182903/data/tabsyn_m4_tabsyn_m4_20260505_182903/X_cat_test.npy b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182903/data/tabsyn_m4_tabsyn_m4_20260505_182903/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ 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b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182903/train_20260505_182903.log new file mode 100644 index 0000000000000000000000000000000000000000..d1e8f38884a1382c72b2a985dc83b7bf97510f98 --- /dev/null +++ b/hyperparameter/m4/tabsyn/tabsyn-m4-20260505_182903/train_20260505_182903.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a85239f52cd105b4793b8aceac8b312dfbd7fdf6a33082be8f851b576a2eeba +size 11675 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..64d07e128ef6dbb294fc5e3083f11bfa36692685 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d28215e962c43113cd7268680d82572ec243c015 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_172924/gen_20260504_172948.log b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/gen_20260504_172948.log new file mode 100644 index 0000000000000000000000000000000000000000..9cb264d80125eb9865bcf1ff211a5f47e5408296 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/gen_20260504_172948.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21074009efaaf889eeada81da04fe08875f77e84e1c9ad6863c7e7801d6f0f96 +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_172924/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git 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0000000000000000000000000000000000000000..a3d27569151d13175f6106f0b9ec16a29ecd454b --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc71fa06a7efd6b7984554097fbac50bc8364689311279a95ec83b921ea69076 +size 134967 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..81b1295a580f8a6f079ccfcc3c40e66fb290a4a7 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1aa090f428d3524915512adaba16283538755528 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/gen_20260504_174327.log b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/gen_20260504_174327.log new file mode 100644 index 0000000000000000000000000000000000000000..60c093f4fc68d51d4270c47f02ced7614c438a31 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/gen_20260504_174327.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af461c511bf86e5d1de31bd40d8796e533698a811681c73ac90e6480905b46d5 +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": 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b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + 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a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..66a493d45188588761c51eab52795cfad070bf87 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/test.csv", + "features_json": 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"target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "64,64", + "TVAE_DECOMPRESS_DIMS": "64,64", + "TVAE_EMBEDDING_DIM": "32", + "TVAE_EPOCHS": "100" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/runtime_result.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..cb553f8c827cd5d12fe1aa3ab0e87b8651244569 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_174302", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": 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+1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_report.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..5a09a4742de3a8b219cf8df46bd0846a8ec726d2 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_transforms_applied.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..068bb0b9d0b426739333b63a6ec98858fc30bda9 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv new file mode 100644 index 0000000000000000000000000000000000000000..cc89d367bbc77fcaa87035697f57afb63c3dd0f5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:18426a62b60277585bdaec0b71059b5102ff55a45beba0836da8fbf8759b3a39 +size 135340 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5752f6a3066cdfc5f9df8ba23e3090b04584f4d8 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..aca9f4cee652b8d957c0618750c8f79706afa308 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174345/gen_20260504_174408.log b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/gen_20260504_174408.log new file mode 100644 index 0000000000000000000000000000000000000000..8fc5e6292cb4d277e08212280353a2066877709f --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/gen_20260504_174408.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a4497f79ab8d0c7ce135c4864c2a53a2e7353da7444daaf7b0e9dde72e02f5e +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174345/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv new file mode 100644 index 0000000000000000000000000000000000000000..57b7a745bd77beb6a86f026fe8306bda5886a559 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:083f6155edb8f495beff37d43146b53d55fbeb8eedb579e53a6ff1db820317d5 +size 135606 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..986470909887e64074ae7b5468923539ce59c61e --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..51849963b3e68038221e8e94145da119f0101e40 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log new file mode 100644 index 0000000000000000000000000000000000000000..bf3bec0ffb0b16650e96487ab6aef4b913e7cf21 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a129533a2ad4f0771f09c7c1113903202b4304c36ffa3260f021cba649543cbe +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + 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b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + 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b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/public_gate/staged_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cd39515addd348f9fcb2b4c8ba4ef09d43238433 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/test.csv", + "features_json": 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0000000000000000000000000000000000000000..20792d803cbf316b3142e9ed6f42bc28fa626586 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:44:26", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "500", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "300" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/runtime_result.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7b2e9d42f8215da9a9728e78bb081221cbec76dd --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_174426", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": 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@@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/public/test.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/public/test.csv @@ -0,0 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@@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_report.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..d5a01ea2a5b209d961dfbffde36d77b4193c82e5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_transforms_applied.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/model_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b155a31733b6d5835127d8d34920b3e34bf262ef --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv new file mode 100644 index 0000000000000000000000000000000000000000..224d4c92d849978c5d816faa4fda791406550bce --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bcf9234a7900b6ed86dbc6178935c16a0f4ba186b337d1c84cff04c15a40e1f4 +size 135512 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b70a0b4d228b99bd6d3e71c9b31701fd7efe7eea --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..72d435e2a4019187fa552c7ef6488a04c198cb74 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/gen_20260504_175349.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/gen_20260504_175349.log new file mode 100644 index 0000000000000000000000000000000000000000..d80b9a01a901102579202b4593edb4c4ee21e77f --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/gen_20260504_175349.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:198de309a988fdec10f4dd85fbbc1f0f354ac22664545de9e229b6a40353e304 +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/models_300epochs/train_20260504_175331.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/models_300epochs/train_20260504_175331.log new file mode 100644 index 0000000000000000000000000000000000000000..84b1f25f76db42e58f5013b6169e84d8a08f70ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/models_300epochs/train_20260504_175331.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef9355f3042f9be1f1e24f023ec141d00b5472f85f7b583ac3988e3bd9ecf510 +size 614 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e229cbd8af4127f12660e01a5dc741635c564f0e --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f00efa1443d564da74605e9f2b5702368cf97a6e2495daf901b29414f34e9ecf +size 264748 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/staged_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1dde719e8651a2477d097a3c5f1c1d7f39230457 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/run_config.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..71797307e0a97f2af74b7c2cf967ace2547d9bad --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:53:31", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "64,64", + "TVAE_DECOMPRESS_DIMS": "64,64", + "TVAE_EMBEDDING_DIM": "32", + "TVAE_EPOCHS": "100" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/runtime_result.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..8d9fff1d4d3a73f0ab5c57ec96df348e154dbb2f --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175331", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:53:31", + "ended_at": "2026-05-04T17:53:49", + "duration_sec": 18.291 + }, + "generate": { + "started_at": "2026-05-04T17:53:49", + "ended_at": "2026-05-04T17:53:54", + "duration_sec": 4.931 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv @@ -0,0 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b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_transforms_applied.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a10b01ee4c8705e73edbac3554347b0446b4fbd4 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv new file mode 100644 index 0000000000000000000000000000000000000000..b4180a57b42d16ad5884478676d597804f2a2abc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3901b669115b3a46120c956bc92e368c0b0ebe459743844f643091b7d2c6b1c +size 135479 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ce22b690c0cf1d6a0c43dff49a21a99b4a9955a0 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d145109f933ae3ccfbc8e5969da96387a97fcd43 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/gen_20260504_175431.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/gen_20260504_175431.log new file mode 100644 index 0000000000000000000000000000000000000000..7a2bdd2a830702ca913ec67da6b9cb26556e1acc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/gen_20260504_175431.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2d559e320b3bee74da788cc285a06ae8720e75230a0db1eb51ca71e53784f832 +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/models_300epochs/train_20260504_175407.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/models_300epochs/train_20260504_175407.log new file mode 100644 index 0000000000000000000000000000000000000000..bf83b297fb071d15ff04372380dabc4196db60fb --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/models_300epochs/train_20260504_175407.log @@ -0,0 +1,3 @@ +version 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b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/public_gate/public_gate_report.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/public_gate/staged_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..eb0801ec96685c9a9fbae813d6242ec57c5340d2 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/test.csv", + "features_json": 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0000000000000000000000000000000000000000..85e0fb54421528c038fb188663a3ffb8c0a503c0 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:54:07", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/models_300epochs/tvae_300epochs.pt", + "output_csv": 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"target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "128,128", + "TVAE_DECOMPRESS_DIMS": "128,128", + "TVAE_EMBEDDING_DIM": "64", + "TVAE_EPOCHS": "200" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/runtime_result.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..8155ff06da6f1b94c81f9bec4b973b83ff1680c5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175407", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": 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b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/staged/tvae/model_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..77943e13a9d58931e45134d53195514fa45fbec9 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv new file mode 100644 index 0000000000000000000000000000000000000000..6782c785f5ba68b4490045ef62ffb08322d5d0f1 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d1beaf0f631c81739f92d94d2b563d598045876727c2857bc5d562accd79a344 +size 135440 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8ce529c5975d8135d8bc87e2d902ce1cd0a5fab7 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0ddb2b7321a12d3aff39be0c0f3c4c23e022d041 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/gen_20260504_175513.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/gen_20260504_175513.log new file mode 100644 index 0000000000000000000000000000000000000000..a9e912bdc4026a088ea34ccdeb07d10973cd5245 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/gen_20260504_175513.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:506bc0358a88c82bed1b729b7efcaccb73aea52807b692c21a5262b6c5c6854c +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/models_300epochs/train_20260504_175449.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/models_300epochs/train_20260504_175449.log new file mode 100644 index 0000000000000000000000000000000000000000..68c1a34e689a2d713886f2cc826e6c9f60bbf379 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/models_300epochs/train_20260504_175449.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e03fc03009b999dc1131d532847efc117ab94d363f9730f715066728ae278fb +size 620 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..09fa703d767213aaac45b0e81a2180a81d6e5db5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version 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"PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6c44a4622897b6a80eac1f92d52e00343f0eacc6 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": 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"generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "500", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "300" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/runtime_result.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..c3792a3ec28b2df18468254616f76eed6e51ce7f --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175449", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:54:49", + "ended_at": "2026-05-04T17:55:13", + "duration_sec": 23.522 + }, + "generate": { + "started_at": "2026-05-04T17:55:13", + "ended_at": 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false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv @@ -0,0 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b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_transforms_applied.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c3c9d78fcce98d02acf18fdedf44445fe601e6b1 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv new file mode 100644 index 0000000000000000000000000000000000000000..0f5c77368d7ce704a1accc5fa2633d69ea7b94dd --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e98a12bdd925dfd2cb11667b7709d30496bb352aa2a492439db83409ec7f05da +size 135526 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5630436cb207679ca36a2faa95aed68d8b90e70c --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8bddc1e2fca3b1f61308cc45e156164c030dd8b2 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/gen_20260504_175610.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/gen_20260504_175610.log new file mode 100644 index 0000000000000000000000000000000000000000..4b1bb491e22a5f18459632adf927b43ede912e80 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/gen_20260504_175610.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5cf0eef736ccb09b775ee91e91bf1cecff4d38769b05d4bf2e40228f12f99156 +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/models_300epochs/train_20260504_175531.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/models_300epochs/train_20260504_175531.log new file mode 100644 index 0000000000000000000000000000000000000000..ab63c7540b64e9f038a5e7f537de8ffa11294c20 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/models_300epochs/train_20260504_175531.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eee2f0fc9338e7e8903f28b8c9484dae9bd01ffaaf28d9ca7d689b117e9646d2 +size 620 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..2f0cecc3a52a25f09009f0d041d9abed7a4eda90 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:312b6cfa7a04181e1c825c875e767675a86c53ef0f51a073830ebdeafb82b715 +size 765292 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/public_gate/normalized_schema_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + 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b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1ccf316d39cda13b861175fe16fcd94babb6fe6a --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/test.csv", + "features_json": 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+ }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/run_config.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/run_config.json new file mode 100644 index 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/runtime_result.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..65b42c7cb5ae1dc51773c686c000d66f14213373 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175530", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": 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+1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/public/val.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_report.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..37f886a4c807fe8b69e99c1104f0e36d6c6adf2e --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_transforms_applied.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cb351c79f41d5f0ba4ad75267366eb75b2b99a89 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv new file mode 100644 index 0000000000000000000000000000000000000000..606a7d02f0adf19904c4d4ba33d3a4dfea5ca992 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2fb289413b48175dafe06bf13265b44045456603a5d208e7fe589e2889bdb54b +size 135518 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..39004fb75945aac1f990221a91985deb650fa5a0 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..024f990670dcbc746aaa55122c04dee9d8ff3a9e --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/gen_20260504_175710.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/gen_20260504_175710.log new file mode 100644 index 0000000000000000000000000000000000000000..a795a8030882a84a26d73a4c573aaeb8f8e885c9 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/gen_20260504_175710.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d622877b81551b0ad684e4a5085c2911727d027af9a46fc420364324caa62b56 +size 404 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/input_snapshot.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": 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a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/models_300epochs/train_20260504_175629.log b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/models_300epochs/train_20260504_175629.log new file mode 100644 index 0000000000000000000000000000000000000000..27900ca9d641e9523358a5597637ab7644edf35b --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/models_300epochs/train_20260504_175629.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:320957d0779658cd00032ad2ad674c5da8cf66cf3de6c185b2919b669f0d319e +size 620 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..3cc976141c8eb3bb7f28b0ada7070e0b9b6d8632 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version 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"PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0bde7a4febf07fdb8780f652af167f5f9405b7c2 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + 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"missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/run_config.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..705b37f42cbc60a30de4fe4a4b0df2f4a0340311 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:56:29", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "256", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/runtime_result.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..617455803da9ab7119a01451262c6b607ec6b6c0 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175629", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:56:29", + "ended_at": "2026-05-04T17:57:10", + "duration_sec": 40.056 + }, + "generate": { + "started_at": "2026-05-04T17:57:10", + "ended_at": "2026-05-04T17:57:15", + "duration_sec": 5.089 + } + } +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_report.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e86089d7f904cc28c7d67d3d0e6ee530bd1bcf07 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_transforms_applied.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a7e71fa71d160ed2e4c98843a542042bbf3ca4d6 --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv new file mode 100644 index 0000000000000000000000000000000000000000..b76f452e50bc1efbd6d3d66b11321686efd20c3f --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f6b6e7690fa478a62874ea75e9b98054f344b22f6d643f1a56f557b739bfe1 +size 135661 diff --git a/hyperparameter/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/hyperparameter/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/_arf_generate.py b/hyperparameter/n3/arf/arf-n3-20260504_205824/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..0970ca296d2ef5faf75a41439b04b04ee0c8713b --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(3918) +c_csv = "/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n3' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/arf-n3-3918-20260504_205856.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/arf-n3-3918-20260504_205856.csv") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/_arf_train.py b/hyperparameter/n3/arf/arf-n3-20260504_205824/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3bba8de15cf535a603d821694cdbada6cc6f550b --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/arf_model.pkl") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/arf-n3-3918-20260504_205856.csv b/hyperparameter/n3/arf/arf-n3-20260504_205824/arf-n3-3918-20260504_205856.csv new file mode 100644 index 0000000000000000000000000000000000000000..1bc154163f3bcb94e1864c4343ec44ff2a21d821 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/arf-n3-3918-20260504_205856.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2fd0ae3ad5f67041c293455494267d57e6113df9d5d8b2ee3bf3d3c04c0b379f +size 878992 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/arf_model.pkl b/hyperparameter/n3/arf/arf-n3-20260504_205824/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b87a623ab54ddacedb77c7f071b005ce41a2a069 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bceb9c438072481d0a697d237dc8e23f615c8607d624cacb86baf0bc5a4530d3 +size 5470611 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/gen_20260504_205856.log b/hyperparameter/n3/arf/arf-n3-20260504_205824/gen_20260504_205856.log new file mode 100644 index 0000000000000000000000000000000000000000..6167f76c5fc288240337b5a76eec0e67430a1ebb --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/gen_20260504_205856.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d5704e01ab6310aaca948db80455d3403cc68fd34c05aa1c6b292a14c2220ca1 +size 719 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/input_snapshot.json b/hyperparameter/n3/arf/arf-n3-20260504_205824/input_snapshot.json new file mode 100644 index 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}, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205824/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/staged_features.json b/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/test.csv b/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/train.csv b/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/val.csv b/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205824/train_20260504_205825.log b/hyperparameter/n3/arf/arf-n3-20260504_205824/train_20260504_205825.log new file mode 100644 index 0000000000000000000000000000000000000000..360e2bd08172700b9cf04b2043b34cabb15dc9e9 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205824/train_20260504_205825.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48baeeb3fa1a9b14ddd48acc8bb7d368f8cd3a8b2f009b5cbbee6bf20c43d93f +size 1545 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/_arf_generate.py b/hyperparameter/n3/arf/arf-n3-20260504_205912/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d97f818f6ce34a883aa0c1acae80ceeacb70d82a --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(3918) +c_csv = "/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n3' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/arf-n3-3918-20260504_210013.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/arf-n3-3918-20260504_210013.csv") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/_arf_train.py b/hyperparameter/n3/arf/arf-n3-20260504_205912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0a47c3c8c70efa3ab178b9e99e6ccd4a0789f201 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/arf_model.pkl") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/arf-n3-3918-20260504_210013.csv b/hyperparameter/n3/arf/arf-n3-20260504_205912/arf-n3-3918-20260504_210013.csv new file mode 100644 index 0000000000000000000000000000000000000000..24d9a22eab2d6d63648c1e06cd2e348b182e78c5 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/arf-n3-3918-20260504_210013.csv @@ -0,0 +1,3 @@ +version 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https://git-lfs.github.com/spec/v1 +oid sha256:28c0ccda0980d5a57f68b29bb184ccd6cc82846babda1377467237720053a692 +size 719 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/input_snapshot.json b/hyperparameter/n3/arf/arf-n3-20260504_205912/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8f61e094a5a82208890ae5982a105d557698988d --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": 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"profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_205912/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/staged_features.json b/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/test.csv b/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/train.csv b/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/val.csv b/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_205912/train_20260504_205912.log b/hyperparameter/n3/arf/arf-n3-20260504_205912/train_20260504_205912.log new file mode 100644 index 0000000000000000000000000000000000000000..6c4e73ca70590a275d871dd6252979cdc92276ba --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_205912/train_20260504_205912.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ff76c558a3b98f78cb490370f935607a19339f0061c129a430eb31103c898fa +size 754 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/_arf_generate.py b/hyperparameter/n3/arf/arf-n3-20260504_210029/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c9797b9586a2339050e09d786df875bf97b6cd6d --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(3918) +c_csv = "/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210029/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210029/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n3' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210029/arf-n3-3918-20260504_210110.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210029/arf-n3-3918-20260504_210110.csv") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/_arf_train.py b/hyperparameter/n3/arf/arf-n3-20260504_210029/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..17aa0d718fb7d182d0f344d9aa5e28e4763aaeec --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210029/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210029/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210029/arf_model.pkl") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/arf-n3-3918-20260504_210110.csv b/hyperparameter/n3/arf/arf-n3-20260504_210029/arf-n3-3918-20260504_210110.csv new file mode 100644 index 0000000000000000000000000000000000000000..db28cb6a108603ea9d12ac81450656666d2737f8 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/arf-n3-3918-20260504_210110.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d65812de4cb805a3cb96e76643f6a164fa583bd90ed53bb4506466a68bb3b94d +size 868915 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/arf_model.pkl b/hyperparameter/n3/arf/arf-n3-20260504_210029/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..387da44901555d8cfb53f5af2f08a6ebc9932795 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2cf6617744597907b3fe38bbd16462e092086450f6f72da071eea3ee9be9f528 +size 51261576 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/gen_20260504_210110.log b/hyperparameter/n3/arf/arf-n3-20260504_210029/gen_20260504_210110.log new file mode 100644 index 0000000000000000000000000000000000000000..3d62fd476bac15b31fc7137b201a510673c43890 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/gen_20260504_210110.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0fb80f9a9577c35ba2631e2feeba2ecd05fdb0325780ffa4b602e40cd733bff2 +size 719 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/input_snapshot.json b/hyperparameter/n3/arf/arf-n3-20260504_210029/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8f61e094a5a82208890ae5982a105d557698988d --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/public_gate/normalized_schema_snapshot.json b/hyperparameter/n3/arf/arf-n3-20260504_210029/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/test.csv b/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/train.csv b/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/val.csv b/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210029/train_20260504_210029.log b/hyperparameter/n3/arf/arf-n3-20260504_210029/train_20260504_210029.log new file mode 100644 index 0000000000000000000000000000000000000000..41ec4c98e6db62ecd2df17186ef46f556fedb712 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210029/train_20260504_210029.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd5c29756014ceb933326ab3bf1f8e653bc80aa79321f96b34fcd3d3df2a48e6 +size 635 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/_arf_generate.py b/hyperparameter/n3/arf/arf-n3-20260504_210125/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..20ced6a16e441fe7e56843fb798b43745629ea86 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(3918) +c_csv = "/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210125/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210125/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n3' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210125/arf-n3-3918-20260504_210228.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210125/arf-n3-3918-20260504_210228.csv") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/_arf_train.py b/hyperparameter/n3/arf/arf-n3-20260504_210125/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..98610d2c871a9c52412fccce232dd0c965fdd49b --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210125/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210125/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210125/arf_model.pkl") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/arf-n3-3918-20260504_210228.csv b/hyperparameter/n3/arf/arf-n3-20260504_210125/arf-n3-3918-20260504_210228.csv new file mode 100644 index 0000000000000000000000000000000000000000..ca61b6acbae717b8a9e18cdd0781c1949d1567b7 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/arf-n3-3918-20260504_210228.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a0479ad5b40dbf7b08eb72914071534f4e5725b9ef3236cb6733532de0d68f1 +size 868403 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/arf_model.pkl b/hyperparameter/n3/arf/arf-n3-20260504_210125/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b6652c425187019ac3aba839bf22e200585b9a0f --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aebb737bb7bf7bed911cb5d3ab492ddf8765404aa57c4f92d040d6c22976327e +size 103303380 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/gen_20260504_210228.log b/hyperparameter/n3/arf/arf-n3-20260504_210125/gen_20260504_210228.log new file mode 100644 index 0000000000000000000000000000000000000000..015d8d25d14083e5607293a83b4dbc8a7054d5a4 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/gen_20260504_210228.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09aa769e18172aefa07d55dcac0755062be6dd47e240b9c444cba13c31b3a742 +size 719 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/input_snapshot.json b/hyperparameter/n3/arf/arf-n3-20260504_210125/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8f61e094a5a82208890ae5982a105d557698988d --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/public_gate/normalized_schema_snapshot.json b/hyperparameter/n3/arf/arf-n3-20260504_210125/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + 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file diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/staged_features.json b/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": 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sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/train.csv b/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/val.csv b/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210125/train_20260504_210126.log b/hyperparameter/n3/arf/arf-n3-20260504_210125/train_20260504_210126.log new file mode 100644 index 0000000000000000000000000000000000000000..67ca2be4ac07636d71f71b1c0ac6fc8017f7bcfc --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210125/train_20260504_210126.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ec3e778a22f402626085d879819b5319cccfed105be7e329dc417c47cbeb0d7f +size 639 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210244/_arf_generate.py b/hyperparameter/n3/arf/arf-n3-20260504_210244/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..fe55c7f430990e64d1733263027699b8feff703b --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210244/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(3918) +c_csv = "/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210244/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210244/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n3' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210244/arf-n3-3918-20260504_210428.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210244/arf-n3-3918-20260504_210428.csv") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210244/_arf_train.py b/hyperparameter/n3/arf/arf-n3-20260504_210244/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..42ee63ebdaa0315d6e0c5384fac16acccee7b048 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210244/_arf_train.py @@ -0,0 +1,47 @@ +import os +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001")) + q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999")) + lo, hi = df[col].quantile(q_low), df[col].quantile(q_high) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210244/staged/public/train.csv") +df = _sanitize_for_arf(df) +num_trees = int(os.environ.get("ARF_NUM_TREES", "30")) +delta = float(os.environ.get("ARF_DELTA", "0")) +max_iters = int(os.environ.get("ARF_MAX_ITERS", "10")) +early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes")) +verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes")) +min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5")) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") +print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}") + +model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210244/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260504_210244/arf_model.pkl") diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210244/arf-n3-3918-20260504_210428.csv b/hyperparameter/n3/arf/arf-n3-20260504_210244/arf-n3-3918-20260504_210428.csv new file mode 100644 index 0000000000000000000000000000000000000000..5eaa1dcdb541522adb07007059aed084463e38a3 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210244/arf-n3-3918-20260504_210428.csv @@ -0,0 +1,3 @@ +version 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https://git-lfs.github.com/spec/v1 +oid sha256:d1dc5000f571b1785b47e3fe6874f9247ce368cd5c3b5bb31f998501c9d779ef +size 692 diff --git a/hyperparameter/n3/arf/arf-n3-20260504_210244/input_snapshot.json b/hyperparameter/n3/arf/arf-n3-20260504_210244/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8f61e094a5a82208890ae5982a105d557698988d --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210244/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": 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0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/hyperparameter/n3/arf/arf-n3-20260504_210244/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": 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+oid sha256:7150e1f066145db1cffdeeac246bb8c1c298cd0f524fd7ef687784b41825f908 +size 640 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/_bayesnet_generate.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..21b62d267788893c0176a7dcef41ee6b976495ab --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(3918) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet-n3-3918-20260504_210452.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet-n3-3918-20260504_210452.csv") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/_bayesnet_train.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2461a4ad1e22fb24a55039cac6da43a420b12714 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_model.pkl") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet-n3-3918-20260504_210452.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet-n3-3918-20260504_210452.csv new file mode 100644 index 0000000000000000000000000000000000000000..c782b3b245d5a8e3d41e1364cf760520ea8a10db --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet-n3-3918-20260504_210452.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a470ccc8229c508b08717e61164cb6d287fb10f89f3f6fd073e288d909340bc5 +size 883743 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_coltypes.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..59893d873ed1f60d3bbb17d18abfda70729fe810 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_coltypes.json @@ -0,0 +1,53 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_model.pkl b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3dc29155f0e37ee3627bd065fe43ca04a7ffb728 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:03e9078c9c8c1468b8437d7730ea8a89050bb5196f7e093a9778e17d29f8d5da +size 7682 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/const_cols.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/gen_20260504_210452.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/gen_20260504_210452.log new file mode 100644 index 0000000000000000000000000000000000000000..7d1932457eff285473ae09a12f58bcc17b2a0421 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/gen_20260504_210452.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3bf08121b4dd22ac9e6f07af013bb75fde1c9df0731daf091a2a9f42cbc3546c +size 3661 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/input_snapshot.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..527cf133718a3c780f9b12547e302d03bea557f3 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/public_gate/normalized_schema_snapshot.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210445/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/staged_features.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/test.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/train.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/val.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/train_20260504_210445.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/train_20260504_210445.log new file mode 100644 index 0000000000000000000000000000000000000000..fb9b386db1937ff4e9e3e0796fd5342539ae5d83 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210445/train_20260504_210445.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af540394fec4c83b343af755500931fa54c7fb166ef345d211ebd4403f075beb +size 3853 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/_bayesnet_generate.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..2fde47d4e72ef075a1cbb45261e0fbd0a3d7a7ec --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(3918) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet-n3-3918-20260504_210520.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet-n3-3918-20260504_210520.csv") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/_bayesnet_train.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c074ffd0e4225b646aa9ef64be6e8d4a2a8cb693 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_model.pkl") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet-n3-3918-20260504_210520.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet-n3-3918-20260504_210520.csv new file mode 100644 index 0000000000000000000000000000000000000000..e2363ddeae1a6d42a6598fa1ae4609188f6b0fcc --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet-n3-3918-20260504_210520.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc81ac57adba606c3a81e4e3e6b4dbda54b3dd167c8c095acd285ac4693a8787 +size 883995 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_coltypes.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..59893d873ed1f60d3bbb17d18abfda70729fe810 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_coltypes.json @@ -0,0 +1,53 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_model.pkl b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..56f9335ccaa9cac2c5cb90e7e90d44f7f9419656 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14fe486610fa6edabc307f05282f06363d355e600b30ce9a7cf49e260d0d39fc +size 9132 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/const_cols.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/gen_20260504_210520.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/gen_20260504_210520.log new file mode 100644 index 0000000000000000000000000000000000000000..032130e71f7d34b1cfb0539ade7c6a65359413bd --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/gen_20260504_210520.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f881b8d0f4f1318b3310f007c0f44249461e37cefa688e38428fb0a48d998b4 +size 3661 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/input_snapshot.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/input_snapshot.json new file mode 100644 index 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+ "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210513/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/staged_features.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/test.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/train.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/val.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/train_20260504_210513.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/train_20260504_210513.log new file mode 100644 index 0000000000000000000000000000000000000000..aeb6d84435b0d2af03e85779a17462a09f877da5 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210513/train_20260504_210513.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af72e4a06fb613f12c476cad7c631b1ea8640e0a732ba7fb005b0e8e9974c434 +size 3853 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/_bayesnet_generate.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e48eddde452565739e98bae6e93bebaea1e7a7eb --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(3918) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet-n3-3918-20260504_210549.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet-n3-3918-20260504_210549.csv") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/_bayesnet_train.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..968ce1714215b6e8d87fa48141ec145285cf0e7d --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet-n3-3918-20260504_210549.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet-n3-3918-20260504_210549.csv new file mode 100644 index 0000000000000000000000000000000000000000..8e4c5f769670d862afa7b08a30ba6ee591f9bee8 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet-n3-3918-20260504_210549.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bef2ba3fbfe9b584895b771aa3d1f0ad47344c6cc03ade7ec03b903021c397ad +size 884107 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_coltypes.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..59893d873ed1f60d3bbb17d18abfda70729fe810 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_coltypes.json @@ -0,0 +1,53 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..bb69cd332f67532dd621f240042431c697714cf0 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b514c2ddd92e061a24c3b90710574c1c291baecdf8cba550da95251c0bf695f0 +size 14168 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/const_cols.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/gen_20260504_210549.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/gen_20260504_210549.log new file mode 100644 index 0000000000000000000000000000000000000000..96e3e873ce5219daca901bdca9b491fbccc392e7 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/gen_20260504_210549.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:948a9604aa0570be9a850ddf3ad97d5c03f7ba775f55950a7a0fec079db693b9 +size 3661 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/input_snapshot.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/input_snapshot.json new file mode 100644 index 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "bayesnet", + "dataset": "n3", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 3918, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet-n3-3918-20260504_210549.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/staged_features.json", + "target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BAYESNET_DISABLE_AUTO_CAPS": "1", + "BAYESNET_EDGE_WEIGHTS_FN": "mutual_info", + "BAYESNET_ESTIMATOR_TYPE": "chow-liu", + "BAYESNET_FIT_ROWS": "120000", + "BAYESNET_MAX_BINS": "8", + "BAYESNET_MAX_CAT_LEVELS": "128", + "BAYESNET_N_JOBS": "1", + "BAYESNET_STRUCT_ROWS": "25000" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/runtime_result.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..4a299e559c086415a12c336c1ff3e54f53449f9c --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "bayesnet", + "run_id": "bayesnet-n3-20260504_210542", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet-n3-3918-20260504_210549.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-04T21:05:42", + "ended_at": "2026-05-04T21:05:49", + "duration_sec": 7.395 + }, + "generate": { + "started_at": "2026-05-04T21:05:49", + "ended_at": "2026-05-04T21:05:57", + "duration_sec": 7.571 + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/adapter_report.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..1576df4d20002f8e6cf727e0bdc6832dbfc563e6 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/adapter_transforms_applied.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/model_input_manifest.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..61f2cd0847646c646ee8cc1ddcc4c0a85cb055db --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "bayesnet", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210542/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/staged_features.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/test.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/train.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/val.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/train_20260504_210542.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/train_20260504_210542.log new file mode 100644 index 0000000000000000000000000000000000000000..8273f50fc14a33f89cb5cbd2cad70d856fabeb6d --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210542/train_20260504_210542.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cbad70772e7ba358882f77db3f7e045d2ab5447a83eab5e995e707091bde22f3 +size 3855 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/_bayesnet_generate.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8a609e8e1669b087d25bee909698e40ffe45a355 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(3918) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet-n3-3918-20260504_210618.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet-n3-3918-20260504_210618.csv") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/_bayesnet_train.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a4513d34c4ec9ffbcbfdea3ff15fa23bc7b6e345 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210610/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_model.pkl") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet-n3-3918-20260504_210618.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet-n3-3918-20260504_210618.csv new file mode 100644 index 0000000000000000000000000000000000000000..7e937cf86b8d73e1e1509d96ad8cd409a57530d2 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet-n3-3918-20260504_210618.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13ddda900c06883c94cebaefacd5c5d3e1cd2f5ce251e2e4e95305053c39b36c +size 884009 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_coltypes.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..59893d873ed1f60d3bbb17d18abfda70729fe810 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_coltypes.json @@ -0,0 +1,53 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_model.pkl b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..bb69cd332f67532dd621f240042431c697714cf0 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b514c2ddd92e061a24c3b90710574c1c291baecdf8cba550da95251c0bf695f0 +size 14168 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/const_cols.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/gen_20260504_210618.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/gen_20260504_210618.log new file mode 100644 index 0000000000000000000000000000000000000000..113892981450ec2813ee8c1e0da4ae25796916ac --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/gen_20260504_210618.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f5195bde3455c476dfd3c64784eceaec85536a86cd80b2dcf1e80984d8307f1b +size 3661 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/input_snapshot.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/input_snapshot.json new file mode 100644 index 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"PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "quality", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/public_gate/staged_input_manifest.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210610/public_gate/staged_input_manifest.json new file mode 100644 index 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0000000000000000000000000000000000000000..8bafe25c09d4a55daf560f5b719bc2bcf86ae1d7 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(3918) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet-n3-3918-20260504_210647.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet-n3-3918-20260504_210647.csv") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/_bayesnet_train.py b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a3bf5347ea37c432dacfeb2901799d629dbb2814 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/_bayesnet_train.py @@ -0,0 +1,146 @@ + +import json +import os +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210640/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0")) +max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0")) +if max_bins <= 0: + max_bins = 10 +if max_cat_levels <= 0: + max_cat_levels = 256 +auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes") +if auto_caps and max_bins == 10 and max_cat_levels == 256: + if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 + if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000")) +fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000")) +estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip() +edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip() +root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None +n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1")) +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > struct_rows: + enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > fit_rows: + enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_model.pkl") diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet-n3-3918-20260504_210647.csv b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet-n3-3918-20260504_210647.csv new file mode 100644 index 0000000000000000000000000000000000000000..0889d4975af1c8b70252bf83b03479dd4f382f6f --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet-n3-3918-20260504_210647.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3b19f5b6e8f4bafd407bacce57bd40e7d30c90b6f3b538573a9ff7967c34c69 +size 884422 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_coltypes.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..59893d873ed1f60d3bbb17d18abfda70729fe810 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_coltypes.json @@ -0,0 +1,53 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_model.pkl b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..bb69cd332f67532dd621f240042431c697714cf0 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b514c2ddd92e061a24c3b90710574c1c291baecdf8cba550da95251c0bf695f0 +size 14168 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/const_cols.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/gen_20260504_210647.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/gen_20260504_210647.log new file mode 100644 index 0000000000000000000000000000000000000000..0845cbba620861e9162c2236ef89b039e6e15e76 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/gen_20260504_210647.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52e82fa571b0c25cec74c5ce2978d789219dae0b913935135b61206356ce1b30 +size 3661 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/input_snapshot.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..527cf133718a3c780f9b12547e302d03bea557f3 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/public_gate/normalized_schema_snapshot.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/public_gate/public_gate_report.json b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..93b97319f739e39079d6c6549fd2020f8a6dac25 --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "n3", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": 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b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/train_20260504_210640.log b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/train_20260504_210640.log new file mode 100644 index 0000000000000000000000000000000000000000..f3def258cadff93cd3e925a5082c34505c07f8bb --- /dev/null +++ b/hyperparameter/n3/bayesnet/bayesnet-n3-20260504_210640/train_20260504_210640.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f945589b988983162ac6d849d12f0c094606a35199b4728170d06ead747c9538 +size 3865 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_generate.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b820b344bc742aa80cfe43715de01bbef1be7c65 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_train.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7cdd5c75b05c8b9cba66e0199f95e5ab98dd3c05 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=8, + generator_dim=(16, 16), + discriminator_dim=(16, 16), + batch_size=32, + pac=1, + epochs=50, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv new file mode 100644 index 0000000000000000000000000000000000000000..a1d9e9c1d0add5c9ffff28dabcef3a7f5aa6950e --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f14ad83416025a5455f69cec6d63e5314c344048ab5372353ae3ca5d75ce34a +size 820708 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/ctgan_metadata.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/gen_20260504_163441.log b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/gen_20260504_163441.log new file mode 100644 index 0000000000000000000000000000000000000000..8eaf0a5f880a368aac68190507b2428610e0d853 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/gen_20260504_163441.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd91dc885739943a172da5fe8f7b656b43c8c09a7a306e321dd06d8bd4651ba3 +size 297 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/input_snapshot.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..15f60f257edd5bcdb6b9e07be8cb5182076affc1 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": 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index 0000000000000000000000000000000000000000..a5f1ed426bceed10bef8a1102a395b130447821a --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "run_id": "ctgan-n3-20260504_163226", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:32:26", + "ended_at": "2026-05-04T16:34:41", + "duration_sec": 135.487 + }, + "generate": { + "started_at": "2026-05-04T16:34:41", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/staged_features.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/test.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/train.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/val.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163226/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_generate.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..22ab4eff2cf36a3dbade7c9717bd82f27574e27a --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_train.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..efefe23c3f4c3b677bdbdd9cd49c5023cb93ec59 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=64, + pac=1, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv new file mode 100644 index 0000000000000000000000000000000000000000..6ace7e6d64bae7d23ac937fe14b1aeba2bb87411 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0af5beb641d31c47be95b72dcde43e1f2257348f4bbe820bc445b54a1373322b +size 820193 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/ctgan_metadata.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": 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"target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "64", + "CTGAN_DEFAULT_EPOCHS": "100", + "CTGAN_DISCRIMINATOR_DIMS": "32,32", + "CTGAN_EMBEDDING_DIM": "16", + "CTGAN_GENERATOR_DIMS": "32,32", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/runtime_result.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..a7f7b88bd571228bf3c14e39259c8ffcf1de3a53 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "run_id": "ctgan-n3-20260504_163500", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + 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b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/ctgan/adapter_transforms_applied.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/ctgan/model_input_manifest.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..66f05882d6f4384726788905fbddc90af22f11da --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/ctgan/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/staged_features.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/test.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/train.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/val.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163500/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_generate.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..958966b7d26451c5cec305e5e95d971ad603c9f4 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_train.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..37981c24b61b6ffbc8d764127aae6a5d20410d3e --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=128, + pac=1, + epochs=150, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv new file mode 100644 index 0000000000000000000000000000000000000000..d01269246f4056aca40e395921ff81b5b22a970a --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff18e4b52213da6adbc8c4ced644c1bc2994a3bc04c52d99bd2a9fb3fc4be71d +size 819814 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/ctgan_metadata.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/gen_20260504_163919.log b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/gen_20260504_163919.log new file mode 100644 index 0000000000000000000000000000000000000000..c15018523d9b663dbb518ee0443132e2447a3679 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/gen_20260504_163919.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4a1b092247ba70ae7e47162bf2690c54df0f896d414770729c5056c19aacf32 +size 297 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/input_snapshot.json 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"test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/staged_features.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/test.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/val.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163735/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_generate.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..11daec1d2a737e9b2a264f4628dd15c77d08202e --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_train.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..932023a5c4862392815be9d0fa56d2f3dcb84b5c --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=80, + pac=5, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv new file mode 100644 index 0000000000000000000000000000000000000000..5506dd849d4d391e8754bc60cff1a6da38745e26 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93d55cffc061237adad6514ad0b9d5b22ab70147932c96f5cde041ff0f2ce05c +size 821320 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/ctgan_metadata.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/gen_20260504_164128.log b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/gen_20260504_164128.log new file mode 100644 index 0000000000000000000000000000000000000000..57879be2cdd4bf15e013811741863132c1797dba --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/gen_20260504_164128.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92e5b04d368e83994eed81d86660bad6611d5497831634fce41b9e8e9b85993c +size 297 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/input_snapshot.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..15f60f257edd5bcdb6b9e07be8cb5182076affc1 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e10bb01ab5a943d3b8f5362eaeab273814da2602 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b2544f21e5cdec77204a9e3825cdbe1d940087b75dd9cfe564b6f065929ac1d +size 441827 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/train_20260504_163938.log b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/train_20260504_163938.log new file mode 100644 index 0000000000000000000000000000000000000000..59e77ff88703915772c30c740890d66f6dabf292 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/train_20260504_163938.log @@ -0,0 +1,3 @@ +version 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a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/staged_features.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/test.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/val.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_163938/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_generate.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ce16fd64962b33468b0055b7bdf9bc8546696887 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/models_200epochs/ctgan_200epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_train.py b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..74c218a06e4ff52752bd2e472c3d7754f1d49e1a --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=100, + pac=10, + epochs=200, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/models_200epochs/ctgan_200epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/models_200epochs/ctgan_200epochs.pt") \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv new file mode 100644 index 0000000000000000000000000000000000000000..f227eb8a8114bb08526db72cb68358776eff32ca --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5cef0aba072b44c34ebff73200196215ccf66da1c54e39ea715d23d26bb30627 +size 820612 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/ctgan_metadata.json b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/gen_20260504_164434.log b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/gen_20260504_164434.log new file mode 100644 index 0000000000000000000000000000000000000000..8749b768b43f35d0ba5e644006219db24e80ffe7 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/gen_20260504_164434.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:539db263dc0c0a18d1e2a1f0521476c20ad046c35ae3d32287acd88d3201d894 +size 297 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/input_snapshot.json 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https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/staged/public/val.csv b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/ctgan/ctgan-n3-20260504_164147/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_X_host.npy b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..bb3be252292265fb98716334d40ccfaf8d75d244 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b15fb02a2af34c0c93fdfe59170a6f8c933a7a529162825116447fdff3efa1ff +size 188192 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_gen.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..08176372bed652bdfc877fa24e7d1b333888c020 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(3918)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/forest-n3-3918-20260505_062054.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_meta_host.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..c65de394e081438752dd19dc6672d70748924abc --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol", "quality"], "cat_indexes": []} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_train.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4dbc87f59a72d817202a2372b69085941ad27e19 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=4 " + f"n_estimators=5 duplicate_K=2 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=4, n_estimators=5, duplicate_K=2, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/forestdiffusion_model.joblib') diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/forest-n3-3918-20260505_062054.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/forest-n3-3918-20260505_062054.csv new file mode 100644 index 0000000000000000000000000000000000000000..e47ad47195cb81492b7ba264343ba6907bf9e9a4 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/forest-n3-3918-20260505_062054.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11f64ba53624421ad02bcabf737ecdd512b040c34bca5aa82d3e630728cd69d5 +size 884040 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/forestdiffusion_model.joblib b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..6c3eb2ec261278e63d8f49b9fe18e18abe031a07 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:10ab08d912c8c67f19f9ebb7eba1bda99fe73b2ac41dd3d4ecdc7fe918f2c939 +size 488375 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/gen_20260505_062054.log b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/gen_20260505_062054.log new file mode 100644 index 0000000000000000000000000000000000000000..43ba15966b8407787550104140a7679f135b3e11 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/gen_20260505_062054.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e7ba53404e680fd3124a5bbf0be636579bca3884695582084c686ca5ba2eb3b +size 293 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/input_snapshot.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1706b1e21e3ea594c0730d0ec7b58ca4e0d54ac2 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/models_fd/model.joblib b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..6c3eb2ec261278e63d8f49b9fe18e18abe031a07 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:10ab08d912c8c67f19f9ebb7eba1bda99fe73b2ac41dd3d4ecdc7fe918f2c939 +size 488375 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/normalized_schema_snapshot.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/public_gate_report.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..93b97319f739e39079d6c6549fd2020f8a6dac25 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "n3", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "quality", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/staged_input_manifest.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..555364dac3eb6c8ffa52737f2732ef3d0ede194b --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_061933/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + 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sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/staged/public/val.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/train_20260505_061933.log b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/train_20260505_061933.log new file mode 100644 index 0000000000000000000000000000000000000000..ba2784917e2d87b37614e479a54aae3ae4aeac0b --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_061933/train_20260505_061933.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f6c6d1a25e71ee329d5cd3b5d70d013dabfe9858aba498e08dcddd6a644f18f +size 431 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_X_host.npy b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..bb3be252292265fb98716334d40ccfaf8d75d244 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b15fb02a2af34c0c93fdfe59170a6f8c933a7a529162825116447fdff3efa1ff +size 188192 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_gen.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..3021d200a39d9ef26941bde91056775a851cc521 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(3918)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/forest-n3-3918-20260505_062519.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_meta_host.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..c65de394e081438752dd19dc6672d70748924abc --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol", "quality"], "cat_indexes": []} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_train.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..617a996ec3b7740c2e05a332bb8f06056742e3dd --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=6 " + f"n_estimators=10 duplicate_K=3 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=6, n_estimators=10, duplicate_K=3, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/forestdiffusion_model.joblib') diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/forest-n3-3918-20260505_062519.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/forest-n3-3918-20260505_062519.csv new file mode 100644 index 0000000000000000000000000000000000000000..d99aaded7ee4e6fddcaca7c3a1131070cee4a501 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/forest-n3-3918-20260505_062519.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7af5e3d897ad837ffdf82e3fde74f7b7dad85e83472d101b290734f0d3425921 +size 884745 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/forestdiffusion_model.joblib b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..3ec161751f942381084670561b17349eb0dbbfef --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa4335dde9bec5bdaa17014dc51c693a9db83e187acad00e993692813ff1e5eb +size 1054283 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/gen_20260505_062519.log b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/gen_20260505_062519.log new file mode 100644 index 0000000000000000000000000000000000000000..cf99657f49307add731d915282434f73ccce901d --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/gen_20260505_062519.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db73f7006bb86a6d94a5eed265628391da50a3bd5ce1d8ca93e655b24e126fb8 +size 294 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/input_snapshot.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1706b1e21e3ea594c0730d0ec7b58ca4e0d54ac2 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": 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b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/staged_features.json", + "public_gate_report": 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"forestdiffusion", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "forestdiffusion", + "dataset": "n3", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 3918, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/models_fd/model.joblib", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/forest-n3-3918-20260505_062519.csv" + }, + "input_artifacts": { + "public_gate_report": 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"FORESTDIFFUSION_MAX_DEPTH": "3", + "FORESTDIFFUSION_MAX_TRAIN_ROWS": "10000", + "FORESTDIFFUSION_N_ESTIMATORS": "10", + "FORESTDIFFUSION_N_JOBS": "1", + "FORESTDIFFUSION_N_T": "6" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/runtime_result.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..708227daf47af76cd18bc01718051c12921f7d60 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "forestdiffusion", + "run_id": "forest-n3-20260505_062110", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": 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b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/forestdiffusion/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062110/staged/forestdiffusion/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/forestdiffusion/adapter_transforms_applied.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/forestdiffusion/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/forestdiffusion/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/forestdiffusion/model_input_manifest.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/forestdiffusion/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b1e2861d72928b8deba2ee2f309b68017075a76a --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "forestdiffusion", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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"missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": 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No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/staged_features.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/test.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/train.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/val.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/train_20260505_062110.log b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/train_20260505_062110.log new file mode 100644 index 0000000000000000000000000000000000000000..b6b1d1e4097eae38053af920be6a4d0b1f73173a --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062110/train_20260505_062110.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:71784c7aa3704a5550ad05bf0fee2e647630f38726ea6710b7b5ade05e90c91e +size 433 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_X_host.npy b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..bb3be252292265fb98716334d40ccfaf8d75d244 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b15fb02a2af34c0c93fdfe59170a6f8c933a7a529162825116447fdff3efa1ff +size 188192 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_gen.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..c5de3238bd03eb73e25dedfd351e2bac7fe357eb --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(3918)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/forest-n3-3918-20260505_063224.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_meta_host.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..c65de394e081438752dd19dc6672d70748924abc --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol", "quality"], "cat_indexes": []} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_train.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..db4a0deeba3c0768780010ed66ff01f602a67c75 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=8 " + f"n_estimators=10 duplicate_K=4 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=8, n_estimators=10, duplicate_K=4, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/forestdiffusion_model.joblib') diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/forest-n3-3918-20260505_063224.csv 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0000000000000000000000000000000000000000..6c955f5413b8544550f70db13340f3b7fef84189 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "forestdiffusion", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_062535/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/staged_features.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/test.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/train.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/val.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/train_20260505_062535.log b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/train_20260505_062535.log new file mode 100644 index 0000000000000000000000000000000000000000..227e84da77ca61ea9b9ef02b6bca88abc113d62e --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_062535/train_20260505_062535.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eeba4e46a738cd99099f89bf855f94629d2a36eb63dfd3cc9020d58d59b3d854 +size 433 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_X_host.npy b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..bb3be252292265fb98716334d40ccfaf8d75d244 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b15fb02a2af34c0c93fdfe59170a6f8c933a7a529162825116447fdff3efa1ff +size 188192 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_gen.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..852b2dcce8c4166c709b014ef7c48fcbac704bbf --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_063240/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(3918)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_063240/forest-n3-3918-20260505_064509.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_meta_host.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..c65de394e081438752dd19dc6672d70748924abc --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol", "quality"], "cat_indexes": []} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_train.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0d5d8a0469793ffc17d063a633a0f180f1fee8f5 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_063240/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_063240/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=10 " + f"n_estimators=15 duplicate_K=5 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=10, n_estimators=15, duplicate_K=5, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_063240/forestdiffusion_model.joblib') diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/forest-n3-3918-20260505_064509.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/forest-n3-3918-20260505_064509.csv new file mode 100644 index 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b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/forestdiffusion/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/forestdiffusion/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/forestdiffusion/model_input_manifest.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/forestdiffusion/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..db90669d7dae1f2f88b851e21332690f5569861b --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "forestdiffusion", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": 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"features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_063240/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/staged_features.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/test.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/train.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/val.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/train_20260505_063240.log b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/train_20260505_063240.log new file mode 100644 index 0000000000000000000000000000000000000000..7e614353f72525a735984267d84f67d705d2a38c --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_063240/train_20260505_063240.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe37de51141b277b041f701d5f53b497c58e44777917fdf45a62d8d04aedeb46 +size 434 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_X_host.npy b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..bb3be252292265fb98716334d40ccfaf8d75d244 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b15fb02a2af34c0c93fdfe59170a6f8c933a7a529162825116447fdff3efa1ff +size 188192 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_gen.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..72dabec8612ee58716d9b183a5bfb1d944026aee --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_064525/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(3918)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_064525/forest-n3-3918-20260505_070241.csv', index=False) +print("saved", len(df)) diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_meta_host.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..c65de394e081438752dd19dc6672d70748924abc --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol", "quality"], "cat_indexes": []} \ No newline at end of file diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_train.py b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2a22b517b5367ca97106338417e3379ce6c6e47d --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_064525/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_064525/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=10 " + f"n_estimators=20 duplicate_K=5 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=10, n_estimators=20, duplicate_K=5, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260505_064525/forestdiffusion_model.joblib') diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/forest-n3-3918-20260505_070241.csv b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/forest-n3-3918-20260505_070241.csv new file mode 100644 index 0000000000000000000000000000000000000000..aeb85a0d300f3526dadb59765f1d3fee7db281fb --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/forest-n3-3918-20260505_070241.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7de12f21b4a0f9e78a91695486d70f2fb79dc61755400e23bf14f8d38e5f9889 +size 885176 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/forestdiffusion_model.joblib b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..8f2419732a4f5f1e646a08b35d1a68afe0d010a3 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c35e821df425eda69081853a53193bd130915dbe151043ac6e3984c70fbd33d +size 4300179 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/gen_20260505_070241.log b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/gen_20260505_070241.log new file mode 100644 index 0000000000000000000000000000000000000000..cf5b5aae7698f9b9f4755c9c3839522b89247b80 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/gen_20260505_070241.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4f02ce044a246e51f4d7d1eaad5d5e4991d1cc8efe53f145936005a62431a42 +size 294 diff --git a/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/input_snapshot.json b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1706b1e21e3ea594c0730d0ec7b58ca4e0d54ac2 --- /dev/null +++ b/hyperparameter/n3/forestdiffusion/forest-n3-20260505_064525/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": 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b/hyperparameter/n3/realtabformer/rtf-n3-20260505_024350/train_20260505_024350.log new file mode 100644 index 0000000000000000000000000000000000000000..4b085b9475585512716014eaa60a88cba7ad99fc --- /dev/null +++ b/hyperparameter/n3/realtabformer/rtf-n3-20260505_024350/train_20260505_024350.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd8591eff961a4518cbb59a39068fe3c6aa4944852877626b0e6512cd9b71d2d +size 140582 diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010259/_tabbyflow_gen.py b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010259/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..59efc60f28085d0ee0d57a7909c2dec3d60740af --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010259/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_010259/tabular_bundle/pipeline_n3" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n3/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_010259/tabbyflow-n3-3918-20260505_010451.csv") diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010259/_tabbyflow_train.py b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010259/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..370484d2d9270254590da221c66ff534621af3b4 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010259/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_010259/tabular_bundle/pipeline_n3" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "100" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010259/gen_20260505_010451.log b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010259/gen_20260505_010451.log new file mode 100644 index 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shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n3/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_010529/tabbyflow-n3-3918-20260505_010913.csv") diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010529/_tabbyflow_train.py b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010529/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..aa7a34e96bd38a0f2ed5381a279cd4e71124c2c0 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010529/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_010529/tabular_bundle/pipeline_n3" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "200" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010529/gen_20260505_010913.log b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010529/gen_20260505_010913.log new file mode 100644 index 0000000000000000000000000000000000000000..ed946d61e466db144e949ed3775e24f7ecd4ac99 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010529/gen_20260505_010913.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1a77e6b93f6ee957de7b9b0a82aa518b655746e4535a054d44b94cc2c109f1f6 +size 3946 diff --git 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0000000000000000000000000000000000000000..38d087e90b150b713c333750b8326b97c22b20cb --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010529/train_20260505_010530.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6178fe20384998efb862094d3bbdd4756983946228da1cd1b2787733c9009307 +size 119247 diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/_tabbyflow_gen.py b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..7b9b7cee0e795252f05e78c1d487dc52baeb24a5 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_010953/tabular_bundle/pipeline_n3" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n3/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_010953/tabbyflow-n3-3918-20260505_011500.csv") diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/_tabbyflow_train.py b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..531f7b0fd0b6b13283c747175d5e72a930b4436a --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_010953/tabular_bundle/pipeline_n3" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "300" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/gen_20260505_011500.log b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/gen_20260505_011500.log new file mode 100644 index 0000000000000000000000000000000000000000..27601ff2261fc7dc4200d6b706f539015f4b8550 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/gen_20260505_011500.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc856fd1334b87c1ecee0579427b1bcfbfaa484c69747fcb7a5a206b492b919a +size 3946 diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/input_snapshot.json b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3a0cbc32605f1212374c27c5a11c08064d5b6b53 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_010953/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": 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0000000000000000000000000000000000000000..5d86b2e68160a800069c79722ddfd970f1adf873 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_011539/tabular_bundle/pipeline_n3" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n3/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_011539/tabbyflow-n3-3918-20260505_012459.csv") diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/_tabbyflow_train.py b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d339202e43c54d57c5d75f24edb3ee87d3d61773 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_011539/tabular_bundle/pipeline_n3" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/gen_20260505_012459.log b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/gen_20260505_012459.log new file mode 100644 index 0000000000000000000000000000000000000000..a72ee686b3f7a180eae89af73e1a4dbbb5fe5a9b --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/gen_20260505_012459.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1cf39a59a5f71e31e1393a0734f603f732945f3e520bf6c0039dd5dd8e983e5b +size 3945 diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/input_snapshot.json b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_011539/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3a0cbc32605f1212374c27c5a11c08064d5b6b53 --- /dev/null +++ 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https://git-lfs.github.com/spec/v1 +oid sha256:4f47184a76cf9f55735dffc81b2027701ae0068d673cbc7e1d548b19bc51d37f +size 290236 diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/_tabbyflow_gen.py b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..c5ee7eba5fc2c39504fce599f74d80267f710f87 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_012543/tabular_bundle/pipeline_n3" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n3/adapter_efvfm/model_700.pt", + "--num_samples_to_generate", str(int(3918)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_012543/tabbyflow-n3-3918-20260505_013725.csv") diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/_tabbyflow_train.py b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d9444365122d511807df906a17149156fbffe776 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260505_012543/tabular_bundle/pipeline_n3" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "700" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/gen_20260505_013725.log b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/gen_20260505_013725.log new file mode 100644 index 0000000000000000000000000000000000000000..fe77122a836604c9bfeda62d9151c648bc2e4431 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/gen_20260505_013725.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1114ff59f09135c24f897b3fe79adb3a29a92082b60689c7a32c11ea6a1c34df +size 3945 diff --git 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0000000000000000000000000000000000000000..337704e99f50e60daa8b680782e4cea3766db810 --- /dev/null +++ b/hyperparameter/n3/tabbyflow/tabbyflow-n3-20260505_012543/train_20260505_012543.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:655d2ba9d46b8601a0ca8d709855e6e01fbb3780e0fbdd89d064ee387d169ac6 +size 405514 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/_tabddpm_sample_r0.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..ada50ee76c927a16be6763e23abc7e1e4c6c4464 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 3918 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/config_sample_20260504_184334_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/tabddpm-n3-3918-20260504_184334.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/tabddpm-n3-3918-20260504_184334.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/_tabddpm_train.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fe53a61471f588b511f792ad82a56804ac821e1c --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/config.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..bab2b0f32f2d76add717ff39968a403cdad40802 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/config_sample_20260504_184334_r0.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/config_sample_20260504_184334_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..6fa42a3262f6cd3a6f2d1d1921f98d0f7d5628da --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/config_sample_20260504_184334_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/X_num_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..94beb8413ef55592710518365965f4cb503523ee --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:093d8f405d764277d5a007953f25ab6466f0857f71f0d8f3c437463fb5bf240a +size 172520 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/info.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/y_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/gen_20260504_184334_r0.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/gen_20260504_184334_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..b1e1319aadf2c261120a577f98567ee34d16f6c9 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/gen_20260504_184334_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6656acb88fe007c8814576f3f7db1adb24aa119af65a4bb52d46d035b82857bf +size 67703 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/input_snapshot.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..34d3cbb5018ded64e57cf3350f6ec58dd908f565 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": 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0000000000000000000000000000000000000000..ec784faeea39a528baf43033aa4c8cb68eceb1f5 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aaed8a7d06ac7e0afc768a6c0afb032234393cc27236f3aa65c7b94e23b1e26d +size 344912 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/X_num_unnorm.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/X_num_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..b50cce7b55abe4fe6c687228e96f5a0e12cfa7a1 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/X_num_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31e96511aad4d9c106590a5d764d5bccaf26b26079a05a60423a85f07b1d06cd +size 344912 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/config.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..6fa42a3262f6cd3a6f2d1d1921f98d0f7d5628da --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/info.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/loss.csv b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..1ddac91b9a40dee09cf388aa06652536dc280c15 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23c133bd3e48cb639b0b834b88cf2c9fccf217849cc9a005e10aaf0a72f35ee1 +size 507 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/model.pt b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..8f71de2707a999e92cc67e1c1bcaafbc67985823 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184319/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/tabddpm-n3-3918-20260504_184334.csv b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/tabddpm-n3-3918-20260504_184334.csv new file mode 100644 index 0000000000000000000000000000000000000000..54d16150d01448aa9f513f9d8a02f5ec9f7ce924 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/tabddpm-n3-3918-20260504_184334.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29dc630ea6b7349a7f2536d908fe524fc1c2cb39e08c12f1ab0b272aa29c49d6 +size 774035 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/train_20260504_184319.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/train_20260504_184319.log new file mode 100644 index 0000000000000000000000000000000000000000..6958afc1f27470cafd9455eb8cdcc71950b5f26e --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184319/train_20260504_184319.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:02a1f186b44029b18f94ee3f9b5b69a4ab2133991e5bb33c2348f284e326d88f +size 756 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/_tabddpm_sample_r0.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..214f41b0fa8957414b1e9ce5244c92f7a43066fd --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 3918 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/config_sample_20260504_184422_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/tabddpm-n3-3918-20260504_184422.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/tabddpm-n3-3918-20260504_184422.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/_tabddpm_train.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4e9930952713c0612c347096656b2983409d15d4 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/config.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..34a3c09db4823c36b31bbd0f04d00e7111d2ee63 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/config_sample_20260504_184422_r0.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/config_sample_20260504_184422_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..368ad6bc866e2ea64e05f1393279bbf242f15663 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/config_sample_20260504_184422_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 256 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/X_num_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..94beb8413ef55592710518365965f4cb503523ee --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:093d8f405d764277d5a007953f25ab6466f0857f71f0d8f3c437463fb5bf240a +size 172520 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/info.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/y_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/gen_20260504_184422_r0.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/gen_20260504_184422_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..e1c2cbf7e19774c7a8368ddd0666632e498affe9 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/gen_20260504_184422_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/staged_features.json", + "target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABDDPM_GPUS": "device=3", + "TABDDPM_NUM_TIMESTEPS": "1000", + "TABDDPM_SAMPLE_BATCH_SIZE": "256", + "TABDDPM_STEPS_PER_EPOCH": "100", + "TABDDPM_TRAIN_BATCH_SIZE": "256", + "TABDDPM_TRAIN_LR": "0.001" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/runtime_result.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..3075d3fe18b4da7dec1debad4bce00f601195be3 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "tabddpm", + "run_id": "tabddpm-n3-20260504_184357", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/tabddpm-n3-3918-20260504_184422.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357" + }, + "timings": { + "train": { + "started_at": "2026-05-04T18:43:57", + "ended_at": "2026-05-04T18:44:22", + "duration_sec": 24.306 + }, + "generate": { + "started_at": "2026-05-04T18:44:22", + "ended_at": "2026-05-04T18:44:47", + "duration_sec": 25.547 + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/staged_features.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/staged_features.json new file mode 100644 index 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"data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/test.csv b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/train.csv 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}, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184357/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/tabddpm-n3-3918-20260504_184422.csv b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/tabddpm-n3-3918-20260504_184422.csv new file mode 100644 index 0000000000000000000000000000000000000000..44cd8efba2a93b96471a07836f3760d6d5ed26f5 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/tabddpm-n3-3918-20260504_184422.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5b13bad7cf3f35399afeeafc7f41540415db23674b417f53f018bc5e2822851e +size 773234 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/train_20260504_184357.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/train_20260504_184357.log new file mode 100644 index 0000000000000000000000000000000000000000..df9bfefa8646ec10814526768919ad5273c0fc10 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184357/train_20260504_184357.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4075163fa1d4c2586dc9a174a453b44393c7f1227905d36d482098e5522385d2 +size 1068 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/_tabddpm_sample_r0.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..65c4b1151471e1de39aeefca66bf240c152c30c8 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 3918 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/config_sample_20260504_184535_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/tabddpm-n3-3918-20260504_184535.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/tabddpm-n3-3918-20260504_184535.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/_tabddpm_train.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6bfc8641e30566a73a701b19da815034d201fa16 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/config.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..39ab577235215be56ef98703d48a162374033db2 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 8000 +lr = 0.0005 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/config_sample_20260504_184535_r0.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/config_sample_20260504_184535_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..d64fd0806db36db6883190dd11ddaf19f0aeef79 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/config_sample_20260504_184535_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 8000 +lr = 0.0005 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/X_num_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..94beb8413ef55592710518365965f4cb503523ee --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:093d8f405d764277d5a007953f25ab6466f0857f71f0d8f3c437463fb5bf240a +size 172520 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/info.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/y_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/gen_20260504_184535_r0.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/gen_20260504_184535_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..5b25880b0d0042c5892411628315d8d42c5dd389 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/gen_20260504_184535_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a9a07f287eec7a61a780f5f2956aac1beb8da7957d732c536069e65fa1142747 +size 651503 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/input_snapshot.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..34d3cbb5018ded64e57cf3350f6ec58dd908f565 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": 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0000000000000000000000000000000000000000..3f08ef03708ada63eeac6fb7dfc3c595d7fda41e --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef67a7b3a11386af9e146a05c177dbe5a2674fa4e130e2d5a262fd6d567f0330 +size 344912 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/X_num_unnorm.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/X_num_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..f3c4646d0b5d9becc73dd74d8281a3bb8f151c8f --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/X_num_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1792c43379bbaa200b07784c77c99abe83b9d380cde89cb927aca5e4d6fbb6fd +size 344912 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/config.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..d64fd0806db36db6883190dd11ddaf19f0aeef79 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 8000 +lr = 0.0005 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/info.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/loss.csv b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..8979fa2515d83e06645fe5b75018da3c9c575fb4 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8016ba1e7b537e6cc0f0a3db93240b1aafcd7d44e98f073b15fd79565ed080a +size 1989 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/model.pt b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..5b15178e15ca2971b8385508f2126acab902ad20 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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https://git-lfs.github.com/spec/v1 +oid sha256:4d886843627c3d1c9944174e0049dcf604cfa5b0b8e0631b955a75ab0e30bc7d +size 31472 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/public_gate/normalized_schema_snapshot.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + 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"unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184500/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/tabddpm-n3-3918-20260504_184535.csv b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/tabddpm-n3-3918-20260504_184535.csv new file mode 100644 index 0000000000000000000000000000000000000000..589375cd23bc4433233c0dd2c0401a6cb49dc844 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/tabddpm-n3-3918-20260504_184535.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c738146e7f0d047733ec58eb2dc833972910c549be56e4e0bd8360335a99e691 +size 772165 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/train_20260504_184500.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/train_20260504_184500.log new file mode 100644 index 0000000000000000000000000000000000000000..d8ecc0d432acd0e9dfcffade25b13b3a3d1ded3c --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184500/train_20260504_184500.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d2ebb14e4aaf1af57a705b7461578273c89585d3856864bdc6f3c11d01806d1 +size 1376 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/_tabddpm_sample_r0.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..22223c410528a9b46734a68a73d3503c54008315 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 3918 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/config_sample_20260504_184713_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/tabddpm-n3-3918-20260504_184713.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/tabddpm-n3-3918-20260504_184713.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/_tabddpm_train.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e31d95d63228dca3c45877066cb1ac29b3603b99 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/config.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..779fa03732fc91343c130fc684402248af34c5ae --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 10000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/config_sample_20260504_184713_r0.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/config_sample_20260504_184713_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..dac8ec5960f7c255bfd9b6281ebef9ab8d4da82b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/config_sample_20260504_184713_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 10000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 128 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/X_num_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..94beb8413ef55592710518365965f4cb503523ee --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:093d8f405d764277d5a007953f25ab6466f0857f71f0d8f3c437463fb5bf240a +size 172520 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/info.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/y_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/gen_20260504_184713_r0.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/gen_20260504_184713_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..039021eb16b21c0088d6fbeaa1e396c97ae10edb --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/gen_20260504_184713_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3c1b66cf54a57ca038219695acb133523dc7de2251d2519655bda51585370d7 +size 651503 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/input_snapshot.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..34d3cbb5018ded64e57cf3350f6ec58dd908f565 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": 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0000000000000000000000000000000000000000..b0006327f14608f8d9037a09ee4f5ccd23c834d0 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/output/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6112bcf98ee3e4ea784a8e43501b738637bdac886f69e13264fdcd702dbb411f +size 344912 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/output/X_num_unnorm.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/output/X_num_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..7cdca64b4674f3d108905f6fa184d0715114cf0d --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/output/X_num_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8354dda9123fb20fbda3ccf6e6b5c3aaeaa9582be7f7c8b212053baad6458613 +size 344912 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/output/config.toml 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+cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 128 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/output/info.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/output/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ 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"data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/public/test.csv b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/public/train.csv 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a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/adapter_report.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3a5fa5d2b72f00e978c96f8029f6e195fec3bcd4 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/adapter_transforms_applied.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/model_input_manifest.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5e801b7249ee40c702769fb596334bbdc74c95a9 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "tabddpm", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184631/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/tabddpm-n3-3918-20260504_184713.csv b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/tabddpm-n3-3918-20260504_184713.csv new file mode 100644 index 0000000000000000000000000000000000000000..7d37ad7c2a899238eb2d9dc05bcd00e382123961 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/tabddpm-n3-3918-20260504_184713.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8dec6b5138fe85288e1f0b1093ba53b45849a31e11a535a2116f7070129fca6a +size 770496 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/train_20260504_184631.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/train_20260504_184631.log new file mode 100644 index 0000000000000000000000000000000000000000..1393d16faae9e60f0a8c234135e029e70b8b3c37 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184631/train_20260504_184631.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:939ef00007bc6a9126a8313ef3d0dfb9322182e66867bbf806e9e4b8ee7f93f0 +size 1607 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/_tabddpm_sample_r0.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..773c85d52c6bac7073f7b84b780307b4448faa21 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 3918 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/config_sample_20260504_184906_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/tabddpm-n3-3918-20260504_184906.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/tabddpm-n3-3918-20260504_184906.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/_tabddpm_train.py b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0f21e12f7103c68aaf6fde4b2afab3f3717aad4b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/config.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..c0d5d3cd28b7ac59bea0ad490f1e28c3f84aa8f7 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 15000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 64 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/config_sample_20260504_184906_r0.toml b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/config_sample_20260504_184906_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..dfb5468a6bdeb8cbd90eb128d417f4b4782c4993 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/config_sample_20260504_184906_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260504_184809/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 15000 +lr = 0.0001 +weight_decay = 0.0 +batch_size = 64 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/data/X_num_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..94beb8413ef55592710518365965f4cb503523ee --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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--git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/data/y_train.npy b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/gen_20260504_184906_r0.log b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/gen_20260504_184906_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..7c17c34b51563fe343aa52d67cd65e0ea7b67fcd --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/gen_20260504_184906_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 64 +seed = 0 diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/output/info.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/output/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ No 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of file diff --git a/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/public_gate/public_gate_report.json b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..93b97319f739e39079d6c6549fd2020f8a6dac25 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "n3", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "quality", + "task_type": "classification", 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0000000000000000000000000000000000000000..41bdef814edcd2e85f5c478beb140680b0fb1356 --- /dev/null +++ b/hyperparameter/n3/tabddpm/tabddpm-n3-20260504_184809/train_20260504_184809.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c309e5eb27853cfdd680d35dc8fce60aa38aa78273bb1e9bed032d07c95a625 +size 2145 diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/_tabdiff_gen.py b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..f8dbc34c2bea2904feb0db610698b50fc933048b --- /dev/null +++ b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203107/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203107/tabdiff-n3-3918-20260504_203314.csv") diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/_tabdiff_train.py b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..61b1adab6bba6a907317207e9bfa3d5762a76ba5 --- /dev/null +++ b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203107/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "100" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/gen_20260504_203314.log b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/gen_20260504_203314.log new file mode 100644 index 0000000000000000000000000000000000000000..ef75d9c7943c7c51bfe0315e287320300616b4c9 --- /dev/null +++ b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203107/gen_20260504_203314.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:87e522949d9669bd7e1504aa91637c180f3ef03e4b497a9eef14945336710d41 +size 5857 diff --git 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b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203341/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203341/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203341/tabdiff-n3-3918-20260504_203751.csv") diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203341/_tabdiff_train.py b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203341/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6bfbabbf61d058cce967fcb105bb20f249bb0222 --- /dev/null +++ b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203341/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203341/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "200" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203341/gen_20260504_203751.log b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203341/gen_20260504_203751.log new 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b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203818/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203818/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203818/tabdiff-n3-3918-20260504_204749.csv") diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203818/_tabdiff_train.py b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203818/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ff5768e9901fcb12387f5548ad2f7fad92bea7fb --- /dev/null +++ b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203818/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_203818/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203818/gen_20260504_204749.log b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_203818/gen_20260504_204749.log new 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b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_204815/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_204815/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_800.pt", + "--num_samples_to_generate", str(int(3918)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_204815/tabdiff-n3-3918-20260504_210307.csv") diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_204815/_tabdiff_train.py b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_204815/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7625b1d6d9fc8659730fbdc31046bbb43ae70440 --- /dev/null +++ b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_204815/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_204815/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "800" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_204815/gen_20260504_210307.log b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_204815/gen_20260504_210307.log new 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b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_210335/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_210335/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_800.pt", + "--num_samples_to_generate", str(int(3918)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_210335/tabdiff-n3-3918-20260504_212137.csv") diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_210335/_tabdiff_train.py b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_210335/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..595a4a48f43b300ead9ba8f73084005f2094969a --- /dev/null +++ b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_210335/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260504_210335/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "1000" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_210335/gen_20260504_212137.log b/hyperparameter/n3/tabdiff/tabdiff-n3-20260504_210335/gen_20260504_212137.log new 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b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv") +target_col = "quality" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(3918) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv") diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/gen_20260505_030704.log b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/gen_20260505_030704.log new file mode 100644 index 0000000000000000000000000000000000000000..8de6e15214b0fee5ba9080b620c29fc6c112539b --- /dev/null +++ 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+ "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/public_gate/staged_input_manifest.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..fcd4bdfad0ef880c03ded4db25c224c434e0279f --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv", + "val_csv": 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a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/run_config.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..070622a0c0a686a1b673be9275deb3ecc8dcc6f4 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T03:07:03", + "dataset_id": "n3", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "n3", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 3918, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/staged_features.json", + "target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "512", + "TABPFGEN_GEN_CHUNK_ROWS": "64" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/runtime_result.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..65073f45c38b3f3cac5a93de0e7c1808ab7f4fc6 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "tabpfgen", + "run_id": "tabpfgen-n3-20260505_030703", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703" + }, + "timings": { + "train": { + "started_at": "2026-05-05T03:07:03", + "ended_at": "2026-05-05T03:07:04", + "duration_sec": 0.011 + }, + "generate": { + "started_at": "2026-05-05T03:07:04", + "ended_at": "2026-05-05T03:09:21", + "duration_sec": 137.051 + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/staged_features.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/test.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/val.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/adapter_report.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..50f844568a27da95424896d20209208f990124dc --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/adapter_transforms_applied.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/model_input_manifest.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9d2cde47fa58c739f31636dc3bacfc7f3287a646 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "tabpfgen", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv new file mode 100644 index 0000000000000000000000000000000000000000..d445c8972e8cefddef213047e137239e208ace34 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e466a3725c54ec09c3445db011b1102ba6aadbfb93a4f8587a10ad9c0aa6dfca +size 445891 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen_meta.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..4b6bee16b6a24d089f9af0581542db75fc475974 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/public/staged_features.json", + "target_col": "quality", + "is_classification": true, + "task_type": "classification", + "n_rows": 3918, + "n_cols": 12 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/train_20260505_030704.log b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/train_20260505_030704.log new file mode 100644 index 0000000000000000000000000000000000000000..bbb19ccc48df6b982cbd4a06021a975ee8a4c85e --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030703/train_20260505_030704.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f6aa81846670ef3092d4bed627769667e552d01a2a80858f4ea88e6490d75c9 +size 595 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/_tabpfgen_generate.py b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c8a5ff1ec27cd1a49e89fa91a0cbe66f97e4cc23 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/train.csv") +target_col = "quality" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(3918) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv") diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/gen_20260505_030933.log b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/gen_20260505_030933.log new file mode 100644 index 0000000000000000000000000000000000000000..b78a1c20d0db8a062638cd3ff71a3eb8222211a5 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/gen_20260505_030933.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e1f09702297bf218083834c86ad2eee695983e0b79ffbc6ff86703486cebc69 +size 12291 diff --git 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b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + 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b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/run_config.json @@ -0,0 +1,43 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-05T03:09:33", + "dataset_id": "n3", + "model": "tabpfgen", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabpfgen", + "dataset": "n3", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 3918, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/tabpfgen/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/staged_features.json", + "target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABPFGEN_GPUS": "device=3", + "TABPFGEN_DEVICE": "cuda", + "TABPFGEN_FIT_MAX_ROWS": "1024", + "TABPFGEN_GEN_CHUNK_ROWS": "64" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/runtime_result.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..0e11b50ce37d39202465324dab5e47dd6c93249b --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "tabpfgen", + "run_id": "tabpfgen-n3-20260505_030933", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933" + }, + "timings": { + "train": { + "started_at": "2026-05-05T03:09:33", + "ended_at": "2026-05-05T03:09:33", + "duration_sec": 0.01 + }, + "generate": { + "started_at": "2026-05-05T03:09:33", + "ended_at": "2026-05-05T03:11:46", + "duration_sec": 132.574 + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/staged_features.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/test.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/train.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/val.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/tabpfgen/adapter_report.json 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}, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv new file mode 100644 index 0000000000000000000000000000000000000000..85f7fa1bc1bd6e156ca8a96bb60ee00d40b0475a --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen-n3-3918-20260505_030933.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:caf5bd4c9a34c5cdd7247518c4ae44a10546ab5dfd42ca28f089c3f846acf3ba +size 446157 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen_meta.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..e212cde402bc2954ba5de7e596e178c873afd683 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_030933/staged/public/staged_features.json", + "target_col": "quality", + "is_classification": true, + "task_type": "classification", + "n_rows": 3918, + "n_cols": 12 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/train_20260505_030933.log b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/train_20260505_030933.log new file mode 100644 index 0000000000000000000000000000000000000000..bc61015a67e2f46e55ceafdb46051850c15de356 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_030933/train_20260505_030933.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed2a8aff94b8464e9b1bef7fd510e13f0a6651a918da9aa0b4956fc424cc35b2 +size 595 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/_tabpfgen_generate.py b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d07fb2a60c4a359dc9ba07b26c2e82bd3ddccb4 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/public/train.csv") +target_col = "quality" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(3918) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/tabpfgen-n3-3918-20260505_031200.csv", index=False) +print(f"[TabPFGen] Saved 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"adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/tabpfgen/adapter_transforms_applied.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/tabpfgen/model_input_manifest.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 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"example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/tabpfgen-n3-3918-20260505_031200.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/tabpfgen-n3-3918-20260505_031200.csv new file mode 100644 index 0000000000000000000000000000000000000000..57e3b5f32c37656e664a0ddb9f5ec51c777fb46c --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/tabpfgen-n3-3918-20260505_031200.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78594120111c2d3bdd9ed921f6cdb156b62ca02ad5b68e39e34a6787aa3996c5 +size 446123 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/tabpfgen_meta.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..f18ff508699e83b1e81c22f19a55c3e502fb3c59 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/staged/public/staged_features.json", + "target_col": "quality", + "is_classification": true, + "task_type": "classification", + "n_rows": 3918, + "n_cols": 12 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/train_20260505_031200.log b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/train_20260505_031200.log new file mode 100644 index 0000000000000000000000000000000000000000..3a1312dd409d3c477950b7a64690e1eb2c96aa02 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031200/train_20260505_031200.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:18994d1a15bc7eb4fdaa75fa42d160430c7255623cfe8cb04b79006e4883b4b1 +size 595 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/_tabpfgen_generate.py b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e2c2d0ecd70717fd7cce358679394d94fc519401 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031328/staged/public/train.csv") +target_col = "quality" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(3918) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031328/tabpfgen-n3-3918-20260505_031328.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031328/tabpfgen-n3-3918-20260505_031328.csv") diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/gen_20260505_031328.log b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/gen_20260505_031328.log new file mode 100644 index 0000000000000000000000000000000000000000..9221472689c18f5261cf92a41c0ab4e9a0d41e32 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/gen_20260505_031328.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e2b1654b46197080f82e7953a868ed274f3fc10bbbe1fd7490e5e7cc2231272 +size 6595 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/input_snapshot.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..2df53dbad689923eb0692c0029f1d32d625f8ce4 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031328/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031328/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/tabpfgen-n3-3918-20260505_031328.csv b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/tabpfgen-n3-3918-20260505_031328.csv new file mode 100644 index 0000000000000000000000000000000000000000..68018de20a2b84aa5f219dece747a9bd4d5b3c27 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/tabpfgen-n3-3918-20260505_031328.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3352bbcdfedb99b5dfe6378669c20435f155da2df288d078b66974ddd865100d +size 446100 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/tabpfgen_meta.json b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..3c4bcbac2dc2c98e015bec62274c235f64aa1f70 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031328/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031328/staged/public/staged_features.json", + "target_col": "quality", + "is_classification": true, + "task_type": "classification", + "n_rows": 3918, + "n_cols": 12 +} \ No newline at end of file diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/train_20260505_031328.log b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/train_20260505_031328.log new file mode 100644 index 0000000000000000000000000000000000000000..18958eda9e4118e61bff9134c0446e11b57d3798 --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031328/train_20260505_031328.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ffc37edcc5b5c720942da0f533f8d2d03409fbfec52caaa2e88ebe6b8b9c64ab +size 595 diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031455/_tabpfgen_generate.py b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031455/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..97bf83a2a689f12cf5fc7191f6b56cdf6d98eadf --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031455/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031455/staged/public/train.csv") +target_col = "quality" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(3918) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031455/tabpfgen-n3-3918-20260505_031455.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031455/tabpfgen-n3-3918-20260505_031455.csv") diff --git a/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031455/gen_20260505_031455.log b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031455/gen_20260505_031455.log new file mode 100644 index 0000000000000000000000000000000000000000..a72b4a73b597b85444a96e3631082ff5b337ab8b --- /dev/null +++ b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031455/gen_20260505_031455.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b59a6709891444a8c337dece738fa9daddd940e2aae7a77c2fbf69814c471f1f +size 3671 diff --git 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b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031455/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + 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b/hyperparameter/n3/tabpfgen/tabpfgen-n3-20260505_031455/train_20260505_031455.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93ed99ac49745e2019fa06331263a9a9e4f30ad0ac4215247dcb64fcce94ab6e +size 595 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_182933/_tabsyn_sample.py b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_182933/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..2e14d31073679496fd712a3246f2483f84986dc9 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_182933/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_182933" +dataname = "tabsyn_n3_tabsyn_n3_20260505_182933" +output_csv = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_182933/tabsyn-n3-3918-20260505_182945.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 3918 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_182933/_tabsyn_train.py b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_182933/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c05b5ef6cb70ba8c19ef2660e745ad48735777a0 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_182933/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_182933" +dataname = "tabsyn_n3_tabsyn_n3_20260505_182933" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git 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0000000000000000000000000000000000000000..c8e0508357378832871c1265bd82333b59fef093 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183003" +dataname = "tabsyn_n3_tabsyn_n3_20260505_183003" +output_csv = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183003/tabsyn-n3-3918-20260505_183016.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 3918 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/_tabsyn_train.py b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8fd4879f07f14e6350ff9edea716386b1cad6df2 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183003" +dataname = "tabsyn_n3_tabsyn_n3_20260505_183003" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/data/tabsyn_n3_tabsyn_n3_20260505_183003/X_cat_test.npy b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/data/tabsyn_n3_tabsyn_n3_20260505_183003/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..24e2b0be40b07b6e79f803a82e6e38723e462320 --- /dev/null +++ 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--git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183003/train_z.npy b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183003/train_z.npy new file mode 100644 index 0000000000000000000000000000000000000000..9a738f2ba3ff3ef54148df59b3bd99d8b8326ebb --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183003/train_z.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9d2066afe0d723e42f0e7202946cacb176a95a79eb3a13e58c7141ba117de20e +size 815072 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/train_20260505_183003.log b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/train_20260505_183003.log new file mode 100644 index 0000000000000000000000000000000000000000..f64f0e6651908cfad6d52335c5a16d7fbf073118 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183003/train_20260505_183003.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4292e807174eeefe8fb52687f01d2cece1cd4203f90e8c9ff85f7578a616680d +size 6142 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183033/_tabsyn_sample.py b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183033/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..ffb6c70207b59258251571ef3959cdf0568df551 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183033/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183033" +dataname = "tabsyn_n3_tabsyn_n3_20260505_183033" +output_csv = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183033/tabsyn-n3-3918-20260505_183047.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 3918 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183033/_tabsyn_train.py b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183033/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3f0c829160dd7e9885821375b9cca38b81ff4d87 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183033/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183033" +dataname = "tabsyn_n3_tabsyn_n3_20260505_183033" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git 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0000000000000000000000000000000000000000..0915b4892edc230c0550cf78d96a5647facfda60 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183104" +dataname = "tabsyn_n3_tabsyn_n3_20260505_183104" +output_csv = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn-n3-3918-20260505_183118.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 3918 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/_tabsyn_train.py b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b2e7ea1cb9d3660d0f9f411a28c64e7b07ce5252 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183104" +dataname = "tabsyn_n3_tabsyn_n3_20260505_183104" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/data/tabsyn_n3_tabsyn_n3_20260505_183104/X_cat_test.npy b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/data/tabsyn_n3_tabsyn_n3_20260505_183104/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..24e2b0be40b07b6e79f803a82e6e38723e462320 --- /dev/null +++ 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sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/public/val.csv b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/adapter_report.json b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..27b3ce8384aea01bca85479fe8e353d8b5891df1 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/adapter_transforms_applied.json b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/model_input_manifest.json b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1ba122b59ad4124b41e7ccc44b586f64e89bc3ee --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "tabsyn", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183104/public_gate/staged_input_manifest.json", + "train_csv": 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b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/synthetic/tabsyn_n3_tabsyn_n3_20260505_183104/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/synthetic/tabsyn_n3_tabsyn_n3_20260505_183104/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/synthetic/tabsyn_n3_tabsyn_n3_20260505_183104/test.csv b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/synthetic/tabsyn_n3_tabsyn_n3_20260505_183104/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/synthetic/tabsyn_n3_tabsyn_n3_20260505_183104/test.csv @@ -0,0 +1,3 @@ +version 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sha256:4e94769de0f06ab51de5ff258f7ac4b151372dc06cae8461caeb00708d82fb9f +size 42374969 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183104/decoder.pt b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183104/decoder.pt new file mode 100644 index 0000000000000000000000000000000000000000..636274b5614c8cc084aac7beb3d554320ecbfcdb --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183104/decoder.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3eaa849d9fb53bde8995cb6fb397031faf23418c3d1a5aec898950b2ee57c775 +size 21318 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183104/encoder.pt b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183104/encoder.pt new file mode 100644 index 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--git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183104/train_z.npy b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183104/train_z.npy new file mode 100644 index 0000000000000000000000000000000000000000..624ffd338c44388442f88bb3f7defcdfbd9bdd0e --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/tabsyn_ckpt/vae/tabsyn_n3_tabsyn_n3_20260505_183104/train_z.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0e8878a3e253b1c287ecdea7fac3ecf9625662b76bd7bbd510aab28c61ed0be +size 815072 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/train_20260505_183104.log b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/train_20260505_183104.log new file mode 100644 index 0000000000000000000000000000000000000000..58e47222ca566bae6a201f5477c1d5f22f07f84e --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183104/train_20260505_183104.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24b2901e9306f21cf31ee6adcbb5d6f96042227bda8e4577dfd48480dadc941f +size 8761 diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183135/_tabsyn_sample.py b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183135/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..9f8ed3522e638e55e41982bcdd5757c6e9db2743 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183135/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183135" +dataname = "tabsyn_n3_tabsyn_n3_20260505_183135" +output_csv = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183135/tabsyn-n3-3918-20260505_183149.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 3918 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183135/_tabsyn_train.py b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183135/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4d37fa2ce2bdf9a57e35a17522dc4ac6d1ab81d2 --- /dev/null +++ b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183135/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260505_183135" +dataname = "tabsyn_n3_tabsyn_n3_20260505_183135" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git 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"0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183135/public_gate/public_gate_report.json b/hyperparameter/n3/tabsyn/tabsyn-n3-20260505_183135/public_gate/public_gate_report.json new file mode 100644 index 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0000000000000000000000000000000000000000..c7b9f5dbe7772cae78842d3e657dde8a6274ea8e --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_172819/_tvae_train.py b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7c7be901c03a6afbdafae5c9fa0fdb9cbe3a66b9 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_172819/gen_20260504_172901.log b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/gen_20260504_172901.log new file mode 100644 index 0000000000000000000000000000000000000000..9f4c8df978304685fee287c97778601fa7ccd326 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/gen_20260504_172901.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a43efa6944e23c4b948b6dcbdb07c34fa3696ecc216dd410142b5a766c516a40 +size 404 diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_172819/input_snapshot.json b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddd4c8dc6c3d918927aad5bcb5dbb27f78068fe --- /dev/null +++ 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv new file mode 100644 index 0000000000000000000000000000000000000000..ce98bd29f56845d8d881c8f81d574afb9ea1db39 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1705f92cdce4d8ba19f5c7cbe746d70c8efc8448d05c10bc2f7845b2ac2570b +size 820144 diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175727/_tvae_generate.py b/hyperparameter/n3/tvae/tvae-n3-20260504_175727/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4f19e5323617cc73049297e90dab025fa598fb8d --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175727/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175727/_tvae_train.py b/hyperparameter/n3/tvae/tvae-n3-20260504_175727/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5dd280a8b9572c97a00b926438610b5dcbd8f601 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175727/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175727/gen_20260504_175758.log 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv b/hyperparameter/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv new file mode 100644 index 0000000000000000000000000000000000000000..0a57c670225b053fefa39b1041779c74e85b01fd --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dbe1d4fdb0a9c6941467d3a92e4d25426d59c01623a0623cfcdef6d4fe7f84b9 +size 820357 diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json b/hyperparameter/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175816/_tvae_generate.py b/hyperparameter/n3/tvae/tvae-n3-20260504_175816/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..24ca6e385d540d624017847f47ba243f2999bd56 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175816/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/tvae-n3-3918-20260504_175903.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/tvae-n3-3918-20260504_175903.csv") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175816/_tvae_train.py b/hyperparameter/n3/tvae/tvae-n3-20260504_175816/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6a0e13d23bea670f006bdee5b27f5ed16ac0aace --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175816/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175816/gen_20260504_175903.log 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"target_column": "quality", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175816/public_gate/staged_input_manifest.json b/hyperparameter/n3/tvae/tvae-n3-20260504_175816/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..795108ea10aec7b6850ee89aa206d4217dce08d3 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175816/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": 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0000000000000000000000000000000000000000..b7c5471b4f309613a61d50a47fc13bb5ba9a7bf0 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175920/_tvae_train.py b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..67d99ebd85e50e12aca0f56191b29ca86c4abf8f --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log new file mode 100644 index 0000000000000000000000000000000000000000..6c444db1593eca9af73a52c23d9523948af9cfb9 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3258505060bad9eddfae5d88bef19d324c0af15ebd939152edb79eb981d1a7f2 +size 404 diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175920/input_snapshot.json b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddd4c8dc6c3d918927aad5bcb5dbb27f78068fe --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": 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"c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175920/models_300epochs/train_20260504_175920.log b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/models_300epochs/train_20260504_175920.log new file mode 100644 index 0000000000000000000000000000000000000000..bb635666427be4bc6541bc02b0980803dc26081b --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/models_300epochs/train_20260504_175920.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3246fcae64caa9d1f197b3c707eb5cbb869dae2b365054835064835b12f15cc4 +size 621 diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..293036634de2b5a65e841089d896f1a4976dacbe --- /dev/null +++ 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv new file mode 100644 index 0000000000000000000000000000000000000000..10ad6aea130216e24134b4f882c5fe5f1a9f8431 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8fbb4d91efeeeb1d7eeebb166c9f1c4576bf4e0bea0ff15c6104fdfade300582 +size 820263 diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_175920/tvae_metadata.json b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_175920/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180018/_tvae_generate.py b/hyperparameter/n3/tvae/tvae-n3-20260504_180018/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4f9cf621d756127ba56d81b5d03a897854c6fb39 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180018/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180018/_tvae_train.py b/hyperparameter/n3/tvae/tvae-n3-20260504_180018/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bd01c88cdd31ec29e1fb2cdeff82715de5186d16 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180018/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180018/gen_20260504_180152.log 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0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv b/hyperparameter/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv new file mode 100644 index 0000000000000000000000000000000000000000..361f8e7dfa48053c9c11f830e964d2d281babccc --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7ddf27d7e67c91b0cc4320fac6c059beb9de77910a2c9914535b5db5a19fccc +size 820283 diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180018/tvae_metadata.json b/hyperparameter/n3/tvae/tvae-n3-20260504_180018/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180018/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/_tvae_generate.py b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e0bb2d7e7d2aefffd85bee311158c71d512b1134 --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/_tvae_train.py b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..99aa02f81dd2ad71f400e0409301e5bc93d9ceaf --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/gen_20260504_180345.log 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"feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/public/test.csv b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git 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diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/adapter_report.json b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..4bf0f97ba6c93866503b3df927d84938ef842bfd --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/adapter_transforms_applied.json b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/model_input_manifest.json b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..e7ef2aa9b33d3f5c50fa61424e02c8bf5e16cd5d --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/staged/tvae/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + 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+ "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb4001a56167f759b7d62206402096f406caea1e --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c5849ac70605215db2f49695e46e4d45a6af7caa95083a86a1ba1d6ccc8f579 +size 820300 diff --git a/hyperparameter/n3/tvae/tvae-n3-20260504_180209/tvae_metadata.json b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/hyperparameter/n3/tvae/tvae-n3-20260504_180209/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/c14/arf/arf-c14-20260501_225126/_arf_generate.py b/timecost/c14/arf/arf-c14-20260501_225126/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..a6b45137c4a69916ec4b23188e77a5850a66c2c0 --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(240000) +c_csv = "/work/output-Benchmark-trainonly-v1/c14/arf/arf-c14-20260501_225126/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c14/arf/arf-c14-20260501_225126/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c14' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c14/arf/arf-c14-20260501_225126/arf-c14-240000-20260501_232038.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c14/arf/arf-c14-20260501_225126/arf-c14-240000-20260501_232038.csv") diff --git a/timecost/c14/arf/arf-c14-20260501_225126/_arf_train.py b/timecost/c14/arf/arf-c14-20260501_225126/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6048d85e041385612cf27641afe8e6300bc49dbd --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/arf/arf-c14-20260501_225126/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c14/arf/arf-c14-20260501_225126/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c14/arf/arf-c14-20260501_225126/arf_model.pkl") diff --git a/timecost/c14/arf/arf-c14-20260501_225126/arf-c14-240000-20260501_232038.csv b/timecost/c14/arf/arf-c14-20260501_225126/arf-c14-240000-20260501_232038.csv new file mode 100644 index 0000000000000000000000000000000000000000..5d76953dcac501ec7e6862d597907b06db64a004 --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/arf-c14-240000-20260501_232038.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc9ce89ed6aa94384dcdc49316311677a01999649421a88fff66b6183fb5a464 +size 55594271 diff --git a/timecost/c14/arf/arf-c14-20260501_225126/arf_model.pkl b/timecost/c14/arf/arf-c14-20260501_225126/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..07e1bebc8216b01afedee1cbf74e8fc769370644 --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3cb31c233283b40de4a37467392d5f5a6e5f1afa46a7150d6f1e3f9d38bdd500 +size 4661503915 diff --git 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}, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "target", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c14/arf/arf-c14-20260501_225126/public_gate/staged_input_manifest.json b/timecost/c14/arf/arf-c14-20260501_225126/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6ce71a388ab413835e966034b60111c3e9d018aa --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/public_gate/staged_input_manifest.json @@ -0,0 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"feature_name": "bin_4", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "nom_0", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_1", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_2", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_3", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_4", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_5", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_6", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_7", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_8", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_9", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "ord_0", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ord_1", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "ord_2", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "ord_3", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "ord_4", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "ord_5", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "day", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "month", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "target", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c14/arf/arf-c14-20260501_225126/staged/public/test.csv b/timecost/c14/arf/arf-c14-20260501_225126/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..afbef1e22ba9597cf509fb91aa271ff6908ca0a9 --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:617ecb620416375c67546e0fa5a8b9a3923d689bda904dc23824ea175d1f8597 +size 3965839 diff --git a/timecost/c14/arf/arf-c14-20260501_225126/staged/public/train.csv b/timecost/c14/arf/arf-c14-20260501_225126/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..322e9de210d736baf266fa4930517b3827d3bf63 --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12398ccb68a2499b9c126f61b25f03c36a65f026d755e1d5ff5653e614676167 +size 31717079 diff --git a/timecost/c14/arf/arf-c14-20260501_225126/staged/public/val.csv b/timecost/c14/arf/arf-c14-20260501_225126/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..594db5d9c0a27ce284d599b8bf62685189510e61 --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f40dbd7bbd9e2783c24d0d12ec35a85581835d45238774eb0676de44a734feda +size 3965919 diff --git a/timecost/c14/arf/arf-c14-20260501_225126/train_20260501_225128.log b/timecost/c14/arf/arf-c14-20260501_225126/train_20260501_225128.log new file mode 100644 index 0000000000000000000000000000000000000000..1ed700fffb4db3bd27f093016e39af0553ba69e2 --- /dev/null +++ b/timecost/c14/arf/arf-c14-20260501_225126/train_20260501_225128.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bdb494e9811dd3873e941a70b73c79f0070b00e12cc5e392cbc533587a749759 +size 503 diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/_bayesnet_generate.py b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4635dd4bfb9b04d136ab1ccb5ef6434f8eda3f5d --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(240000) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet-c14-240000-20260501_232624.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet-c14-240000-20260501_232624.csv") diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/_bayesnet_train.py b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f765ef6cd460e4a7cf4870b4bb85698d59f78119 --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/bayesnet/bayesnet-c14-20260501_232608/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_model.pkl") diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet-c14-240000-20260501_232624.csv b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet-c14-240000-20260501_232624.csv new file mode 100644 index 0000000000000000000000000000000000000000..c2418d86e92bec9240c8d67b7cc1a4b8edb8b3c6 --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet-c14-240000-20260501_232624.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aa4811bed2557c8413d8b6be76ea82df525c17a5536229f9fd03daef708f9088 +size 47204687 diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_coltypes.json b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..85df79de4590cc7794f243fbb6597bd9e3cd2c2c --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_coltypes.json @@ -0,0 +1,105 @@ +{ + "columns": [ + { + "name": "id", + "type": "continuous" + }, + { + "name": "bin_0", + "type": "categorical" + }, + { + "name": "bin_1", + "type": "categorical" + }, + { + "name": "bin_2", + "type": "categorical" + }, + { + "name": "bin_3", + "type": "categorical" + }, + { + "name": "bin_4", + "type": "categorical" + }, + { + "name": "nom_0", + "type": "categorical" + }, + { + "name": "nom_1", + "type": "categorical" + }, + { + "name": "nom_2", + "type": "categorical" + }, + { + "name": "nom_3", + "type": "categorical" + }, + { + "name": "nom_4", + "type": "categorical" + }, + { + "name": "nom_5", + "type": "categorical" + }, + { + "name": "nom_6", + "type": "categorical" + }, + { + "name": "nom_7", + "type": "categorical" + }, + { + "name": "nom_8", + "type": "categorical" + }, + { + "name": "nom_9", + "type": "categorical" + }, + { + "name": "ord_0", + "type": "continuous" + }, + { + "name": "ord_1", + "type": "categorical" + }, + { + "name": "ord_2", + "type": "categorical" + }, + { + "name": "ord_3", + "type": "categorical" + }, + { + "name": "ord_4", + "type": "categorical" + }, + { + "name": "ord_5", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "continuous" + }, + { + "name": "target", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_model.pkl b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..f026f8773d8edfa9c1b93701258d7a3b7be86960 --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:074778d5a61cf4a8caad7fd4ab35106eb271707e99573b94594d2e195a19f9e6 +size 251724 diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/const_cols.json b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/gen_20260501_232624.log b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/gen_20260501_232624.log new file mode 100644 index 0000000000000000000000000000000000000000..405b53361cf84e82938781b594a8977dd31144b6 --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/gen_20260501_232624.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ef23e09cfb65a3edd951d40d3fa0a020d465424c9dde13228e08bdca140552f +size 3671 diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/input_snapshot.json b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c85b80e36320546050eb77482afc4a17a5651efb --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", 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b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12398ccb68a2499b9c126f61b25f03c36a65f026d755e1d5ff5653e614676167 +size 31717079 diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/staged/public/val.csv b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..594db5d9c0a27ce284d599b8bf62685189510e61 --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f40dbd7bbd9e2783c24d0d12ec35a85581835d45238774eb0676de44a734feda +size 3965919 diff --git a/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/train_20260501_232611.log b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/train_20260501_232611.log new file mode 100644 index 0000000000000000000000000000000000000000..c0fc19cfba5d67c4efbbf9a270c88e824e6da7ef --- /dev/null +++ b/timecost/c14/bayesnet/bayesnet-c14-20260501_232608/train_20260501_232611.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2ae9f8db5a15c6148385194ff5ab45561342d3e84aa3c61b0da51c2edd424e4c +size 3856 diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/_ctgan_generate.py b/timecost/c14/ctgan/ctgan-c14-20260505_185844/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..93e801f00dbc0095ccbd51b0cfc987cbd34acf38 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/ctgan_5epochs.pt") +total = 240000 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260505_185844/ctgan-c14-240000-20260505_185951.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260505_185844/ctgan-c14-240000-20260505_185951.csv") \ No newline at end of file diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/_ctgan_train.py b/timecost/c14/ctgan/ctgan-c14-20260505_185844/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..807d6cdeedf103239f3bceac24a8bdb4ff8295bb --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260505_185844/ctgan_train_prepared.csv") +discrete_columns = ['bin_0', 'bin_1', 'bin_2', 'bin_3', 'bin_4', 'nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4', 'nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9', 'ord_1', 'ord_2', 'ord_3', 'ord_4', 'ord_5', 'target'] +model = CTGAN( + embedding_dim=4, + generator_dim=(8, 8), + discriminator_dim=(8, 8), + batch_size=2, + pac=1, + epochs=5, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/ctgan_5epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/ctgan_5epochs.pt") \ No newline at end of file diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan-c14-240000-20260505_185951.csv b/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan-c14-240000-20260505_185951.csv new file mode 100644 index 0000000000000000000000000000000000000000..e792ddbc7b811594eb33e2dff9ce3956bb5ea9f0 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan-c14-240000-20260505_185951.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14aec343119ca1c6007276405e7d28344182b68db2e5f81bfd346af9539ddc11 +size 31648248 diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan_metadata.json b/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..43d1d6d3e611ce6b232979c04622470688342e90 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan_metadata.json @@ -0,0 +1,104 @@ +{ + "columns": [ + { + "name": "id", + "type": "continuous" + }, + { + "name": "bin_0", + "type": "categorical" + }, + { + "name": "bin_1", + "type": "categorical" + }, + { + "name": "bin_2", + "type": "categorical" + }, + { + "name": "bin_3", + "type": "categorical" + }, + { + "name": "bin_4", + "type": "categorical" + }, + { + "name": "nom_0", + "type": "categorical" + }, + { + "name": "nom_1", + "type": "categorical" + }, + { + "name": "nom_2", + "type": "categorical" + }, + { + "name": "nom_3", + "type": "categorical" + }, + { + "name": "nom_4", + "type": "categorical" + }, + { + "name": "nom_5", + "type": "categorical" + }, + { + "name": "nom_6", + "type": "categorical" + }, + { + "name": "nom_7", + "type": "categorical" + }, + { + "name": "nom_8", + "type": "categorical" + }, + { + "name": "nom_9", + "type": "categorical" + }, + { + "name": "ord_0", + "type": "continuous" + }, + { + "name": "ord_1", + "type": "categorical" + }, + { + "name": "ord_2", + "type": "categorical" + }, + { + "name": "ord_3", + "type": "categorical" + }, + { + "name": "ord_4", + "type": "categorical" + }, + { + "name": "ord_5", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "continuous" + }, + { + "name": "target", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan_train_prepared.csv b/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan_train_prepared.csv new file mode 100644 index 0000000000000000000000000000000000000000..c1447024fac051a678f65b4e41d8bcf861c25462 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/ctgan_train_prepared.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f135a8a18cdb7d9127d7fa97595cc8eca1ab406b017b2557cf6a5d0c77a52db2 +size 135612 diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/gen_20260505_185951.log b/timecost/c14/ctgan/ctgan-c14-20260505_185844/gen_20260505_185951.log new file mode 100644 index 0000000000000000000000000000000000000000..ab6189ee354205a25903bb62741756f6ed0ae8b0 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/gen_20260505_185951.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a92bcdd2e47f7191f371af40a28340788bf118673cf1bf8bce947bf6dbb4f2cf +size 304 diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/input_snapshot.json b/timecost/c14/ctgan/ctgan-c14-20260505_185844/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..bbac6b99393b3a4ad5ef6f4168ce74f83e798899 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": 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a/timecost/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/ctgan_5epochs.pt b/timecost/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/ctgan_5epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e6d7c4ab5c70869570769bd5508488ada0ece817 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/ctgan_5epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef6cd76e53d96ef3296027c483c275b1158b725e850a6ec5db58a3194e55c9d6 +size 43090359 diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/train_20260505_185849.log b/timecost/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/train_20260505_185849.log new file mode 100644 index 0000000000000000000000000000000000000000..1ec599f2d7e3a05fdd041830ee247100c4430a08 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/models_5epochs/train_20260505_185849.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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of file diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/staged_features.json b/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..508fbe870849f119fe0f440e76f620b91489d965 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/staged_features.json @@ -0,0 +1,127 @@ +[ + { + "feature_name": "id", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "bin_0", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_1", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_2", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_3", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_4", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "nom_0", + "data_type": "categorical", + "is_target": false + }, + { + 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sha256:617ecb620416375c67546e0fa5a8b9a3923d689bda904dc23824ea175d1f8597 +size 3965839 diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/train.csv b/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..322e9de210d736baf266fa4930517b3827d3bf63 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12398ccb68a2499b9c126f61b25f03c36a65f026d755e1d5ff5653e614676167 +size 31717079 diff --git a/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/val.csv b/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..594db5d9c0a27ce284d599b8bf62685189510e61 --- /dev/null +++ b/timecost/c14/ctgan/ctgan-c14-20260505_185844/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f40dbd7bbd9e2783c24d0d12ec35a85581835d45238774eb0676de44a734feda +size 3965919 diff --git a/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_X_host.npy b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d9d1fd4125f2dcc72c13f995268147e48e25fb1 --- /dev/null +++ b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb7fc43d434cbc1359eb3bff2c07c0281b2bcaacf7cb156a289de90b1d04ed7a +size 102528 diff --git a/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_gen.py b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..3c547aed1483591494f068e9c44037df67576dbc --- /dev/null +++ b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260506_023140/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(240000)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260506_023140/forest-c14-240000-20260506_023514.csv', index=False) +print("saved", len(df)) diff --git a/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_meta_host.json b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..4646e31cc069da465f39a4a0bf0f9ef92fc82aa4 --- /dev/null +++ b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["id", "bin_0", "bin_1", "bin_2", "bin_3", "bin_4", "nom_0", "nom_1", "nom_2", "nom_3", "nom_4", "nom_5", "nom_6", "nom_7", "nom_8", "nom_9", "ord_0", "ord_1", "ord_2", "ord_3", "ord_4", "ord_5", "day", "month", "target"], "cat_indexes": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21]} \ No newline at end of file diff --git a/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_train.py b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bd6d088430e91c8415d686fc196ed7159f3cc4fc --- /dev/null +++ b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260506_023140/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260506_023140/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=2 " + f"n_estimators=1 duplicate_K=1 n_jobs=1 " + f"max_depth=2 xgb_verbosity=0 xgb_nthread=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=2, n_estimators=1, duplicate_K=1, n_jobs=1, + model="xgboost", max_depth=2, tree_method="hist", cat_indexes=cat_indexes, + verbosity=0, nthread=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260506_023140/forestdiffusion_model.joblib') diff --git a/timecost/c14/forestdiffusion/forest-c14-20260506_023140/forest-c14-240000-20260506_023514.csv b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/forest-c14-240000-20260506_023514.csv new file mode 100644 index 0000000000000000000000000000000000000000..848924eb0a54d77a3162acf9125e6728875800d1 --- /dev/null +++ b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/forest-c14-240000-20260506_023514.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27073672cc4198463fbee578c13942d59f2117f38a0cb55d1ee7f56b76cd4e95 +size 38506392 diff --git a/timecost/c14/forestdiffusion/forest-c14-20260506_023140/forestdiffusion_model.joblib b/timecost/c14/forestdiffusion/forest-c14-20260506_023140/forestdiffusion_model.joblib new file mode 100644 index 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"train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/realtabformer/rtf-c14-20260501_010151/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/realtabformer/rtf-c14-20260501_010151/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/realtabformer/rtf-c14-20260501_010151/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/realtabformer/rtf-c14-20260501_010151/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/realtabformer/rtf-c14-20260501_010151/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c14/realtabformer/rtf-c14-20260501_010151/train_20260501_010155.log b/timecost/c14/realtabformer/rtf-c14-20260501_010151/train_20260501_010155.log new file mode 100644 index 0000000000000000000000000000000000000000..0e775321adefeb41175f73f8bddb6c8b9824ca5d --- /dev/null +++ b/timecost/c14/realtabformer/rtf-c14-20260501_010151/train_20260501_010155.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95effa42ea2bd7eee0ab1eed2f1870b9c9b4c7219215424999e534bc789ad72e +size 30169849 diff --git a/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/_tabddpm_sample_r0.py b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..cb06fa579f45d6328251245aec686c3541ab8513 --- /dev/null +++ b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 64 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/config_sample_20260504_065452_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/tabddpm-c14-64-20260504_065452.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/tabddpm-c14-64-20260504_065452.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/_tabddpm_train.py b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..461eed0952d2d42a0430f8109ec4ed8b632b7bae --- /dev/null +++ b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/config.toml b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..c11dc56c739676ebf5e8730a260d3b4fa0f3c568 --- /dev/null +++ b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 20 +gaussian_loss_type = "mse" + +[train.main] +steps = 10 +lr = 0.0002 +weight_decay = 0.0 +batch_size = 32 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 16 +seed = 0 diff --git a/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/config_sample_20260504_065452_r0.toml b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/config_sample_20260504_065452_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..a34a6d1218716339d8b107b13476c7b23534b5de --- /dev/null +++ b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/config_sample_20260504_065452_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260504_065427/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 20 +gaussian_loss_type = "mse" + +[train.main] +steps = 10 +lr = 0.0002 +weight_decay = 0.0 +batch_size = 32 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 64 +batch_size = 16 +seed = 0 diff --git a/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/data/X_cat_train.npy b/timecost/c14/tabddpm/tabddpm-c14-20260504_065427/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..98bd909f2c6e01569b889e7013b55bfd9382349a --- /dev/null +++ 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0000000000000000000000000000000000000000..0f9ebfef3756321bbcd24b6b668cc29cb5d7400a --- /dev/null +++ b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c14" +src = r"/work/output-Benchmark-trainonly-v1/c14/tabdiff/tabdiff-c14-20260501_043512/tabular_bundle/pipeline_c14" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_c14/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(240000)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c14/tabdiff/tabdiff-c14-20260501_043512/tabdiff-c14-240000-20260501_110122.csv") diff --git a/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/_tabdiff_train.py b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..861ee1918484b41730b339e152ef076fbb2301ae --- /dev/null +++ b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c14" +src = r"/work/output-Benchmark-trainonly-v1/c14/tabdiff/tabdiff-c14-20260501_043512/tabular_bundle/pipeline_c14" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/gen_20260501_110122.log b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/gen_20260501_110122.log new file mode 100644 index 0000000000000000000000000000000000000000..fc7438bb2a7359561f0bc17b624a138428de315e --- /dev/null +++ b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/gen_20260501_110122.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63ae109986b1db6a533baca7c7a01c6d739db8319ad5c44f1c94a244c6f79fe1 +size 102718 diff --git a/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/input_snapshot.json b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..53f6264fbf4c03500d7b16d85cd1e1911d1ff1db --- /dev/null +++ b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": 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https://git-lfs.github.com/spec/v1 +oid sha256:0f723b0138e622255ce8db1d27cf9b51554364c371989801193edd4c87fc0921 +size 1920128 diff --git a/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/train_20260501_043526.log b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/train_20260501_043526.log new file mode 100644 index 0000000000000000000000000000000000000000..705e65f7ef7b59c471a0d8fa286b97c30f95561e --- /dev/null +++ b/timecost/c14/tabdiff/tabdiff-c14-20260501_043512/train_20260501_043526.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eef2b3b6305fb5ff348bf714299ad8f786067e06c2ffa611fa6b4698e212d65b +size 9711741 diff --git a/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/_tabpfgen_generate.py b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..2c73ded158bd94698189b4bb08f45b0729c51ca1 --- /dev/null +++ b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_200228/staged/public/train.csv") +target_col = "target" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(240000) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_200228/tabpfgen-c14-240000-20260504_200233.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_200228/tabpfgen-c14-240000-20260504_200233.csv") diff --git a/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/gen_20260504_200233.log b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/gen_20260504_200233.log new file mode 100644 index 0000000000000000000000000000000000000000..21facc85b846e61d7bc26c66496c8a265593e34b --- /dev/null +++ b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/gen_20260504_200233.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:178c4e44408990178105c81fb3d2b512d18d90def7206e3fcf6f4d2d94a03560 +size 177457 diff --git a/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/input_snapshot.json b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..7b3f769e8fd8154b95fc0600df1ab16bb5991aec --- /dev/null +++ b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": "b04d723eeb59d339a356302660463c129141b4213fefbf472ef48f8a4daa50c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-test.csv", + "exists": true, + "size": 3995840, + "sha256": "4780b90e46f6b440983cc9315cad8bca0e86bd26d8877ffae7111258fba0a132" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_profile.json", + "exists": true, + "size": 9505, + "sha256": "577cc5830dcb336243555d3d41c06e2e7c0a63df2a9caccb7a0411b9332cde15" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_contract_v1.json", + "exists": true, + "size": 11901, + "sha256": "955197dff3346d55aaa999adf98d64c375a7e4ca9bc4bcfe626499c9cfea9a41" + } + } +} \ No newline at end of file diff --git a/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/public_gate/normalized_schema_snapshot.json b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c43047a97cac9542e79dfa127e651be7146aa2a4 --- /dev/null +++ b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,509 @@ +{ + "dataset_id": "c14", + "target_column": "target", + "task_type": "classification", + "columns": [ + { + "name": "id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.083333, + "example_values": [ + "182547", + "28342", + "172611", + "81876", + "110780" + ] + } + }, + { + "name": "bin_0", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 8e-06, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "bin_1", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 8e-06, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "bin_2", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 8e-06, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "bin_3", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": 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b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/train_20260504_200233.log new file mode 100644 index 0000000000000000000000000000000000000000..49e5e98071c3b7f583af9c320f7cd7631052d33c --- /dev/null +++ b/timecost/c14/tabpfgen/tabpfgen-c14-20260504_200228/train_20260504_200233.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e230a9a47ea32368229f50ddce0c96f6ae99a399c0f2060b61cdca280b44cdf2 +size 599 diff --git a/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/_tabsyn_sample.py b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..f70a3098b8a6ebb0d28c902664c47e7d5ac5d161 --- /dev/null +++ b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/_tabsyn_sample.py @@ -0,0 +1,43 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c14/tabsyn/tabsyn-c14-20260504_042655" +dataname = "tabsyn_c14" +output_csv = "/work/output-Benchmark-trainonly-v1/c14/tabsyn/tabsyn-c14-20260504_042655/tabsyn-c14-240000-20260504_051538.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 240000 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/_tabsyn_train.py b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..97b66770d55044929b3f19dd3a4e53314f5a4d1a --- /dev/null +++ b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/_tabsyn_train.py @@ -0,0 +1,69 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c14/tabsyn/tabsyn-c14-20260504_042655" +dataname = "tabsyn_c14" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "0") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "32") +env.setdefault("TABSYN_VAE_NUM_WORKERS", "0") +env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"]) +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = 10 +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_cat_test.npy b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..95cc03ccfebc7a71aafef14ec7f38f5844f747fb --- /dev/null +++ b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96fb5a86a8b5c99f151626dee541d9024bac427c393b9ed577c13dbcc6b6832d +size 38400128 diff --git a/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_cat_train.npy b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..95cc03ccfebc7a71aafef14ec7f38f5844f747fb --- /dev/null +++ b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96fb5a86a8b5c99f151626dee541d9024bac427c393b9ed577c13dbcc6b6832d +size 38400128 diff --git a/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_num_test.npy b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c3499d15139a46785b9614e79d47ee35f58034ea --- /dev/null +++ b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3026532dc1e8f61b12f8f829ec618733e69f9da70d40e8234069ff10f3381878 +size 3840128 diff --git a/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_num_train.npy b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/data/tabsyn_c14/X_num_train.npy new file mode 100644 index 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b/timecost/c14/tabsyn/tabsyn-c14-20260504_042655/train_20260504_042709.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00c438a488c258b5b796279d36fcf3b827ce378fc49b7ccdfa1b0d05f4e3d5dc +size 1335790 diff --git a/timecost/c14/tvae/tvae-c14-20260503_194908/_tvae_generate.py b/timecost/c14/tvae/tvae-c14-20260503_194908/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..81fa44ecc3410f3048a21e827fafb1d8bdb7c5fa --- /dev/null +++ b/timecost/c14/tvae/tvae-c14-20260503_194908/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c14/tvae/tvae-c14-20260503_194908/models_300epochs/tvae_300epochs.pt") +total = 240000 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c14/tvae/tvae-c14-20260503_194908/tvae-c14-240000-20260503_201638.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c14/tvae/tvae-c14-20260503_194908/tvae-c14-240000-20260503_201638.csv") diff --git a/timecost/c14/tvae/tvae-c14-20260503_194908/_tvae_train.py b/timecost/c14/tvae/tvae-c14-20260503_194908/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5677a184a4f846c02d0befbc6a3f544c6b316a30 --- /dev/null +++ b/timecost/c14/tvae/tvae-c14-20260503_194908/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c14/tvae/tvae-c14-20260503_194908/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c14/tvae/tvae-c14-20260503_194908/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c14/tvae/tvae-c14-20260503_194908/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c14/tvae/tvae-c14-20260503_194908/gen_20260503_201638.log b/timecost/c14/tvae/tvae-c14-20260503_194908/gen_20260503_201638.log new file mode 100644 index 0000000000000000000000000000000000000000..a30ae65edc0f9213045b415ac10f81807a572977 --- /dev/null +++ b/timecost/c14/tvae/tvae-c14-20260503_194908/gen_20260503_201638.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9a3aad6b866853eed267bd9a32bf13136bd2fc5f63a0f9193196dea7784d0d5 +size 412 diff --git a/timecost/c14/tvae/tvae-c14-20260503_194908/input_snapshot.json b/timecost/c14/tvae/tvae-c14-20260503_194908/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..93b43c8a2b565f767f0cb197b411ec91dc73db2c --- /dev/null +++ b/timecost/c14/tvae/tvae-c14-20260503_194908/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": 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as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(1382) +c_csv = "/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c2' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/arf-c2-1382-20260501_224905.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/arf-c2-1382-20260501_224905.csv") diff --git a/timecost/c2/arf/arf-c2-20260501_224900/_arf_train.py b/timecost/c2/arf/arf-c2-20260501_224900/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ba95402ff696a85b83e57232405ebd09988cad63 --- /dev/null +++ b/timecost/c2/arf/arf-c2-20260501_224900/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/arf_model.pkl") diff --git a/timecost/c2/arf/arf-c2-20260501_224900/arf-c2-1382-20260501_224905.csv b/timecost/c2/arf/arf-c2-20260501_224900/arf-c2-1382-20260501_224905.csv new file mode 100644 index 0000000000000000000000000000000000000000..92d11d02e2729a5d2236986482c133cee53d1736 --- /dev/null +++ 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b/timecost/c2/arf/arf-c2-20260501_224900/train_20260501_224900.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f3d6f58ca9aacbd0711ba48e5d7952028046497adce39d08a4578fb07ea487bb +size 494 diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_generate.py b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..bc85868eb04b1d284cad15328a73bed2db188403 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(1382) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet-c2-1382-20260501_224928.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet-c2-1382-20260501_224928.csv") diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_train.py b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b5233d0b40be8a68f36a1b0f24295a4fce51cf81 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl") diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet-c2-1382-20260501_224928.csv b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet-c2-1382-20260501_224928.csv new file mode 100644 index 0000000000000000000000000000000000000000..fc5d17a270181a71ee29473a8c43adb23eedeabf --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet-c2-1382-20260501_224928.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:22f6707eed5cf85d2d861bbaf9ea58d07579425a0b10b3e9f2778d53283dcccc +size 41603 diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_coltypes.json b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..dc49694e781f314f19b59a568715784a7cabec24 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a41a2cb32a11c469c794a576140511b67c8f7bdb --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9fc1dfaabb7725d9da65e2853e0e7d4de92da7281a7237ef5c6909e01f2a513 +size 4250 diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/const_cols.json b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/gen_20260501_224928.log b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/gen_20260501_224928.log new file mode 100644 index 0000000000000000000000000000000000000000..f92a14d19dd6fae54551330bb6b0a1fcaf1afe45 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/gen_20260501_224928.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1872b2d76a6b2ac6e20c0bf32ee325a73b9e50f5923ed58c381823ff249455f +size 3661 diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/input_snapshot.json b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..85fdc398e0d261ad97c34409134cc4aebb1bb672 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/normalized_schema_snapshot.json b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/public_gate_report.json b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/staged_input_manifest.json b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..63d2cc85814ec597e6a6b06ac6261c3e62cbfe0e --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260501_224919/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + 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--git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/staged_features.json b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/test.csv b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/train.csv b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/val.csv b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/train_20260501_224919.log b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/train_20260501_224919.log new file mode 100644 index 0000000000000000000000000000000000000000..3659189dfd45052e2f0a1678a14fafb89d1f0408 --- /dev/null +++ b/timecost/c2/bayesnet/bayesnet-c2-20260501_224919/train_20260501_224919.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a2419ea58dce6b8666957845438ee953b6addfa166281431702145cc7aa608af +size 3738 diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/_ctgan_generate.py b/timecost/c2/ctgan/ctgan-c2-20260501_070350/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..7e652dd6812624ed68ce201d93a0c49dc9c354fb --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/models_300epochs/ctgan_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/ctgan-c2-1382-20260501_070414.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/ctgan-c2-1382-20260501_070414.csv") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/ctgan-c2-1382-20260501_070414.csv b/timecost/c2/ctgan/ctgan-c2-20260501_070350/ctgan-c2-1382-20260501_070414.csv new file mode 100644 index 0000000000000000000000000000000000000000..ee364b5f4a4a09aef6c8117f2d81621bff3e04fc --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/ctgan-c2-1382-20260501_070414.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e1f7b02a4ec291764c1cbd976b049a30764002ba3171dd16092ef3fcb5ef7656 +size 42112 diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/ctgan_metadata.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/gen_20260501_070414.log b/timecost/c2/ctgan/ctgan-c2-20260501_070350/gen_20260501_070414.log new file mode 100644 index 0000000000000000000000000000000000000000..0305c04a8b0409d2f2ea8984a24ba0f2bbf7bbc6 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/gen_20260501_070414.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26522d005d72be4f52afcf1da0ff670e0f431e6674695829c3ba52a8a20a9ed8 +size 560 diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/input_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": 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a/timecost/c2/ctgan/ctgan-c2-20260501_070350/models_300epochs/ctgan_300epochs.pt b/timecost/c2/ctgan/ctgan-c2-20260501_070350/models_300epochs/ctgan_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..53e0cc9707263935558a00fe579c26deac4ad2ff --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/models_300epochs/ctgan_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8a8cc35257101b33a517a602d7302dcb9095c4b077d2789f5fbd974f941b691 +size 926947 diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/models_300epochs/train_20260501_070350.log b/timecost/c2/ctgan/ctgan-c2-20260501_070350/models_300epochs/train_20260501_070350.log new file mode 100644 index 0000000000000000000000000000000000000000..82ce7f3bab48b21bcdb5ae591ed0aa68523d8fb2 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/models_300epochs/train_20260501_070350.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f18d98ddfdd822b77a9c59e4da0d85f0850b223686d472cbf58fbc88007cc64 +size 1836 diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/normalized_schema_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": 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of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/public_gate_report.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/staged_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..eda8b2df3e869b39a309a1be556c07d14a428799 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/staged/public/train.csv", + "val_csv": 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"name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/runtime_result.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..0e75cf27dd873f787a59f69d3e94ac7fdacf9ef2 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260501_070350", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/ctgan-c2-1382-20260501_070414.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/models_300epochs/ctgan_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-01T07:03:50", + "ended_at": "2026-05-01T07:04:14", + "duration_sec": 24.053 + }, + "generate": { + "started_at": "2026-05-01T07:04:14", + "ended_at": "2026-05-01T07:04:19", + "duration_sec": 4.917 + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/adapter_report.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..a1c5f6927bc1f287f54d3e38c405af6cc08f703a --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/adapter_transforms_applied.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/model_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f1358d263878d1c9bdf05961e45bc8c32fe0a057 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260501_070350/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/staged_features.json b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/test.csv b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/train.csv b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/val.csv b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260501_070350/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_generate.py b/timecost/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..72d631b335a5126d37d39b7ce8a5a7267f2a06ad --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_train.py b/timecost/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6b9b4676792febe944a8372bf1283e47afc4c4dd --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=8, + generator_dim=(16, 16), + discriminator_dim=(16, 16), + batch_size=32, + pac=1, + epochs=50, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv new file mode 100644 index 0000000000000000000000000000000000000000..681e27aa7742ed0c684b00f71f3be788e0e869d8 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29e976f587efbc955df869946a4bb105ce51b79458d3c62ceca09ac148b6ed20 +size 41616 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/ctgan_metadata.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/gen_20260504_152353.log b/timecost/c2/ctgan/ctgan-c2-20260504_152304/gen_20260504_152353.log new file mode 100644 index 0000000000000000000000000000000000000000..de002e4b88e95acd9bc50ca032cc0ba1f0b86e39 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/gen_20260504_152353.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9acf86561cff1e9c6a4c9c06233f1f9190b7ed2b840fc2e34e276dbd370ea385 +size 297 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/input_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt b/timecost/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e36636095420ce570b0bfe9047133f9ba54849ac --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39c7bef1dfa571bec7a879d742cb40f266707e489676ce3b74528b1c78d49d51 +size 279437 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/train_20260504_152304.log b/timecost/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/train_20260504_152304.log new file mode 100644 index 0000000000000000000000000000000000000000..31881c865e73fc7a604711f229b0138561760091 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/train_20260504_152304.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34ee1fa8c63c912749b01e9ecd9d79d28d46671f3739ed5d339a692b3f263b85 +size 10893 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/normalized_schema_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9adffa84c9a982333c5e541977c7b6dc9bc69810 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/run_config.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..fb41d8bc3413589c05aaeab9ea2b38014b7b853b --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T15:23:04", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "32", + "CTGAN_DEFAULT_EPOCHS": "50", + "CTGAN_DISCRIMINATOR_DIMS": "16,16", + "CTGAN_EMBEDDING_DIM": "8", + "CTGAN_GENERATOR_DIMS": "16,16", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/runtime_result.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..d04243918b6cf71cea8be69dbd1ac322df2d0d9b --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_152304", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/ctgan-c2-1382-20260504_152353.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/models_50epochs/ctgan_50epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T15:23:04", + "ended_at": "2026-05-04T15:23:53", + "duration_sec": 49.624 + }, + "generate": { + "started_at": "2026-05-04T15:23:53", + "ended_at": "2026-05-04T15:23:59", + "duration_sec": 6.006 + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_report.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..7a10b79b3fd438b352d349856e6aaa5170be0296 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_transforms_applied.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..73b58d2610a7c6335c463f5ded04d279904d13f3 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152304/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152304/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_generate.py b/timecost/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..a6e2cc17bfbb8d558e91a73d47e786f6a6ea4a89 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_train.py b/timecost/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..eb7e6332e2a5f17ac5c5659de2b5fa14f3402fa6 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=64, + pac=1, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv new file mode 100644 index 0000000000000000000000000000000000000000..24115f3ea8ecea36e3add98d8105e4e8fb44f121 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1a3894c926a9de28a2c5d069c22c5841dba3d46059ebb36e4d9d6a34cf64b9d7 +size 41628 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/ctgan_metadata.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/gen_20260504_152503.log b/timecost/c2/ctgan/ctgan-c2-20260504_152412/gen_20260504_152503.log new file mode 100644 index 0000000000000000000000000000000000000000..8dc6481b689cc2843ea2cf84ad6d5317d8d6db8d --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/gen_20260504_152503.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4fdf3116f7dbf5aa10526c09e0aa425ff5d1d7b6d646d7493b1947d94e53cdc3 +size 297 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/input_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt b/timecost/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..0e4044dc65ae5c3bd120e640c227865c3a03dc6f --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:374f0a3ab118b4c358cfa846e538a91d1c0312a5d866fcadfd475f070e6873d8 +size 295011 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/train_20260504_152412.log b/timecost/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/train_20260504_152412.log new file mode 100644 index 0000000000000000000000000000000000000000..016ed31b2b3da069ab0f57b0e1d8afed0aedb0de --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/train_20260504_152412.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13eea713840c309e407bb778338ff2e10f7e51ab12a6d203f926b413c280d5ad +size 20259 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/normalized_schema_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..80aeec1d38cb19d7ef9896f62f53b30f8a90381f --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/run_config.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..2cb71686f0f7799148a51aa35b2a4ee84aca72e3 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T15:24:12", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "64", + "CTGAN_DEFAULT_EPOCHS": "100", + "CTGAN_DISCRIMINATOR_DIMS": "32,32", + "CTGAN_EMBEDDING_DIM": "16", + "CTGAN_GENERATOR_DIMS": "32,32", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/runtime_result.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..937e58e0473c6bbd8b3ee0e92fae0b15b8422aeb --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_152412", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/ctgan-c2-1382-20260504_152503.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/models_100epochs/ctgan_100epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T15:24:12", + "ended_at": "2026-05-04T15:25:03", + "duration_sec": 50.201 + }, + "generate": { + "started_at": "2026-05-04T15:25:03", + "ended_at": "2026-05-04T15:25:08", + "duration_sec": 5.617 + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_report.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..f5f5ece257cc2be54a51b3dd7cb44e9814f81e60 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_transforms_applied.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4962ba89ebed95e0c2b0d4ca815088c5fa2c32b8 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152412/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152412/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_generate.py b/timecost/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..16e1cbf8bd422bec2ca1d496741eb412a6d3b10c --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_train.py b/timecost/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..78628106b930abae414832204fa9175a7455533f --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=128, + pac=1, + epochs=150, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv new file mode 100644 index 0000000000000000000000000000000000000000..fb43b629026c53e55167fb66183df12a8ede4f6e --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9e5f49ea1929935b2cbff19d40b624b643fce1ce9b25dcd47f8a95b18c8e658 +size 41931 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/ctgan_metadata.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/gen_20260504_152602.log b/timecost/c2/ctgan/ctgan-c2-20260504_152521/gen_20260504_152602.log new file mode 100644 index 0000000000000000000000000000000000000000..f33ebfa9b854a8e71a032a35483e56c9cc8683f6 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/gen_20260504_152602.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:90a4a7de75eec0fbee24fc86b99413d696184b508dbb5166fb1f6369c00a0259 +size 297 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/input_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt b/timecost/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..464b111ee5a641b1c23c4ed76cc24c07febeefd0 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e03e4717b96ceed0bbec1c8771604a12d35d2af80e6233323dea1b055eec9ae8 +size 336739 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/train_20260504_152521.log b/timecost/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/train_20260504_152521.log new file mode 100644 index 0000000000000000000000000000000000000000..776c175a578fb21419b6be68f41b2a4305893332 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/train_20260504_152521.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3af72c614a2bedcd9896ce46da7ab7b8947307ee05fdababfa63c84c3265b34 +size 29431 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/normalized_schema_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..66e850014a3d2c262c9f9ba6b2ea2fbd0b4277a8 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/run_config.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..17ebaa88d95e65cb684c715dc2ce0a786db7eba5 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T15:25:21", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "128", + "CTGAN_DEFAULT_EPOCHS": "150", + "CTGAN_DISCRIMINATOR_DIMS": "64,64", + "CTGAN_EMBEDDING_DIM": "32", + "CTGAN_GENERATOR_DIMS": "64,64", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/runtime_result.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..5441404826fb87c542aeb6ba3fe39ddb39ed7d05 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_152521", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/ctgan-c2-1382-20260504_152602.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/models_150epochs/ctgan_150epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T15:25:21", + "ended_at": "2026-05-04T15:26:02", + "duration_sec": 40.385 + }, + "generate": { + "started_at": "2026-05-04T15:26:02", + "ended_at": "2026-05-04T15:26:07", + "duration_sec": 5.523 + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_report.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..0b097136f4896f9d5deee418a166899244ba88a2 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_transforms_applied.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9e0cdf8d1c63e1c6f50e3ef553b75abd8d288f85 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152521/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_152521/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_generate.py b/timecost/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4a513b6c64f8729a0592980505e361f0ede7a9ea --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_train.py b/timecost/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..dd7ddb7bea931b54947b0e3d88527c9ad137178d --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=80, + pac=5, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv b/timecost/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c2bcd6913d6fc848e66bb9fdda340a1e05554b7 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06f4bd2740e2d92884bded619bc47b8652f747c537f543c463da26b4ecc3b2e9 +size 41563 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/ctgan_metadata.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/gen_20260504_162321.log b/timecost/c2/ctgan/ctgan-c2-20260504_162240/gen_20260504_162321.log new file mode 100644 index 0000000000000000000000000000000000000000..121e2d02ec34b94bf50346ead1a3d5eb919fd14f --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/gen_20260504_162321.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e9a8bd298558c0d0c5121a8f68fd97cdcf80e5e349de95203e772ebf7d6a0d7 +size 297 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/input_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e70c6b5c1d7c7363c5970bab5156163411d211e7 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt b/timecost/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..185ce48c7b3861004a1ae943ced45428837221e1 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:89001f5bc9d51b23712eb3bb71f85ef655ff710707b825e7ae6ac1d586cc395b +size 294947 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/train_20260504_162240.log b/timecost/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/train_20260504_162240.log new file mode 100644 index 0000000000000000000000000000000000000000..296e5914be6b77b0dc29b560dbcc0daa497eab65 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/train_20260504_162240.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a80e86a687df24cbf5cef0f767d6091a7728594215f9fdc8bbf3e4c508252b43 +size 20165 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/normalized_schema_snapshot.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3e04926e55a12a23c91b123f0fdf38caa78c98fe --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/run_config.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..7e77ec9db818a984c777289e51c82c6925a25d92 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:22:40", + "dataset_id": "c2", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "80", + "CTGAN_DEFAULT_EPOCHS": "100", + "CTGAN_DISCRIMINATOR_DIMS": "32,32", + "CTGAN_EMBEDDING_DIM": "16", + "CTGAN_GENERATOR_DIMS": "32,32", + "CTGAN_PAC": "5" + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/runtime_result.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..c943130baae43b3a0a41eb9a2f1e7079a453a38e --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "run_id": "ctgan-c2-20260504_162240", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/ctgan-c2-1382-20260504_162321.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/models_100epochs/ctgan_100epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:22:40", + "ended_at": "2026-05-04T16:23:21", + "duration_sec": 41.673 + }, + "generate": { + "started_at": "2026-05-04T16:23:21", + "ended_at": "2026-05-04T16:23:27", + "duration_sec": 5.382 + } + } +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_report.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..a84ffcef8e6e4db6b0ae03f2de9ce8b993bf33d0 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_transforms_applied.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b8bed5508038be911353f57dfc64f5c3fd221056 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/ctgan/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162240/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162240/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_generate.py b/timecost/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ff855eb274e019fb91b6476423543290aaf540c3 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_train.py b/timecost/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e0dd700a1ac2533023d1f50e947dbe32044a8098 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162339/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv") +discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class'] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=100, + pac=10, + epochs=200, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/models_200epochs/ctgan_200epochs.pt") \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv b/timecost/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv new file mode 100644 index 0000000000000000000000000000000000000000..475fb7eee9738c341bde5529d780b2427ba4b094 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162339/ctgan-c2-1382-20260504_162437.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dcd29852524ef13d0009da75adaf69c0d09dc640b8fbb13ba387045198c5c725 +size 40940 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162339/ctgan_metadata.json b/timecost/c2/ctgan/ctgan-c2-20260504_162339/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162339/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_162339/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json b/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv b/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv b/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv b/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/ctgan/ctgan-c2-20260504_162339/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_X_host.npy b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..2c631eb05814d7aa116816e9799b9c52f8c0e566 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:260745c93bbaaa167bd761bd653a194f7b25b477b3929a24e343741a3c4fa280 +size 38824 diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_gen.py b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..d2b42922010bc7ec07265645dfb0e1d6cf86fca4 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(1382)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv', index=False) +print("saved", len(df)) diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_meta_host.json b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..be467ac019d08a03e8ba5c0d436f59170db78786 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["buying", "maint", "doors", "persons", "lug_boot", "safety", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5]} \ No newline at end of file diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_train.py b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c5db79a6adac10e5443c38df7e27a35dfe629363 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib') diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv new file mode 100644 index 0000000000000000000000000000000000000000..f27ce02de22935a63bf9c44b36c1ec95abf22b11 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f05e6305ad9ae6a73ceedca6ac21485ca0252b55a3a2d9c86e9d14b6b1c566fb +size 57598 diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..91b82d488e3ca149c88205a6b539417bae30b551 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29b1a2d549cebbb41f05e432a66310bee854f4001499e662ced2d5638d6a1a69 +size 144873431 diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/gen_20260501_180507.log b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/gen_20260501_180507.log new file mode 100644 index 0000000000000000000000000000000000000000..468dd563bf18965e5a15365cfbb1fbb8ecf3ca12 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/gen_20260501_180507.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a0d15ba478b2b03e234e7ef665cd4a0179699fde3851b7149a2ec1bc47f81519 +size 294 diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/input_snapshot.json b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..24ba74f272fb2c967af52a4ad4947955f45646ea --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/models_fd/model.joblib b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..91b82d488e3ca149c88205a6b539417bae30b551 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29b1a2d549cebbb41f05e432a66310bee854f4001499e662ced2d5638d6a1a69 +size 144873431 diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/normalized_schema_snapshot.json b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/public_gate_report.json b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/staged_input_manifest.json b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..40f47ecbbad7297b9fcd90dfedc1405053e4ced0 --- /dev/null +++ b/timecost/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/test.csv", + 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--git a/timecost/c2/realtabformer/rtf-c2-20260501_033610/train_20260501_033610.log b/timecost/c2/realtabformer/rtf-c2-20260501_033610/train_20260501_033610.log new file mode 100644 index 0000000000000000000000000000000000000000..0f11a183ee02dcf365160c18b7f456f1aff30d4a --- /dev/null +++ b/timecost/c2/realtabformer/rtf-c2-20260501_033610/train_20260501_033610.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:71485f3c386baff64106d1cf94bd73b78804d3ff79be33b856fce97700dba450 +size 182129 diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_gen.py b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..766ebe32a4394cc53ea12a89ad3424187d78632c --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/tabular_bundle/pipeline_c2" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_c2/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/tabbyflow-c2-1382-20260501_053427.csv") diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_train.py b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..75ca1b3f716377e162c52a64be03a64f2aa3dc14 --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/tabular_bundle/pipeline_c2" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/gen_20260501_053427.log b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/gen_20260501_053427.log new file mode 100644 index 0000000000000000000000000000000000000000..2b04e56f59be7b12b34f7fc644d24c37604f5c64 --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/gen_20260501_053427.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ebf455cec75b302ce3913449337578522211c421499afab3f3956fd9e5dfb36c +size 3114 diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/input_snapshot.json b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..89c9d5855c86fb753a25070ff5c19d45d1e2fb9e --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/models_tabbyflow/trained.pt b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/models_tabbyflow/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..f063a878c470d9d40f79db79881596e1e497dffd --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/models_tabbyflow/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adb734fec12f2251befd371dca69e481b19464aded2a6823105a01b4be6ddbe5 +size 40 diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/normalized_schema_snapshot.json b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/public_gate_report.json b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/staged_input_manifest.json b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a9b81ba9787b915e756fab34b82d4ddc1f8d0105 --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": 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"artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/tabbyflow-c2-1382-20260501_053427.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabbyflow/tabbyflow-c2-20260501_052813/models_tabbyflow/trained.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-01T05:28:13", + "ended_at": "2026-05-01T05:34:27", + "duration_sec": 374.559 + }, + "generate": { + "started_at": "2026-05-01T05:34:27", + "ended_at": "2026-05-01T05:34:37", + "duration_sec": 9.962 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/staged_features.json b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/test.csv b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/test.csv new file mode 100644 index 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b/timecost/c2/tabbyflow/tabbyflow-c2-20260501_052813/train_20260501_052813.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b0f057a74a0bffb161d24ac00249eb8e9e1d04cb5519e026f539daf6092430aa +size 284038 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/_tabddpm_sample_r0.py b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..1332f77f922300a8fd73344d77e0f62f24f16145 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1382 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/config_sample_20260501_053541_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/tabddpm-c2-1382-20260501_053541.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/tabddpm-c2-1382-20260501_053541.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/_tabddpm_train.py b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..92d450469a11256c4fd44fb797498e3ddd855674 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/config.toml b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..cfd9b4ef9bd2c804ea29a19f0ad39fbb43d76cd1 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/config_sample_20260501_053541_r0.toml b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/config_sample_20260501_053541_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..995cd61ec100ee4b3422228b85916af561a13b82 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/config_sample_20260501_053541_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 1000 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/X_cat_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/info.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/y_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/gen_20260501_053541_r0.log b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/gen_20260501_053541_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..5c10bbac2bdc4e338275e0d3d15859957f568ba2 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/gen_20260501_053541_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7213a586526de04ecda26ec75cada4459cebf641c8481eb89462372a8ef761d +size 42462 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/input_snapshot.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..772021148f055243c89ddfd9b6fa388ee33d05ea --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/X_cat_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1afe10e9ad04bdf628acab837f97dc71381ae6b5 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f3e794630e03fa6aa423cbcc412303cd342a9e772682c127caa3988778103dbd +size 16885 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/X_cat_unnorm.npy b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..a59ef680a27f15c2dfc431c4c0a79779ecc321fa --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7aaee2a9171fa9fe9f0027724f0e45a2e2a3b49c592f5c6c98b509a48b361563 +size 66464 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/config.toml b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..995cd61ec100ee4b3422228b85916af561a13b82 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 1000 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/info.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/loss.csv b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..dd9587ff64e877faf18fb187eea560a843e932d5 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f6f64aab8041c39f33c6e3bb32a0964b75a4692062c9eec03a84cae0cfb47ac +size 1249 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/model.pt b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..96769674b49295a16307d75a46fae3b6bdb6dd55 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db4f8cce94f8d9428f68d597138bdd3d357d268f5da7b268c89fab510fce817a +size 566934 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/model_ema.pt b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/model_ema.pt new file mode 100644 index 0000000000000000000000000000000000000000..54e3568232f44cd6be11757eb357c334575d37d6 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:285d1dffd7456b5ccf89321bfd98304d0594f85982aee4729e3175ffbffa68aa +size 567778 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/y_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..aa9afff4d061333b18828274b1072d1b884f162c --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b1a722d369027a22bed702e3539c6fefa976741570c4c0a2f8546e2978c3d7 +size 11184 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/normalized_schema_snapshot.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/public_gate_report.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/staged_input_manifest.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..47fb0c9ff6989fe85c97a1d5aad8f1a563da07c8 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + 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"feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/test.csv b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/test.csv @@ 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b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/adapter_report.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..dcbf79e631dc451882e0e2a5edf8e6abe9cec7ed --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/adapter_transforms_applied.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/model_input_manifest.json b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8f0ce3ef11af81e5424e650c88f496a673727e93 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabddpm", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260501_053445/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/tabddpm-c2-1382-20260501_053541.csv b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/tabddpm-c2-1382-20260501_053541.csv new file mode 100644 index 0000000000000000000000000000000000000000..c1395ed38570055862bc0b0e4808966baf74db1e --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/tabddpm-c2-1382-20260501_053541.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d1ae5fa4380a0a8879510e49d09625f4653e2ac3b5bb4f8fe08cd7a776dc532 +size 38745 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/train_20260501_053445.log b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/train_20260501_053445.log new file mode 100644 index 0000000000000000000000000000000000000000..c9cecc5137d01c1ae345ddc11d054205dd6087a3 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260501_053445/train_20260501_053445.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc81c3da56b1deeaa60556e8f6cec9fd97ce7200a6cc83fda2acef2f37cba648 +size 1076 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_sample_r0.py b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..acd07a0b4e4bcf12e9a8c3908b4d5a6367d1d0d6 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1382 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_train.py b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ddb041209cab17e11561f35f7bc9132dfe91f27a --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..b32437de5ec87ecfb85758bc6c686c27a4499f45 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..d2a6930b9108b5e2a8f747ee1a781a87b6bc55bb --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/config_sample_20260504_181140_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 256 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/X_cat_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/y_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/gen_20260504_181140_r0.log b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/gen_20260504_181140_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..9fcd131940297336c95b3df388afad1eccd0f03f --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/gen_20260504_181140_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:699ec388503c3fef971e172974cee64500a765cff8855575df673d28450f3420 +size 25662 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/input_snapshot.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..772021148f055243c89ddfd9b6fa388ee33d05ea --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..862303f0519083c21dea3de3dfc30aaabc4a90fd --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ab974a88850d8041e57dd6341c43e40877629a6e0adaf17a6152e0165922ed3 +size 16885 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_unnorm.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..b648311a8f6441d68bdba8e8f5737657ea79a9e1 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06a6d4b433422299123e5388de04c0166e6143ab0788c38fe47f40da1c9c727d +size 66464 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/config.toml b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..d2a6930b9108b5e2a8f747ee1a781a87b6bc55bb --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 200 +gaussian_loss_type = "mse" + +[train.main] +steps = 2000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 256 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/info.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/loss.csv b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..d5c7da855b8ef5835b2dcde545c19bbb7c72d227 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:57f250e7354878e53460575baff2b9f2824f988cc8ff11b40b4c980408373c4f +size 505 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/model.pt b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..127c529e5a524f3d4d02fdfd5c84087cabb70040 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:594458bf70bca8778c1355fcc833e6db2d387465769d2ea8135d727934e2c28f +size 566934 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/model_ema.pt b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/model_ema.pt new file mode 100644 index 0000000000000000000000000000000000000000..d29643dbbb54baed7564f91a11c10dffe3f1ed90 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ecd8cbe4cf1c5d92c800e17e5be4d19b80c5673a835f655f373bf0dc0eef4c1 +size 567778 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/y_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..aa9afff4d061333b18828274b1072d1b884f162c --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b1a722d369027a22bed702e3539c6fefa976741570c4c0a2f8546e2978c3d7 +size 11184 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/normalized_schema_snapshot.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/public_gate_report.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/staged_input_manifest.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..637a4e41d23df216439071f6757c5549d916d585 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/test.csv", + "features_json": 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/dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T18:11:03", + "dataset_id": "c2", + "model": "tabddpm", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tabddpm", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103", + "output_csv": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TABDDPM_GPUS": "device=3", + "TABDDPM_NUM_TIMESTEPS": "200", + "TABDDPM_SAMPLE_BATCH_SIZE": "256", + "TABDDPM_STEPS_PER_EPOCH": "40", + "TABDDPM_TRAIN_BATCH_SIZE": "256", + "TABDDPM_TRAIN_LR": "0.001" + } +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/runtime_result.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..878ba523189e12e2c0225261e4848c928b5dfaf3 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabddpm", + "run_id": "tabddpm-c2-20260504_181103", + "public_gate_status": "pass", + "adapter_ready_status": 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+ "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181103/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv new file mode 100644 index 0000000000000000000000000000000000000000..80588c5545b15e52e605da2d287e0d164b16372b --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/tabddpm-c2-1382-20260504_181140.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:98221dc88260a2ea1e55cdbd30eaedbb33e88e6d066c3bf395565afbf203b2b8 +size 38745 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/train_20260504_181103.log b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/train_20260504_181103.log new file mode 100644 index 0000000000000000000000000000000000000000..d1bb741fbeb6f2fcd03950e3432f561861e3193f --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181103/train_20260504_181103.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14cf68fa12bf8a9e4d7cc59a892456d54b976c676e71e3d6d92486ff58ee32d0 +size 766 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_sample_r0.py b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..9a94d2a41b2dcf189c488b2c4912a7dadb8e35ee --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1382 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_train.py b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9930b28e955dff462191f2525b3a0fa738c7ca34 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..1f89e2e3f7a297157b784a249a11249eeb27c9d0 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 256 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..6d926ececa1d9852eded91a9d1abf795c2056384 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/config_sample_20260504_181327_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 256 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/X_cat_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/y_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/gen_20260504_181327_r0.log b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/gen_20260504_181327_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..877963b0fe6be73beadf40653b39c241200f7327 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/gen_20260504_181327_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b93d275fe4edebae555a0b578fb14998a7b5164eb5c79abe5b9d996cf4d671a9 +size 126462 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/input_snapshot.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..772021148f055243c89ddfd9b6fa388ee33d05ea --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b8ce5b95bc8251728c835c81edbb5e6a6185d9c9 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e77f34c91b60cbce837540a3e08097ecb343703ce3d0246710819ef447e1d54 +size 16885 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_unnorm.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..7ea825ed64e038b9ddc5863f265a1cd55ca73819 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c3584fe9ca4a1b527e84e477f6fd5695011f2223445495240a83d02ecb2075e +size 66464 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/config.toml b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..6d926ececa1d9852eded91a9d1abf795c2056384 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1382 +batch_size = 256 +seed = 0 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/info.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0f2b351a317260c450725d5a04373330ad3701a3 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "num_classes": 4 +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/loss.csv b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..5f996a25328bdaab56049c4f49f7567d240eace0 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:35ec4ea19e9311190f8c430e41ae865decb12a205a13591d7f3d008db462406e +size 1251 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/model.pt b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..b80bbad06939fc0ead1b779678d99d473f5b2074 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5354cbe787b33e8d9ec39f348ab76a760a7bcf5359143c2cb10cb84c72c79c0 +size 566934 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/model_ema.pt b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/model_ema.pt new file mode 100644 index 0000000000000000000000000000000000000000..348701ed22a7b54c87bcf068ba681a3b7d763b59 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a111267f57c5e1493761867d6869283240e2c0499acd84e32bf616126181ca68 +size 567778 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/y_train.npy b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..aa9afff4d061333b18828274b1072d1b884f162c --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b1a722d369027a22bed702e3539c6fefa976741570c4c0a2f8546e2978c3d7 +size 11184 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/normalized_schema_snapshot.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": 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diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/val.csv b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/staged/tabddpm/adapter_report.json b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/staged/tabddpm/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..39ceea716a5a35040872974d2eb66c6003d74991 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/staged/tabddpm/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + 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+ "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv new file mode 100644 index 0000000000000000000000000000000000000000..a735ab2dad04a579c9f5d5388a4fa8ac2c1ece91 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ef7b04421aefbad10890e4ab781f979634cee232355d0c0fa801837604385a7 +size 38745 diff --git a/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/train_20260504_181207.log b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/train_20260504_181207.log new file mode 100644 index 0000000000000000000000000000000000000000..1a69546b543fcdb5aad504909aefe195ec2cdd07 --- /dev/null +++ b/timecost/c2/tabddpm/tabddpm-c2-20260504_181207/train_20260504_181207.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c43a64728eefff6ad798ad80865649a2403c1ec2aeda34c198141392ff04fb7a +size 1078 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_gen.py b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..6ad02cde0ebb7085c857c9467656a0c3dc2b36e6 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_c2/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(1382)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff-c2-1382-20260501_054253.csv") diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_train.py b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c639c32804feee4ba0d67da24759d1db2a17f4f6 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_c2" +src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/gen_20260501_054253.log b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/gen_20260501_054253.log new file mode 100644 index 0000000000000000000000000000000000000000..aca2c90c10233d71f8aef41d6b51dd4950215416 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/gen_20260501_054253.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3de292e34bb26dafd4263e6580a3a73883941a9f22ab42d402500eb32b7de440 +size 4472 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/input_snapshot.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..6d83542090e9720c485d72abe1bb75a800175917 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/models_tabdiff/trained.pt b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/models_tabdiff/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc319a55a9c7152a1137f968b6fa1c735125ad11 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/models_tabdiff/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:306660b8aad4549267c68390b017e15ddb9bc06a02c80515eb72a97ae31a81eb +size 74 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/normalized_schema_snapshot.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/public_gate_report.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/staged_input_manifest.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..20da241320b7b58bc53e5eb6d102cb90cc7218e1 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/runtime_result.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..86fdaa982483a14c04db6cd45a4e84a4c04ed817 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "run_id": "tabdiff-c2-20260501_053602", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff-c2-1382-20260501_054253.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/models_tabdiff/trained.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-01T05:36:02", + "ended_at": "2026-05-01T05:42:53", + "duration_sec": 411.134 + }, + "generate": { + "started_at": "2026-05-01T05:42:53", + "ended_at": "2026-05-01T05:43:02", + "duration_sec": 9.393 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/staged_features.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/test.csv b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/train.csv b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/val.csv b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/adapter_report.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..ebf25cfa73bc5165b00898833edb483d4395add1 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/adapter_transforms_applied.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/model_input_manifest.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6e8444ed55ce353c6b12da55b3670012d2ade6d1 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/staged/tabdiff/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabdiff", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff-c2-1382-20260501_054253.csv b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff-c2-1382-20260501_054253.csv new file mode 100644 index 0000000000000000000000000000000000000000..a2493f1665aa8b13ef982441972f9b14b1a95e8e --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff-c2-1382-20260501_054253.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:914638c490c2fa5d234f37276b5c51d1a6b3cf429ac73bab0741cc357075b03e +size 19397 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff_train_meta.json b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff_train_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..b93e041579ad7b1a984971416e1bb8b567d71270 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff_train_meta.json @@ -0,0 +1,5 @@ +{ + "exp_name": "adapter_learnable", + "dataname": "pipeline_c2", + "steps": 500 +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_test.npy b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_train.npy b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_val.npy b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..e99f1ae8dc9e7155e5c4bbbd3c2f3f7215c628f7 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/X_cat_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:24d2203601ba060728920e754747e5a1dc3955a1b5775e147382fee4f1812542 +size 66464 diff --git 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b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/info.json @@ -0,0 +1,88 @@ +{ + "name": "pipeline_c2", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 6, + "train_size": 1382, + "test_num": 1382, + "val_num": 1382, + "train_num": 1382, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "categorical" + }, + "1": { + "sdtype": "categorical" + }, + "2": { + "sdtype": "categorical" + }, + "3": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "categorical" + } + } + }, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "inverse_idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "idx_name_mapping": { + "0": "buying", + "1": "maint", + "2": "doors", + "3": "persons", + "4": "lug_boot", + "5": "safety", + "6": "class" + }, + "n_classes": 4 +} \ No newline at end of file diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/real.csv b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..f778fcd2758e712a195229c6f50504f94f046c8d --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a54d8aab34745836c4d0260009787c8064c2c3e9d99040c5a6856190eb0745e9 +size 24215 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/test.csv b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..f778fcd2758e712a195229c6f50504f94f046c8d --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a54d8aab34745836c4d0260009787c8064c2c3e9d99040c5a6856190eb0745e9 +size 24215 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/val.csv b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..f778fcd2758e712a195229c6f50504f94f046c8d --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a54d8aab34745836c4d0260009787c8064c2c3e9d99040c5a6856190eb0745e9 +size 24215 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_test.npy b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_train.npy b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_val.npy b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..8bfc8f9de7350657cd6411c55d2b9b87bea9c13a --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f8eb0b25043331a30fde5439f063eed59e7a76f45d7d102b9b4fbcd16b551b4 +size 11184 diff --git a/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/train_20260501_053602.log b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/train_20260501_053602.log new file mode 100644 index 0000000000000000000000000000000000000000..6de7164078944facc6e0eb3de930d92356d8d9f9 --- /dev/null +++ b/timecost/c2/tabdiff/tabdiff-c2-20260501_053602/train_20260501_053602.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c984eb61e2f9cdc4c93c5c141f20a2c4bc968b83806b61b00faa48a7107b0ec7 +size 287391 diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/_tabpfgen_generate.py b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..2e5bce57f95edf1af58f5e2feb9331fdadf5fe8e --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/train.csv") +target_col = "class" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(1382) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen-c2-1382-20260501_054310.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen-c2-1382-20260501_054310.csv") diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/gen_20260501_054310.log b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/gen_20260501_054310.log new file mode 100644 index 0000000000000000000000000000000000000000..5e8eaa6e31f04719374111dcb97f6380827d61c4 --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/gen_20260501_054310.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df9c8e7725f68e68c48bbd793583da34cdc8df62d4753f48f5d794b3c2fe2a6e +size 1299 diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/input_snapshot.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0dfc898784526759fbfe4fcdd1aa60dc4d52829f --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/normalized_schema_snapshot.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/public_gate_report.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/staged_input_manifest.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c4d6168e58727023f6112fb5b0d6336fc318482f --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/runtime_result.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..9a3ac6e349670b0b59ae9932669950ef901f6dab --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "run_id": "tabpfgen-c2-20260501_054310", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen-c2-1382-20260501_054310.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310" + }, + "timings": { + "train": { + "started_at": "2026-05-01T05:43:10", + "ended_at": "2026-05-01T05:43:10", + "duration_sec": 0.005 + }, + "generate": { + "started_at": "2026-05-01T05:43:10", + "ended_at": "2026-05-01T05:43:30", + "duration_sec": 19.972 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/staged_features.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/test.csv b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/train.csv b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/val.csv b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/adapter_report.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3586c931712b08ab687b4f24e81e0c90c8b61da1 --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/adapter_transforms_applied.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/model_input_manifest.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..37e58f9691a32aa088daf57022cb90fb57f416b8 --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tabpfgen", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen-c2-1382-20260501_054310.csv b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen-c2-1382-20260501_054310.csv new file mode 100644 index 0000000000000000000000000000000000000000..7deaca4f7a6caf6fad2c45c7da3bb3d51f33f543 --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen-c2-1382-20260501_054310.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1b5c3eb2de3884b6fa7c5f7dfec978ca2c4fb9011eb27b75455fdb21cd7e0c4 +size 41880 diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen_meta.json b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..d583cd196e1529239c5ed015efbdba6cd0af7fe1 --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabpfgen/tabpfgen-c2-20260501_054310/staged/public/staged_features.json", + "target_col": "class", + "is_classification": true, + "task_type": "classification", + "n_rows": 1382, + "n_cols": 7 +} \ No newline at end of file diff --git a/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/train_20260501_054310.log b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/train_20260501_054310.log new file mode 100644 index 0000000000000000000000000000000000000000..94fdfcacc461196060df1374638916fa0f16eedd --- /dev/null +++ b/timecost/c2/tabpfgen/tabpfgen-c2-20260501_054310/train_20260501_054310.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:90cbba9f28b2fe9d533d38ca45ee2eb45779177b84e934be49d88ee80150e9e1 +size 595 diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_sample.py b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..ecd5da7c2197ba6052bf73dd776c2236f87c2821 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336" +dataname = "tabsyn_c2" +output_csv = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/tabsyn-c2-1382-20260501_060640.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 1382 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_train.py b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2c799ff7949ee261da70b3c0f15a1c4cfd25b669 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336" +dataname = "tabsyn_c2" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/X_cat_test.npy 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sha256:f937731a3e7785676e9f861e44eddab69c323327e03bdd1f6e8755c5321538fb +size 5656 diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/info.json b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3d85c74a88f8b29c5054c0455ce2f895b74cc726 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/info.json @@ -0,0 +1,89 @@ +{ + "name": "tabsyn_c2", + "task_type": "multiclass", + "n_num_features": 1, + "n_cat_features": 5, + "train_size": 1382, + "num_col_idx": [ + 0 + ], + "cat_col_idx": [ + 1, + 2, + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "buying", + "maint", + "doors", + "persons", + "lug_boot", + "safety", + "class" + ], + "train_num": 1382, + "test_num": 1382, + "header": 0, + "file_type": "csv", + "data_path": "data/tabsyn_c2/train.csv", + "test_path": null, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 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0000000000000000000000000000000000000000..2d0549f2b8d7c49273cac23d3b44743858a3f869 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/gen_20260501_060640.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e6a621aaff31f015ef41220895c2510442df1fd27aafca47bce909f7a2fbe92e +size 940 diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/input_snapshot.json b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..7d3fddeb570e67b127ac5aef0d448cae7e934e6c --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tabsyn", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": 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a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/normalized_schema_snapshot.json b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + 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a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/public_gate_report.json b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/staged_input_manifest.json b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..31e2c4cd999d62cc3b0ad0902b1d20685d7f553f --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/train.csv", + "val_csv": 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+ } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/runtime_result.json b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..b61ea3f3c0e9d8ae2fa7e933fce86116f11606af --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tabsyn", + "run_id": "tabsyn-c2-20260501_054336", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/tabsyn-c2-1382-20260501_060640.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336" + }, + "timings": { + 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"categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/test.csv b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/train.csv b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/train.csv new file mode 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/synthetic/tabsyn_c2/real.csv b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/synthetic/tabsyn_c2/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/synthetic/tabsyn_c2/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/synthetic/tabsyn_c2/test.csv b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/synthetic/tabsyn_c2/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..94d40776e20a2226c5a316fc3eaf8b74b8513970 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/synthetic/tabsyn_c2/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e7b4d4a1f787d81e53eddffbd43603149e1dd038d94b4c0244340ff4a2e4511 +size 19397 diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/tabsyn-c2-1382-20260501_060640.csv b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/tabsyn-c2-1382-20260501_060640.csv new file mode 100644 index 0000000000000000000000000000000000000000..fedf6f64b28363e72d9e20a69e9444cc8716e0e8 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/tabsyn-c2-1382-20260501_060640.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e978de460d7392592e372d541a1449124a9d1dc0a8431bb52226c1b3be7c3ad4 +size 22171 diff --git a/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/train_20260501_054336.log b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/train_20260501_054336.log new file mode 100644 index 0000000000000000000000000000000000000000..3f47b32c1e7ee1a9ca0414ac30c4a85a14d55146 --- /dev/null +++ b/timecost/c2/tabsyn/tabsyn-c2-20260501_054336/train_20260501_054336.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e99c585306f1279128b0848854ae012c2e21c5df40f4520a77ffe1a85bb733a +size 2086454 diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260501_060652/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ee07f8086f9dbcecc5be413f1c22bda98e3c3aad --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/tvae-c2-1382-20260501_060709.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/tvae-c2-1382-20260501_060709.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260501_060652/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bf188e71fc3ecfb788fd1e9ad029b67b8c19e45a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/gen_20260501_060709.log b/timecost/c2/tvae/tvae-c2-20260501_060652/gen_20260501_060709.log new file mode 100644 index 0000000000000000000000000000000000000000..6e7da99bdaa881a0255e099018caabf0fcb884d2 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/gen_20260501_060709.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:811a90c82a00a2453464da6ac5fc4ab320e6647e8228f8cec6951bd6f380b484 +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260501_060652/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/models_300epochs/train_20260501_060652.log b/timecost/c2/tvae/tvae-c2-20260501_060652/models_300epochs/train_20260501_060652.log new file mode 100644 index 0000000000000000000000000000000000000000..9e1180bfc3aade0f1b5beab810653d04743a3f29 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/models_300epochs/train_20260501_060652.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c85525e2a454b9c93bb4128e261000b45ec90958e4758d80a0e77a9d772ece2e +size 531 diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260501_060652/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..17b6a7e21950887bb50757c079cfd899b0c9e47c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3a0d83f23a59f1dd860e3ef1c7fd7e17bf189a4f5b79f6078b43dbbe8d01dd6 +size 344876 diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..988ba0785562dff2c09944102d58b9ee55a965c2 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260501_060652/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..ddeb3dfc1b4314a2e1b55f685a7b26f8aafcf7c8 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260501_060652", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/tvae-c2-1382-20260501_060709.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-01T06:06:52", + "ended_at": "2026-05-01T06:07:09", + "duration_sec": 16.933 + }, + "generate": { + "started_at": "2026-05-01T06:07:09", + "ended_at": "2026-05-01T06:07:14", + "duration_sec": 4.962 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/staged_features.json b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/train.csv b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/val.csv b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..53fd18cae5801eb9ff510758dbec2f26bb298463 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..25ac1d6d3495f34be078fd191411933776b433ec --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/tvae-c2-1382-20260501_060709.csv b/timecost/c2/tvae/tvae-c2-20260501_060652/tvae-c2-1382-20260501_060709.csv new file mode 100644 index 0000000000000000000000000000000000000000..7f03aa1c4919240cc177bb5d5621d801da134f1d --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/tvae-c2-1382-20260501_060709.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0623ee3f26117b60c69a602520884727739b320db35033d330e1b1415a4d8469 +size 41504 diff --git a/timecost/c2/tvae/tvae-c2-20260501_060652/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260501_060652/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260501_060652/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_173008/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ba56b67e501be1a02f9df11b17b5be278a4fbaf4 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_173008/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8ca6db5febbe87084a63b4625ce50a43245d7d --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/gen_20260504_173024.log b/timecost/c2/tvae/tvae-c2-20260504_173008/gen_20260504_173024.log new file mode 100644 index 0000000000000000000000000000000000000000..53d08cd059dddaa7aff8bae5458dce3fa0b5ab7e --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/gen_20260504_173024.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c250d16c78bb78776dfd39677e43fa301524a14766e56dfb776757716b41ad3c +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_173008/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/models_300epochs/train_20260504_173008.log b/timecost/c2/tvae/tvae-c2-20260504_173008/models_300epochs/train_20260504_173008.log new file mode 100644 index 0000000000000000000000000000000000000000..4f1bc8a0c8aa1472b5b5a147d542eb71dee6b421 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/models_300epochs/train_20260504_173008.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1c352ed86bd6646a32996cf14ef89e36f1c70492c504f33136461af759d97f8 +size 531 diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..601624c018769994626fca7acae79f71da20202b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:359d4ce6570b67da168f59f79a24d6afcc861aaf7416c29c0ad54e031827ea1b +size 344876 diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..34b8b885d16936dabc4bfa6ae10d9902d0dcbe1e --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_173008/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..7335c4ba5feedfd103bfd4c81cc612041c2e4e81 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/run_config.json @@ -0,0 +1,40 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:30:08", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_173008/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..22be744ff183e559b6ad1a680776340fac9c465e --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_173008", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:30:08", + "ended_at": "2026-05-04T17:30:24", + "duration_sec": 15.956 + }, + "generate": { + "started_at": "2026-05-04T17:30:24", + "ended_at": "2026-05-04T17:30:30", + "duration_sec": 5.259 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json b/timecost/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv @@ -0,0 +1,3 @@ +version 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b/timecost/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f04fd5c7b319044a938f125aa99765abe6589a9c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv b/timecost/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv new file mode 100644 index 0000000000000000000000000000000000000000..0facc9d2d09dd192dcf6aaf22fef2e2ec709388b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5c13329eb5fa57144da4e37e2c72a488103b21bb242e3711339b259178094e71 +size 41572 diff --git a/timecost/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_174013/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..3286ed3675bc2e6dab9822c66720b2afe05caa4d --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_174013/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..907e254de6937dd18f07ac7af235f221f36bb32a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/gen_20260504_174029.log b/timecost/c2/tvae/tvae-c2-20260504_174013/gen_20260504_174029.log new file mode 100644 index 0000000000000000000000000000000000000000..a1b1e990c2280e5c69b5bb23bc9d3b47e6938193 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/gen_20260504_174029.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56d72e0efac984702b081bb48d7cb946ea3691388616e818ff414e6e231d5453 +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174013/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/models_300epochs/train_20260504_174013.log b/timecost/c2/tvae/tvae-c2-20260504_174013/models_300epochs/train_20260504_174013.log new file mode 100644 index 0000000000000000000000000000000000000000..bad1b646c923e3b9a8e91ad353281c3cc4607689 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/models_300epochs/train_20260504_174013.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b25cd4019839ccaca05dcab067fe07c9a8293c777a3e34ded19ab97c8e49e17 +size 620 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..08961776a4f44dbaf6745845c6423443d3fbed2b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6cb2b979aca8616d64810c710e8de8e8ca165a0d1117a54c4bc6ba60ff36a79 +size 344876 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..663ecf4e842af7f358042d1ff125793c36e9c902 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_174013/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6a076fb31e3df33d38747e4d6395e6be885e9eb4 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:40:13", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "64,64", + "TVAE_DECOMPRESS_DIMS": "64,64", + "TVAE_EMBEDDING_DIM": "32", + "TVAE_EPOCHS": "100" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_174013/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..164db63b9f9a2691769a87cde6ec4207ef8d4409 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_174013", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:40:13", + "ended_at": "2026-05-04T17:40:29", + "duration_sec": 16.005 + }, + "generate": { + "started_at": "2026-05-04T17:40:29", + "ended_at": "2026-05-04T17:40:34", + "duration_sec": 5.26 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/staged_features.json b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/train.csv b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/val.csv b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..d02acf7a8d601fb7def477f25e447dc0b277dcb4 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f400cbfa46a2b6a52c93e8ade8ad35565c6c90e7 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv b/timecost/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv new file mode 100644 index 0000000000000000000000000000000000000000..5c0427bc294d7b6a885a82a120413f02c0b6b483 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb907028881b168202eadcc30313f4c70d0b390f379c1f64699c77c0cb5b1a23 +size 41983 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174013/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_174013/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174013/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_174047/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..54301febcfdbada05a2cc11f3d8302fc4c95abfa --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_174047/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..975f46af9bc9246f533df0df46150305c2b5913d --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/gen_20260504_174104.log b/timecost/c2/tvae/tvae-c2-20260504_174047/gen_20260504_174104.log new file mode 100644 index 0000000000000000000000000000000000000000..03b14e21e7e199bc2f26331ac5842f0dc60facd4 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/gen_20260504_174104.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:084af1bcbd34d4f755a09dd9ffbc4cce57069fbb7bbb8c8eff2ae22b9d3e7a7a +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174047/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/models_300epochs/train_20260504_174047.log b/timecost/c2/tvae/tvae-c2-20260504_174047/models_300epochs/train_20260504_174047.log new file mode 100644 index 0000000000000000000000000000000000000000..dcae6ce961bfa13c10a2b5cf75c07f435593295b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/models_300epochs/train_20260504_174047.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:30a727c05f092232ce9b947d1ef769919085dd8c762c2eae3492cb27950a2802 +size 620 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..037655d3c719ce686f316a15170097bcd4082671 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e86ebfc162121c60718157c90bdb1625acba8d2b170c086a6eaa4a3f9adc47eb +size 344812 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..735b63a84685350d1026e3b103616fca33b64d1c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_174047/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..eb699b6178b4fec97d70cffefc82f29ebab21fa4 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:40:47", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "128,128", + "TVAE_DECOMPRESS_DIMS": "128,128", + "TVAE_EMBEDDING_DIM": "64", + "TVAE_EPOCHS": "200" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_174047/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..a409396bf2280e0bf0083aa37a0cbacc3c2a0c17 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_174047", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:40:47", + "ended_at": "2026-05-04T17:41:04", + "duration_sec": 16.508 + }, + "generate": { + "started_at": "2026-05-04T17:41:04", + "ended_at": "2026-05-04T17:41:09", + "duration_sec": 5.213 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/val.csv b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..06f4c95c62f7be8ab33f2428fbbeb6007bd51879 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ee377a50c2a2c26beaa41219a40cdf526ae72890 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174047/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv b/timecost/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv new file mode 100644 index 0000000000000000000000000000000000000000..22155eb14eab4a0343f73faaeff5b279cb5b8e30 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/tvae-c2-1382-20260504_174104.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f3d50a2af8fc7f73f7995213dac76d9a43b9b386a2008e9b0c6dc02b8446f65 +size 41450 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174047/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_174047/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174047/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_174121/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ec162da85d8047a163fdaf4dcb2a5ad436a24fed --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_174121/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..68cb971e55e8481a522d1968d1b464386b46ef56 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/gen_20260504_174137.log b/timecost/c2/tvae/tvae-c2-20260504_174121/gen_20260504_174137.log new file mode 100644 index 0000000000000000000000000000000000000000..88ffae8c24be5d2edfc2409b11cb081adb30f462 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/gen_20260504_174137.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e53e01455da4782d481bbd50839033f0111faaf92c485276e3cbd8c32d56679 +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174121/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/models_300epochs/train_20260504_174121.log b/timecost/c2/tvae/tvae-c2-20260504_174121/models_300epochs/train_20260504_174121.log new file mode 100644 index 0000000000000000000000000000000000000000..f800cda209dfb903de878f3e788d08e76c1a6e08 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/models_300epochs/train_20260504_174121.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5cb02568a6f8495e6421e9264a30e3ba31dec47cfae9988a71fefea03963a2f +size 620 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_174121/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e72dfc1e14a30f9339527324998970255cefe4d6 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de5a76a10a2d1af3ee2f46d67f3e917ab275e457bc912df0201c440f05949b7b +size 344812 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cbdc8394986794990476966ddf1e40c05ed5e826 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/val.csv", + "test_csv": 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_174121/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..e81894e726b3bc07bcc6ef203eb2627230f85dc0 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:41:21", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "500", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "300" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_174121/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..ae08bccde264898fc9c9617de2a738e0dca29f69 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_174121", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": 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b/timecost/c2/tvae/tvae-c2-20260504_174121/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..8e5f88dfc2ef54136ba069841270a9bc1107c991 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4025a7ab4cdca8637f0c0f73606fe22e2f7a7433 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174121/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv b/timecost/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv new file mode 100644 index 0000000000000000000000000000000000000000..0737ba5dfe37b5d7ec7bc0bc5c69c9ede6a47216 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/tvae-c2-1382-20260504_174137.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b2dc7b4aac308837c0d16c501f82943b483cde999544304161ff98921ed6e4c +size 41505 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174121/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_174121/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174121/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_174154/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d80d25210e0289b817b08633cb84481dad9fa72c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_174154/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..26e132045e4721046a67aaab210cdd192059dbd4 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/gen_20260504_174210.log b/timecost/c2/tvae/tvae-c2-20260504_174154/gen_20260504_174210.log new file mode 100644 index 0000000000000000000000000000000000000000..63123e59ac40ced95634de8d57657fd11d935320 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/gen_20260504_174210.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9295c44c01b1fd97e9c805d7a481d72fcd0e87d969e89140bc0d39af40dfc04a +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174154/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/models_300epochs/train_20260504_174154.log b/timecost/c2/tvae/tvae-c2-20260504_174154/models_300epochs/train_20260504_174154.log new file mode 100644 index 0000000000000000000000000000000000000000..18eba14510752169d46511d24b9fa056d4d48a17 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/models_300epochs/train_20260504_174154.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:545125874fecb8ddd76af96a5ee5e06601127a0dd2e37ce07f49191222e99dc3 +size 620 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..8a29d8a55e3370ac0603b39a5784954180c743de --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c313f03bd4c66656f8c97200f3e68fa34fe32d3c2b0f3e6faccdd71d3f96f276 +size 344876 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3b36c44b28c91fc89b7acfc0f2b620ed715caaf6 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/val.csv", + "test_csv": 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_174154/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..d7c6834b0a8e47bb7e6f56b9fd086655f8441bce --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:41:54", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_174154/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..731b3fb75f31150c5c810369fca9d9fb0a47b65e --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_174154", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:41:54", + "ended_at": "2026-05-04T17:42:10", + "duration_sec": 15.509 + }, + "generate": { + "started_at": "2026-05-04T17:42:10", + "ended_at": "2026-05-04T17:42:15", + "duration_sec": 5.341 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/staged/public/staged_features.json b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- 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b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..189b83c6cb1392d0d91b7025744650bbf87dfb55 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3b4bcd1074c6e0d6cc900926febe83d6ece42b52 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174154/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv b/timecost/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv new file mode 100644 index 0000000000000000000000000000000000000000..c83688d2010e78aeb0ecc98529fdd18e33c714d5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/tvae-c2-1382-20260504_174210.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94ee321408fa4dd67c768ba7f1522bec0d6ad27f49ecc50eb179d2ae844dd3f5 +size 41485 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174154/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_174154/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174154/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_174228/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..bc004c641ef3108ef8d5f082c796ac82f8020042 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_174228/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bb08e002da231ddadd43ffcfd0f08856e725b5fb --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/gen_20260504_174245.log b/timecost/c2/tvae/tvae-c2-20260504_174228/gen_20260504_174245.log new file mode 100644 index 0000000000000000000000000000000000000000..8c9691faa417502fa8ab6fd539913322f6ab883f --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/gen_20260504_174245.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:52660e570f2b1798f41853f45ac28d1c585f75b473dc0bafb63cea59cf9cd40d +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174228/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/models_300epochs/train_20260504_174228.log b/timecost/c2/tvae/tvae-c2-20260504_174228/models_300epochs/train_20260504_174228.log new file mode 100644 index 0000000000000000000000000000000000000000..5de5d0ad51877c8b6ec752621ca300d9862143ba --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/models_300epochs/train_20260504_174228.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58c60bb9fe86a317efabacf81546eb2b25f680a81383de073a9feceb8729d822 +size 620 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..9924f6a37f84dc82fb6b9898dcfd4dd32b199504 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15fc84c398c90a6b1dfa00fc6c2395643c8ca9901216eb12f9c29c1389c2775b +size 344876 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8b14a46f210a5c5de1d60562b8556e1663ab2d60 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_174228/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..7ba68d85fbf3be069f5726a7ab23f2656ab6a24e --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:42:28", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "256", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_174228/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..79f329aafcff2be74e9f81bdeb2d4b5a868bdd83 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_174228", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:42:28", + "ended_at": "2026-05-04T17:42:45", + "duration_sec": 16.727 + }, + "generate": { + "started_at": "2026-05-04T17:42:45", + "ended_at": "2026-05-04T17:42:50", + "duration_sec": 4.871 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/staged_features.json b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/val.csv b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..0e82075016a8326f1f9b6c83948df8bb21b14b53 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..daeb2bc660e9e321edfd0696d3d48311f9070c78 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174228/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv b/timecost/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv new file mode 100644 index 0000000000000000000000000000000000000000..1eef2401674d55c788c8457160db07fb3ca8f4e4 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/tvae-c2-1382-20260504_174245.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed8d81c7986042bdc6d46d880cf4b8ecbb4ab544eed3754733fc1ee8424db47d +size 41641 diff --git a/timecost/c2/tvae/tvae-c2-20260504_174228/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_174228/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_174228/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175027/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_175027/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8104879488e8e7e39b0f1ce35e55b7498a71c718 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175027/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175027/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_175027/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..cb7ef14cbfef58c7485e0f2c428a4de8aa54f4f7 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175027/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175027/gen_20260504_175041.log b/timecost/c2/tvae/tvae-c2-20260504_175027/gen_20260504_175041.log new file mode 100644 index 0000000000000000000000000000000000000000..fc18445fff6eeef90a8c0c788069fa1fc492dd49 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175027/gen_20260504_175041.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a01f4b4e5969a83ef737504dd36e3b52b491552504978f2029b415c1d0899625 +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175027/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_175027/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175027/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175027/models_300epochs/train_20260504_175027.log b/timecost/c2/tvae/tvae-c2-20260504_175027/models_300epochs/train_20260504_175027.log new file mode 100644 index 0000000000000000000000000000000000000000..75d081b6b67b71d98c32792a120f034523c9ed61 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175027/models_300epochs/train_20260504_175027.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27a31e992b3e274f3bef6e36d82e154a1098ee374a42da5d23ba2eb446f2e9d0 +size 615 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..b0e9191e1b878412f955dd1c2f27ac5ec132edbe --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f1e00695ed4fb39555e6e1b9efe1af61665640f750ddca0c6dd743d68f8b55a +size 223404 diff --git 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file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175027/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_175059/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9bdfcbea9bd89c4db99013044747122d14480916 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_175059/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..16748e54eb9a1ae5a6f60d11a4f52397f67560f6 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/gen_20260504_175116.log b/timecost/c2/tvae/tvae-c2-20260504_175059/gen_20260504_175116.log new file mode 100644 index 0000000000000000000000000000000000000000..d49ada4499e260e32bafa422e2d1facee36f56f7 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/gen_20260504_175116.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7273add7930ed5939cafe3d43ea1602d7885408ec8627e3f03bf7ca8aae8d86a +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_175059/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/models_300epochs/train_20260504_175100.log b/timecost/c2/tvae/tvae-c2-20260504_175059/models_300epochs/train_20260504_175100.log new file mode 100644 index 0000000000000000000000000000000000000000..f4b363f0e40c680a056ef1415fbb43824b139ec1 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/models_300epochs/train_20260504_175100.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13d7f892be66207b52dd8124e5bbe5442b4e8fcd0668d9a502ea70381c6bc846 +size 619 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c1d147d3d16f2d818036c6c7e229480c733a81f1 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05b303a596195c046df86ed9e9dd4aeac7f0072cc58d433b73a2cbafe0c93e12 +size 319852 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a1dd8f289c4edf67a2cd2fb29e1d242f70efeb9a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_175059/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..9d5bf1b03dd5312dc8f0b6b480c2d8333e42ff85 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:51:00", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "128,128", + "TVAE_DECOMPRESS_DIMS": "128,128", + "TVAE_EMBEDDING_DIM": "64", + "TVAE_EPOCHS": "200" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_175059/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..498a194563371d3471c9dc07442f9249c2fb375e --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_175059", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": 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b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- 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b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..f39e1ab317ffaaa0c489f1d152d3541f687bec20 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a3d86b3bcd4548705a5cc753afef3a6d4838e19e --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv b/timecost/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv new file mode 100644 index 0000000000000000000000000000000000000000..ed1aaacefa064433627b31272a6a0570e43c431a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31e5e3a91a0418044bc61b09e258d72356c52e332b3e614c0699f5f2962fe56c +size 41725 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175134/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_175134/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c5f94d959d7eeaa29f9d882a3763d81f02da8b2e --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175134/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175134/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_175134/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..091e65996ed24436d0d0cec26d6e97b2d8a731cb --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175134/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175134/gen_20260504_175151.log b/timecost/c2/tvae/tvae-c2-20260504_175134/gen_20260504_175151.log new file mode 100644 index 0000000000000000000000000000000000000000..7eadd238040dc89416ea5fb86cf52da087bf5a71 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175134/gen_20260504_175151.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40511260bd9eb00dcc35cfc95b37da03e79a617b156ee312d5e0a42d69f03ec5 +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175134/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_175134/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175134/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": 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"class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv b/timecost/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv new file mode 100644 index 0000000000000000000000000000000000000000..f4b76c08fd9e79d194664a18efca7c2da7973dba --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd6e35a64f02e454c60026dced645a026e8ff462523a2befdd87293700edee0e +size 41542 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_175208/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..15ffa754523d7c1b3a6284549e839113f4d69d20 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_175208/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e493362becc793f8b983b6bd6bfc45c3b7fd1422 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/gen_20260504_175232.log b/timecost/c2/tvae/tvae-c2-20260504_175208/gen_20260504_175232.log new file mode 100644 index 0000000000000000000000000000000000000000..e96d2b0bb30e659460d8b3287452895d64ac6b35 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/gen_20260504_175232.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b8ff2fdb44fa6e18bdeb7130618b3ebdd47cd7c82320e407c9cad2778a38258b +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_175208/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/models_300epochs/train_20260504_175208.log b/timecost/c2/tvae/tvae-c2-20260504_175208/models_300epochs/train_20260504_175208.log new file mode 100644 index 0000000000000000000000000000000000000000..bd12ed223dd5717571a179c201d36a613606c8c5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/models_300epochs/train_20260504_175208.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:59dac04cf25233b63c8854c0f2a0cc19f76ce5ccba7c3ea8bc62496c7595041f +size 620 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..1d25793602500f1b942c6b9c92013786108ee57c --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe6ba0f4654657fae90688f2455aa63a74fd0dbacc1fa20576c3921a6e802c90 +size 675500 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6600bdfdf9a7e545696eeb926d8ceb33799e8760 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_175208/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..1324de920f33ee421e8de3a00d82d7ad3411227a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:52:08", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_175208/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..2571fd685f2ca86d9026a5aeb4b2d5d2135271e0 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_175208", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": 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b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- 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b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..dd934cb8736896aea413f6c1ee693f46b6a150f2 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b2dfb70e860332f2d4729abbb0e5335fa3f2fa58 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175208/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv b/timecost/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv new file mode 100644 index 0000000000000000000000000000000000000000..f87119bdc86f96e711c84b1e0e548aef02373279 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/tvae-c2-1382-20260504_175232.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfa0ee0f5edf291d4a1440b73cc43eb64a1bef6799cdf66405b3384a12031f3f +size 41049 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175208/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_175208/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175208/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/_tvae_generate.py b/timecost/c2/tvae/tvae-c2-20260504_175249/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e07148c1ba4ddca992b0f7e20cf0df0dcdeb69c0 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt") +total = 1382 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/_tvae_train.py b/timecost/c2/tvae/tvae-c2-20260504_175249/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c032455229e3f6f5d8909e0b2a91cfe61ca5b48b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/gen_20260504_175313.log b/timecost/c2/tvae/tvae-c2-20260504_175249/gen_20260504_175313.log new file mode 100644 index 0000000000000000000000000000000000000000..aba55b7de57d2847ab08ab93541dc182fd99c817 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/gen_20260504_175313.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6fd3a537a0edd5fbdc6f85ba4458aafb0b13c7a9e94d15ec3e50395e2b28182b +size 404 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/input_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_175249/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9e28a5421f2176e174a0659e66a40054269fb2ac --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "exists": true, + "size": 42948, + "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "exists": true, + "size": 5349, + "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv", + "exists": true, + "size": 5448, + "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json", + "exists": true, + "size": 3240, + "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json", + "exists": true, + "size": 3731, + "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009" + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/models_300epochs/train_20260504_175249.log b/timecost/c2/tvae/tvae-c2-20260504_175249/models_300epochs/train_20260504_175249.log new file mode 100644 index 0000000000000000000000000000000000000000..4d871338efdc74e0d6149b21cbb781486f4d93bb --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/models_300epochs/train_20260504_175249.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36225451e64c28e079e1ffc5a02a1ff4755ba438ebe0f77dc8c5e87cafc124df +size 619 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt b/timecost/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c83492b85c2b473e1c5456cd62dd742606c08223 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6784c6b8e4a0d3d2654f738df5614002347068fc0f6512daf0970b15d29c6106 +size 806572 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/normalized_schema_snapshot.json b/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,144 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json b/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c2", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..75e186f4667b8837057d5c45d32abccf2e5dd0e4 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json @@ -0,0 +1,149 @@ +{ + "dataset_id": "c2", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/run_config.json b/timecost/c2/tvae/tvae-c2-20260504_175249/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..fc3643ef5c96749e6ef0e5aacb3ae517516134be --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:52:49", + "dataset_id": "c2", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "c2", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 1382, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json", + "target_column": "class", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "256", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/runtime_result.json b/timecost/c2/tvae/tvae-c2-20260504_175249/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..0cb85f7daa3338b4f0cbada27e90ac334c284ed3 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "run_id": "tvae-c2-20260504_175249", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:52:49", + "ended_at": "2026-05-04T17:53:13", + "duration_sec": 23.837 + }, + "generate": { + "started_at": "2026-05-04T17:53:13", + "ended_at": "2026-05-04T17:53:18", + "duration_sec": 5.187 + } + } +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "buying", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "maint", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "doors", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "persons", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "lug_boot", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "safety", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- 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b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_report.json b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..64b18a507f528876aa0c3b86fcbc3c69370770e1 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_transforms_applied.json b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ff76871e41b31780ffa015bfa09409fab9868e57 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/staged/tvae/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "buying", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "med", + "high", + "low" + ] + } + }, + { + "name": "maint", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "vhigh", + "low", + "med", + "high" + ] + } + }, + { + "name": "doors", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "2", + "5more", + "3", + "4" + ] + } + }, + { + "name": "persons", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "2", + "4", + "more" + ] + } + }, + { + "name": "lug_boot", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "small", + "big", + "med" + ] + } + }, + { + "name": "safety", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.002171, + "example_values": [ + "low", + "high", + "med" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.002894, + "example_values": [ + "unacc", + "good", + "acc", + "vgood" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175249/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv b/timecost/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae3153f5e356d1b61c8b9d640e35d36e141387c2 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/tvae-c2-1382-20260504_175313.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef368b14d1cd6f881c56fcba6683fbb15c133cf8ab7331cea2484b675b53bfc6 +size 41456 diff --git a/timecost/c2/tvae/tvae-c2-20260504_175249/tvae_metadata.json b/timecost/c2/tvae/tvae-c2-20260504_175249/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..183d627ebc98e89233655ec98c9dcbc528c4c328 --- /dev/null +++ b/timecost/c2/tvae/tvae-c2-20260504_175249/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "buying", + "type": "categorical" + }, + { + "name": "maint", + "type": "categorical" + }, + { + "name": "doors", + "type": "categorical" + }, + { + "name": "persons", + "type": "categorical" + }, + { + "name": "lug_boot", + "type": "categorical" + }, + { + "name": "safety", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/_arf_generate.py b/timecost/c7/arf/arf-c7-20260429_030948/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..44f72d63db070539b9ff78e9f4150ec2dff090c7 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(10368) +c_csv = "/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c7' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/arf-c7-10368-20260429_031026.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/arf-c7-10368-20260429_031026.csv") diff --git a/timecost/c7/arf/arf-c7-20260429_030948/_arf_train.py b/timecost/c7/arf/arf-c7-20260429_030948/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..91239e3716171e5b18e61a1140b0bcbdeb926dce --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/arf_model.pkl") diff --git a/timecost/c7/arf/arf-c7-20260429_030948/arf-c7-10368-20260429_031026.csv b/timecost/c7/arf/arf-c7-20260429_030948/arf-c7-10368-20260429_031026.csv new file mode 100644 index 0000000000000000000000000000000000000000..0f1aef2f26f514478160dc5e75b581aa0a1f8367 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/arf-c7-10368-20260429_031026.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66bc5cb299efafaea27685aa7dabdd62ede6e19adf9aae833cfe6c8c6aa6b81c +size 846612 diff --git a/timecost/c7/arf/arf-c7-20260429_030948/arf_model.pkl b/timecost/c7/arf/arf-c7-20260429_030948/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3008fca0f2b479982cb9cb9836163225c4b6000f --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8786230db2bbc8dec7374177bbdc74810e36d36ad685dcdba983ed5e6e3f84c +size 44356271 diff --git a/timecost/c7/arf/arf-c7-20260429_030948/gen_20260429_031026.log b/timecost/c7/arf/arf-c7-20260429_030948/gen_20260429_031026.log new file mode 100644 index 0000000000000000000000000000000000000000..1d997be670b8ef4c9905653e9acc8324604abbca --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/gen_20260429_031026.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f94fa2b074d235488ffe68c5133b8e6c8361d97a22d7ac461165f2b363bbbaa1 +size 3250 diff --git a/timecost/c7/arf/arf-c7-20260429_030948/input_snapshot.json b/timecost/c7/arf/arf-c7-20260429_030948/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f1ae1af8b683e62a8986562037668d2d1c4d2cbf --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv", + "exists": true, + "size": 107327, + "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json", + "exists": true, + "size": 4014, + "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json", + "exists": true, + "size": 4759, + "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd" + } + } +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/public_gate/normalized_schema_snapshot.json b/timecost/c7/arf/arf-c7-20260429_030948/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/public_gate/public_gate_report.json b/timecost/c7/arf/arf-c7-20260429_030948/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/public_gate/staged_input_manifest.json b/timecost/c7/arf/arf-c7-20260429_030948/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6d7c9cc4cce18fe9a5b514be511a77b1d76a4496 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/runtime_result.json b/timecost/c7/arf/arf-c7-20260429_030948/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..aa1de66407fea7feb131a64ed46a998d7297591a --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c7", + "model": "arf", + "run_id": "arf-c7-20260429_030948", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/arf-c7-10368-20260429_031026.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/arf_model.pkl" + } +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/adapter_report.json b/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..ef6561b5c3c9e15fe7e39facbf75637087678d81 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/adapter_transforms_applied.json b/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/model_input_manifest.json b/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4f6e17545c099f677f73125419e8e3fc430905b7 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/staged/arf/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "arf", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_030948/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/staged/public/staged_features.json b/timecost/c7/arf/arf-c7-20260429_030948/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_030948/staged/public/test.csv b/timecost/c7/arf/arf-c7-20260429_030948/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/timecost/c7/arf/arf-c7-20260429_030948/staged/public/train.csv b/timecost/c7/arf/arf-c7-20260429_030948/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/timecost/c7/arf/arf-c7-20260429_030948/staged/public/val.csv b/timecost/c7/arf/arf-c7-20260429_030948/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/timecost/c7/arf/arf-c7-20260429_030948/train_20260429_030948.log b/timecost/c7/arf/arf-c7-20260429_030948/train_20260429_030948.log new file mode 100644 index 0000000000000000000000000000000000000000..d2465360e17472280cb92a92b76b7ed4434e39bc --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_030948/train_20260429_030948.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:872a5d80f7aabdecef7e9cb7ef3d751219c9b6a70b1427bda23e39a521d5012c +size 497 diff --git a/timecost/c7/arf/arf-c7-20260429_031427/_arf_generate.py b/timecost/c7/arf/arf-c7-20260429_031427/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8079ff32c83c3e892dbf0f99fbb2097efb1dc824 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(10368) +c_csv = "/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'c7' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/arf-c7-10368-20260429_031508.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/arf-c7-10368-20260429_031508.csv") diff --git a/timecost/c7/arf/arf-c7-20260429_031427/_arf_train.py b/timecost/c7/arf/arf-c7-20260429_031427/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c859b5bcb717b5d63f72a4bf258a26c51e41bb22 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/arf_model.pkl") diff --git a/timecost/c7/arf/arf-c7-20260429_031427/arf-c7-10368-20260429_031508.csv b/timecost/c7/arf/arf-c7-20260429_031427/arf-c7-10368-20260429_031508.csv new file mode 100644 index 0000000000000000000000000000000000000000..d0f07209c63b168554fcbb27516022809dc43a56 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/arf-c7-10368-20260429_031508.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3134ad72093b490836b740edcce2a9f1b54af146b5b634edecb40bedc58f5f3 +size 847327 diff --git a/timecost/c7/arf/arf-c7-20260429_031427/arf_model.pkl b/timecost/c7/arf/arf-c7-20260429_031427/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..f0b3d05774b8a2a35970664377cfb70749cbbe05 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b090af3e7010af1dc06c6e8a03175ca70bb9bb84f3d2eca7127e57bc0ecb7ebc +size 44391799 diff --git a/timecost/c7/arf/arf-c7-20260429_031427/gen_20260429_031508.log b/timecost/c7/arf/arf-c7-20260429_031427/gen_20260429_031508.log new file mode 100644 index 0000000000000000000000000000000000000000..f0f58ab28c73e36160cf8b30c4324cecb3c1960b --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/gen_20260429_031508.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2768b1d2fb4bd05c8f06205fd11c0fab338aa3aa9156048dc145708e6ffca906 +size 3251 diff --git a/timecost/c7/arf/arf-c7-20260429_031427/input_snapshot.json b/timecost/c7/arf/arf-c7-20260429_031427/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f1ae1af8b683e62a8986562037668d2d1c4d2cbf --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv", + "exists": true, + "size": 107327, + "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json", + "exists": true, + "size": 4014, + "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json", + "exists": true, + "size": 4759, + "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd" + } + } +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/public_gate/normalized_schema_snapshot.json b/timecost/c7/arf/arf-c7-20260429_031427/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/public_gate/public_gate_report.json b/timecost/c7/arf/arf-c7-20260429_031427/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/public_gate/staged_input_manifest.json b/timecost/c7/arf/arf-c7-20260429_031427/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..df1866933f8b7c40d7c77b7bf0cbba209ee2440e --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/runtime_result.json b/timecost/c7/arf/arf-c7-20260429_031427/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7fd0aa517559af04f9f6143cf52296c666249364 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c7", + "model": "arf", + "run_id": "arf-c7-20260429_031427", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/arf-c7-10368-20260429_031508.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/arf_model.pkl" + } +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/adapter_report.json b/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3eefd15ddbd34c1475f204a69edda1786692e452 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/adapter_transforms_applied.json b/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/model_input_manifest.json b/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b38798668da3f8e6d341a771d620d6b72dc08a1d --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/staged/arf/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "arf", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/arf/arf-c7-20260429_031427/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/staged/public/staged_features.json b/timecost/c7/arf/arf-c7-20260429_031427/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c7/arf/arf-c7-20260429_031427/staged/public/test.csv b/timecost/c7/arf/arf-c7-20260429_031427/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/timecost/c7/arf/arf-c7-20260429_031427/staged/public/train.csv b/timecost/c7/arf/arf-c7-20260429_031427/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/timecost/c7/arf/arf-c7-20260429_031427/staged/public/val.csv b/timecost/c7/arf/arf-c7-20260429_031427/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/timecost/c7/arf/arf-c7-20260429_031427/train_20260429_031427.log b/timecost/c7/arf/arf-c7-20260429_031427/train_20260429_031427.log new file mode 100644 index 0000000000000000000000000000000000000000..cc12ff77f3f7d475e2e8be7055e3000dfcb8b2db --- /dev/null +++ b/timecost/c7/arf/arf-c7-20260429_031427/train_20260429_031427.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0d935bc8eb8e8cd8f3afd6a152d66de11e20ed7cf990d2a7166afae26cb856e +size 496 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_generate.py b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..78641417cc9d3360992d592f1855feaa882ce648 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(10368) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv") diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_train.py b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..11a124de915aefe335a4cace17f610c3d2b370c2 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl") diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv new file mode 100644 index 0000000000000000000000000000000000000000..34e00fb879c4d92a49a889129b1578b2faa354dc --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ec483f732565f4c875574544cd638c236741ab73b64670c524b307e81d3762a +size 847720 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_coltypes.json b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..6408be4c6bd3187fdebffa9c5b618f974690b33d --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_coltypes.json @@ -0,0 +1,41 @@ +{ + "columns": [ + { + "name": "parents", + "type": "categorical" + }, + { + "name": "has_nurs", + "type": "categorical" + }, + { + "name": "form", + "type": "categorical" + }, + { + "name": "children", + "type": "categorical" + }, + { + "name": "housing", + "type": "categorical" + }, + { + "name": "finance", + "type": "categorical" + }, + { + "name": "social", + "type": "categorical" + }, + { + "name": "health", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..291862b34113b529e8bf02f930bf59f1fe805dee --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:342563385d822291efea03ef34858a3fa7a9c37695d1d9aa761ad616bb14bd4f +size 5680 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/const_cols.json b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/gen_20260429_031053.log b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/gen_20260429_031053.log new file mode 100644 index 0000000000000000000000000000000000000000..dcd9e42a58b26f8bc5cdbd3e21bc7fa629bf9246 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/gen_20260429_031053.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e814c1f07c97634a205072c6af264b79105ca899b88cdfec9e2a14198170e22e +size 3664 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/input_snapshot.json b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b61f053445c3620fe2db11628eda0f82572c1916 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + 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+size 106192 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/train_20260429_031038.log b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/train_20260429_031038.log new file mode 100644 index 0000000000000000000000000000000000000000..9104255c0a1b829752d5d6a5b2aff7a04598814b --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031038/train_20260429_031038.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4111c669d5532e874b7f7b68b102dfe387746d6756cffa5d3ad8d7cb0ea7f147 +size 3741 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_generate.py b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..2f68468ec9c3fb4110422420ec5c9dc1886f8c18 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(10368) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv") diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_train.py b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4b564a2f8e858b3932ee00b96d13a5fd46926031 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl") diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv new file mode 100644 index 0000000000000000000000000000000000000000..524cf9d802c20f757784f989c1f23f22ff5f9f48 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39ab8a39e164ff03b6176d8e2ecb2cd4bcaad07a36ece74636a690a5b5fc0128 +size 848576 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_coltypes.json b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..6408be4c6bd3187fdebffa9c5b618f974690b33d --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_coltypes.json @@ -0,0 +1,41 @@ +{ + "columns": [ + { + "name": "parents", + "type": "categorical" + }, + { + "name": "has_nurs", + "type": "categorical" + }, + { + "name": "form", + "type": "categorical" + }, + { + "name": "children", + "type": "categorical" + }, + { + "name": "housing", + "type": "categorical" + }, + { + "name": "finance", + "type": "categorical" + }, + { + "name": "social", + "type": "categorical" + }, + { + "name": "health", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..291862b34113b529e8bf02f930bf59f1fe805dee --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:342563385d822291efea03ef34858a3fa7a9c37695d1d9aa761ad616bb14bd4f +size 5680 diff --git 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b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b61f053445c3620fe2db11628eda0f82572c1916 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv", + "exists": true, + "size": 107327, + "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json", + "exists": true, + "size": 4014, + "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json", + "exists": true, + "size": 4759, + "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd" + } + } +} \ No newline at end of file diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/normalized_schema_snapshot.json b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + 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"categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": 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"model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_transforms_applied.json b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json new file mode 100644 index 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b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/train_20260429_031537.log b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/train_20260429_031537.log new file mode 100644 index 0000000000000000000000000000000000000000..5bdc045b3f9046a909b43dabaf0f8a3f80061ee2 --- /dev/null +++ b/timecost/c7/bayesnet/bayesnet-c7-20260429_031537/train_20260429_031537.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1788e94eba6e542f6afdf6c0054a1d521deae3fba20e72212282c5bd06f20d84 +size 3741 diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/_ctgan_generate.py b/timecost/c7/ctgan/ctgan-c7-20260429_032054/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..07dcc9743b472953e91f3e07ecf5bba82a9e4e69 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/ctgan_300epochs.pt") +total = 10368 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv") \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv b/timecost/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv new file mode 100644 index 0000000000000000000000000000000000000000..cf988bee2b95867429d9dbc730c1922a9bf65abc --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6292ae1576f8682712eb05a628de61384dab5cf211e620852f27aaea02e87599 +size 840588 diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/ctgan_metadata.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..83f41ad233860beaed71ee8e4637e99ce522d64a --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/ctgan_metadata.json @@ -0,0 +1,40 @@ +{ + "columns": [ + { + "name": "parents", + "type": "categorical" + }, + { + "name": "has_nurs", + "type": "categorical" + }, + { + "name": "form", + "type": "categorical" + }, + { + "name": "children", + "type": "categorical" + }, + { + "name": "housing", + "type": "categorical" + }, + { + "name": "finance", + "type": "categorical" + }, + { + "name": "social", + "type": "categorical" + }, + { + "name": "health", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/gen_20260429_032437.log b/timecost/c7/ctgan/ctgan-c7-20260429_032054/gen_20260429_032437.log new file mode 100644 index 0000000000000000000000000000000000000000..fce5186d026734b62295d4253fb1492d2bfb5d6f --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/gen_20260429_032437.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1d7b26ffe98c9c4f15c6f3f4fcf38ed6984c6577f5b8686141b8abea66e75a8 +size 563 diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/input_snapshot.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..7f121e36aa5b57e68554114fd4795c6b6e40f143 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv", + "exists": true, + "size": 107327, + "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json", + "exists": true, + "size": 4014, + "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json", + "exists": true, + "size": 4759, + "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd" + } + } +} \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/ctgan_300epochs.pt b/timecost/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/ctgan_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..781fa466df5e3a50520c45aa00d33a937475bb15 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/ctgan_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a47a60b20791694378a281f78a743706e39736e244a1eab9e74ecda1c047290 +size 1720675 diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/train_20260429_032054.log b/timecost/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/train_20260429_032054.log new file mode 100644 index 0000000000000000000000000000000000000000..499f48810a7bdebbe86e48a8bb854f4e60eadb91 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/train_20260429_032054.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f792127bbf2b816a8b9651c667011239008e28916d147130fc115dd27b3304c +size 2137 diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/normalized_schema_snapshot.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/public_gate_report.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/staged_input_manifest.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..43fc67deb419650a4887a1dd4befc660e50d5a38 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": 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"role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/runtime_result.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..4c3164ec0de0618b4e93ba245a13645a0a1bf709 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c7", + "model": "ctgan", + "run_id": "ctgan-c7-20260429_032054", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/ctgan_300epochs.pt" + } +} \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/adapter_report.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..003ee3427cc830d7719f286a9fbd99b913a2273d --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/adapter_transforms_applied.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/model_input_manifest.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..faa34452a508e683536b48bace7db8669cf8033b --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/ctgan/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "ctgan", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/staged_features.json b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/test.csv b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/train.csv b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/val.csv b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/timecost/c7/ctgan/ctgan-c7-20260429_032054/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_X_host.npy b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..a69a99ccbffbc951a8691a96b79ee4ef0725d1a4 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:704bab13ead16cf08a6c8521c33a1e9c77ec3c94ed9b6b190511a1b1d55d7ba1 +size 373376 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_gen.py b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..46312bf3b3bcb880551505e16637e06b59eed3d8 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(10368)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/forest-c7-10368-20260430_054452.csv', index=False) +print("saved", len(df)) diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_meta_host.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..a6c4906bd4050053b5e4d3027aa1125b5feae0cf --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["parents", "has_nurs", "form", "children", "housing", "finance", "social", "health", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5, 6, 7]} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_train.py b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e712ce764a6df3d2f4fe87c74fff051644e3b0b3 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/forestdiffusion_model.joblib') diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/forest-c7-10368-20260430_054452.csv b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/forest-c7-10368-20260430_054452.csv new file mode 100644 index 0000000000000000000000000000000000000000..31b640988ec67b7d4650796f2ab808a2e5f0f5c2 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/forest-c7-10368-20260430_054452.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:703360b58e9d53fe5254592edfe3c9a39155fde5e78017c6dcf544c20b00ab2e +size 504811 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/forestdiffusion_model.joblib b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..ed64701a672f42334d5944dee00838abe86b5b41 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b6b0fa6051d5a485cbfd393e980f1ac6f949cdb1b3f91b49c4e55d5f637a0be +size 191178113 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/gen_20260430_054452.log b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/gen_20260430_054452.log new file mode 100644 index 0000000000000000000000000000000000000000..85d5fbcd5693428521cad011040017867da1cd84 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/gen_20260430_054452.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c49c54437191843a32a8a9e36c89644373d4384f753025182b0e4b2bed2e3f7 +size 294 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/input_snapshot.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..be64076e7dd6fc44b3a431506cf263059963b329 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv", + "exists": true, + "size": 107327, + "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json", + "exists": true, + "size": 4014, + "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json", + "exists": true, + "size": 4759, + "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd" + } + } +} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/models_fd/model.joblib b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..ed64701a672f42334d5944dee00838abe86b5b41 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b6b0fa6051d5a485cbfd393e980f1ac6f949cdb1b3f91b49c4e55d5f637a0be +size 191178113 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/normalized_schema_snapshot.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/public_gate_report.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/staged_input_manifest.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9ff469cc5277035e59f661925b42d0649ffb82c4 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + 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"name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/runtime_result.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..e8e5df3181b3d566e71a9683795f4940a86c8555 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c7", + "model": "forestdiffusion", + "run_id": "forest-c7-20260429_210000", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/forest-c7-10368-20260430_054452.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/models_fd/model.joblib" + } +} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/adapter_report.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..8edd1b3e328a6c6f7ab4a0c36952b2f6664a1830 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/adapter_transforms_applied.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/model_input_manifest.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..649934e833c5829dfd7dbf3d1aa640383f2bfb4e --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/forestdiffusion/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "forestdiffusion", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260429_210000/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/staged_features.json b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/test.csv b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/train.csv b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/val.csv b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260429_210000/train_20260429_210000.log b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/train_20260429_210000.log new file mode 100644 index 0000000000000000000000000000000000000000..b2a94990d75bb5355618a5d5fa1c99123b73cc3c --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260429_210000/train_20260429_210000.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9226abaddf0b235c55e758a858abb878322b6fa7ce4917212f87f4237f1dcfa3 +size 426 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_X_host.npy b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..a69a99ccbffbc951a8691a96b79ee4ef0725d1a4 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:704bab13ead16cf08a6c8521c33a1e9c77ec3c94ed9b6b190511a1b1d55d7ba1 +size 373376 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_gen.py b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..19668c2b18fc68dedbc74fdf00e077f7ac7bd8b1 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(10368)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/forest-c7-10368-20260430_060359.csv', index=False) +print("saved", len(df)) diff --git a/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_meta_host.json b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..a6c4906bd4050053b5e4d3027aa1125b5feae0cf --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["parents", "has_nurs", "form", "children", "housing", "finance", "social", "health", "class"], "cat_indexes": [0, 1, 2, 3, 4, 5, 6, 7]} \ No newline at end of file diff --git a/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_train.py b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..61981b375c1363c0bf6291fe77cabf72234fd20b --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c7/forestdiffusion/forest-c7-20260430_060233/forestdiffusion_model.joblib') diff --git a/timecost/c7/forestdiffusion/forest-c7-20260430_060233/forest-c7-10368-20260430_060359.csv b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/forest-c7-10368-20260430_060359.csv new file mode 100644 index 0000000000000000000000000000000000000000..1d46c5ff99fff9ad97525e576432970485c09b17 --- /dev/null +++ b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/forest-c7-10368-20260430_060359.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9027b66b5e58a47c6c08de377244a98e3d41fa6dd83a432316aefb7e7bed716 +size 504511 diff --git a/timecost/c7/forestdiffusion/forest-c7-20260430_060233/forestdiffusion_model.joblib b/timecost/c7/forestdiffusion/forest-c7-20260430_060233/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..ed64701a672f42334d5944dee00838abe86b5b41 --- /dev/null +++ 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b/timecost/c7/realtabformer/rtf-c7-20260429_070344/train_20260429_070344.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c729c4774de7f9400da97b1cd3ce45a56835d11ee8ee0de191e7e09f1eda6152 +size 1439900 diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/_tabbyflow_gen.py b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..9ad288fec400c0668155bb19d8de6e9b07caa996 --- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c7" +src = r"/work/output-Benchmark-trainonly-v1/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_c7/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(10368)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabbyflow-c7-10368-20260429_041920.csv") diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/_tabbyflow_train.py b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9484ef96dd6870b7e56330dbf2a0dd197072132f --- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c7" +src = r"/work/output-Benchmark-trainonly-v1/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/gen_20260429_041920.log b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/gen_20260429_041920.log new file mode 100644 index 0000000000000000000000000000000000000000..9ac03c6fda4b58aba68c9a602fc3b49495141ac9 --- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/gen_20260429_041920.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5e97cf73bc23ea58d1eb7881bc42f26521eef278a43d7d496799711f96af4b52 +size 3177 diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/input_snapshot.json b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/input_snapshot.json 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b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba6482dc6705ea0ac5fc0355391800cb0937fe4c3221baa21bc1f47beec8c260 +size 128 diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/X_num_val.npy b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/X_num_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..05368376414bc8e2221b9744c2e3746a587e749c --- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/X_num_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba6482dc6705ea0ac5fc0355391800cb0937fe4c3221baa21bc1f47beec8c260 +size 128 diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/info.json b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/info.json new file mode 100644 index 0000000000000000000000000000000000000000..b7dce7397c47eaf0d0ac811c5e11699e183be0b7 --- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/info.json @@ -0,0 +1,104 @@ +{ + "name": "pipeline_c7", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 8, + "train_size": 10368, + "test_num": 10368, + "val_num": 10368, + "train_num": 10368, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7 + ], + "target_col_idx": [ + 8 + ], + "column_names": [ + "parents", + "has_nurs", + "form", + "children", + "housing", + "finance", + "social", + "health", + "class" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "categorical" + }, + "1": { + "sdtype": "categorical" + }, + "2": { + "sdtype": "categorical" + }, + "3": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "categorical" + }, + "7": { + "sdtype": "categorical" + }, + "8": { + "sdtype": "categorical" + } + } + }, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6, + "7": 7, + "8": 8 + }, + "inverse_idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6, + "7": 7, + "8": 8 + }, + "idx_name_mapping": { + "0": "parents", + "1": "has_nurs", + "2": "form", + "3": "children", + "4": "housing", + "5": "finance", + "6": "social", + "7": "health", + "8": "class" + }, + "n_classes": 5 +} \ No newline at end of file diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/real.csv 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+size 269700 diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/val.csv b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..38ea9e3e3a618729b1d98a0d1efb1a2e98f2cbc8 --- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ba16ecd55573b2bdc268f8a19464703e0109546f5951689c6d9a99eb5c6cc09 +size 269700 diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/y_test.npy b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..91a84420670ea33a588c708837d1c1fff56c64cd --- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/y_test.npy @@ -0,0 +1,3 @@ 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--- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/tabular_bundle/pipeline_c7/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99ac41d294af4eecd6e8a45863077f58b49456e9d0e055344706824cbb034964 +size 83072 diff --git a/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/train_20260429_041030.log b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/train_20260429_041030.log new file mode 100644 index 0000000000000000000000000000000000000000..572e7a80a24b5d4995f0aede17cc50fca294934b --- /dev/null +++ b/timecost/c7/tabbyflow/tabbyflow-c7-20260429_041029/train_20260429_041030.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc98d78a1665a8ecd34fdf05631ab0fb93fd7e961a785cba1ba557d521b9a74f +size 452556 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/_tabddpm_sample_r0.py b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..6cee16423f22d5273df79a48b8f237db4b9d4d7b --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 10368 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/config_sample_20260429_052217_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/tabddpm-c7-10368-20260429_052217.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/tabddpm-c7-10368-20260429_052217.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/_tabddpm_train.py b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d8e86af9f02664794a8c84227a73652c2b8262e6 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/config.toml b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..625b4ad477eb3350597a79bf0b7db4c1111f26e6 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 8 +num_classes = 5 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/config_sample_20260429_052217_r0.toml b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/config_sample_20260429_052217_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..137b608a092d1e2349dd96cfb1342a65e6f832d1 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/config_sample_20260429_052217_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 8 +num_classes = 5 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 10368 +batch_size = 1000 +seed = 0 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/X_cat_train.npy b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..5088dd2f36e0ffeae013fe0c5cdc769d9d88d33b --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe170020f0e97be3d763c4207ff95efc4e981264660ef6397fcf5f6e62d31eec +size 663680 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/info.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..348c194d9d198ccbef46a7046431163d973ec28c --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/info.json @@ -0,0 +1,33 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 8, + "train_size": 10368, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7 + ], + "target_col_idx": [ + 8 + ], + "column_names": [ + "parents", + "has_nurs", + "form", + "children", + "housing", + "finance", + "social", + "health", + "class" + ], + "num_classes": 5 +} \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/y_train.npy b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..91a84420670ea33a588c708837d1c1fff56c64cd --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99ac41d294af4eecd6e8a45863077f58b49456e9d0e055344706824cbb034964 +size 83072 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/gen_20260429_052217_r0.log b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/gen_20260429_052217_r0.log new file mode 100644 index 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a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/X_cat_train.npy b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..a98dcbd9386984ce5744920900b9198619f13c49 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:16be36641783ba511d1c6a52baffaf0e9505ea21441908bf4c3fc36523972099 +size 166337 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/X_cat_unnorm.npy b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..14f18f5c90392d0eef416d0ca0c335c20bc18a2f --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37912a71902967a76f0872c545f8c8886f4a690441621c9e4db08306fd41cb01 +size 663680 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/config.toml b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..137b608a092d1e2349dd96cfb1342a65e6f832d1 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 8 +num_classes = 5 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 10368 +batch_size = 1000 +seed = 0 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/info.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..348c194d9d198ccbef46a7046431163d973ec28c --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/info.json @@ -0,0 +1,33 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 8, + "train_size": 10368, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7 + ], + "target_col_idx": [ + 8 + ], + "column_names": [ + "parents", + "has_nurs", + "form", + "children", + "housing", + "finance", + "social", + "health", + "class" + ], + "num_classes": 5 +} \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/loss.csv b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..7a67948430aeb22a5031a563520388e47ccfb9ff --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:439e8e111684e872c6aa50f895f54ce61a8afc53de6b21b6dd9ac899635b99d8 +size 1255 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/model.pt b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..4006cc0798dcc51e265bdf98b7d51e6000d091b0 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:50a336bd69bb6cc75541f8f634083ff597f3d334c11e6bf049b0c2718fee85f2 +size 576662 diff --git 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a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/normalized_schema_snapshot.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + 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0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/staged_input_manifest.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..fb711af1709273878b4b21e508316a04db344b57 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": 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"role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/runtime_result.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..8f2230e71cc9df598e3315aaddf717d268a082cc --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c7", + "model": "tabddpm", + "run_id": "tabddpm-c7-20260429_052039", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/tabddpm-c7-10368-20260429_052217.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039" + } +} \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/staged_features.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/test.csv b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/train.csv b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/val.csv b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/adapter_report.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..4888184f75e5df42a3807e08241d0e5804849bc6 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/adapter_transforms_applied.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/model_input_manifest.json b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..80c4e4287624e3d3628620de7cb7fefa5466c1cf --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "tabddpm", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabddpm/tabddpm-c7-20260429_052039/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/tabddpm-c7-10368-20260429_052217.csv b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/tabddpm-c7-10368-20260429_052217.csv new file mode 100644 index 0000000000000000000000000000000000000000..ea759a732aef85c64642b1015a371a628db4f023 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/tabddpm-c7-10368-20260429_052217.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fca12de9a3621b5e41a7f0c4952f8cffd66ae002b2e412abe4df34493b1a18f1 +size 373315 diff --git a/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/train_20260429_052039.log b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/train_20260429_052039.log new file mode 100644 index 0000000000000000000000000000000000000000..c28b18e3f62d9b224c51346f42e6e7f5e78b4702 --- /dev/null +++ b/timecost/c7/tabddpm/tabddpm-c7-20260429_052039/train_20260429_052039.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd88eb16a0a4da60df76f1cab22fee5c923aa05babd86a292910fda970565c1e +size 1080 diff --git a/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/_tabdiff_gen.py b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..a958e904213e983a883c85d2d4a121a13d96fef0 --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_ds" +src = r"/work/output-Benchmark-trainonly-v1/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_ds/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(10368)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c7/tabdiff/tabdiff-c7-20260429_033922/tabdiff-c7-10368-20260429_034804.csv") diff --git a/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/_tabdiff_train.py b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..db18c0f7ca9f99e86380416e384e03dba8a006a7 --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_ds" +src = r"/work/output-Benchmark-trainonly-v1/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/gen_20260429_034804.log b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/gen_20260429_034804.log new file mode 100644 index 0000000000000000000000000000000000000000..429c2acfe53e5626962af89f3bad3a030a5b8896 --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/gen_20260429_034804.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d24c7ed48f7e338d0fd32224bbab0ef22d1fcf4bbed15f58106e572839d8955e +size 5240 diff --git a/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/input_snapshot.json b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..20d4b266b7691979822c6190a9818f61e8ac3aa1 --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + 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"nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + 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a/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/X_num_val.npy b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/X_num_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..05368376414bc8e2221b9744c2e3746a587e749c --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/X_num_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba6482dc6705ea0ac5fc0355391800cb0937fe4c3221baa21bc1f47beec8c260 +size 128 diff --git a/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/info.json b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0a27214b83536ad36b42f7207db6cfb548d0f7d8 --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/info.json @@ -0,0 +1,104 @@ +{ + "name": "pipeline_ds", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 8, + "train_size": 10368, + "test_num": 10368, + "val_num": 10368, + "train_num": 10368, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7 + ], + "target_col_idx": [ + 8 + ], + "column_names": [ + "parents", + "has_nurs", + "form", + "children", + "housing", + "finance", + "social", + "health", + "class" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "categorical" + }, + "1": { + "sdtype": "categorical" + }, + "2": { + "sdtype": "categorical" + }, + "3": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "categorical" + }, + "7": { + "sdtype": "categorical" + }, + "8": { + "sdtype": "categorical" + } + } + }, + "idx_mapping": { + 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a/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/test.csv b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..21879e2e98f6a99a9fb514ff1b5dc907c5eeef6f --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:691798157151e2810978f9c3113050cbde9ce547607309e6994953e3a77b5b60 +size 269749 diff --git a/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/val.csv b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..21879e2e98f6a99a9fb514ff1b5dc907c5eeef6f --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/tabular_bundle/pipeline_ds/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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0000000000000000000000000000000000000000..2d7000b7f1ea5b8ebb6c32e92efb8ff18c986e2a --- /dev/null +++ b/timecost/c7/tabdiff/tabdiff-c7-20260429_033922/train_20260429_033923.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:250df662b3b4e6a64ea5b736cdc88b9aa5030a75bcd495d84a90e03e7e15ff1e +size 441018 diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/_tabpfgen_generate.py b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..08b47351c3e118ba3a59239e6f1ed473844988aa --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/train.csv") +target_col = "class" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(10368) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen-c7-10368-20260429_061314.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen-c7-10368-20260429_061314.csv") diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/gen_20260429_061314.log b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/gen_20260429_061314.log new file mode 100644 index 0000000000000000000000000000000000000000..f40e5b0db009ef96d449271680c1c264e5a07830 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/gen_20260429_061314.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:50514dbd9cd064ed6754ea4b46fd82f7df79bcc7f0ff228dab81cdd9aeb3c8a6 +size 1267 diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/input_snapshot.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5d364a8d2a623f4bc59dd9c80500eaf78ec0cddf --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv", + "exists": true, + "size": 107327, + "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json", + "exists": true, + "size": 4014, + "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json", + "exists": true, + "size": 4759, + "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd" + } + } +} \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/normalized_schema_snapshot.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/public_gate_report.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/staged_input_manifest.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cefd5d636f9fd36e82b1096ea892c13c32702704 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/runtime_result.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..a3af4707a0d395f9eb1f98433ac129a15fb69e41 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c7", + "model": "tabpfgen", + "run_id": "tabpfgen-c7-20260429_061314", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen-c7-10368-20260429_061314.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314" + } +} \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/staged_features.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/test.csv b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/train.csv b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/val.csv b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/adapter_report.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..f301d945f1883d729f0f1f61101ccfb021d0811d --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/adapter_transforms_applied.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/model_input_manifest.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..964945e70f46d24807e770166cc8c47d6665fbd8 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "tabpfgen", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": 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+ ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen-c7-10368-20260429_061314.csv b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen-c7-10368-20260429_061314.csv new file mode 100644 index 0000000000000000000000000000000000000000..b9e3a22f1185e49aaa016ef6b5418dd62b6d2df5 --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen-c7-10368-20260429_061314.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11bb6aee31a1bcbe969d798a8a69906cd73c17223291104079b93797d7cf94d0 +size 843881 diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen_meta.json b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..a75798b883cc8707f32859a385a3edee3324970a --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tabpfgen/tabpfgen-c7-20260429_061314/staged/public/staged_features.json", + "target_col": "class", + "is_classification": true, + "task_type": "classification", + "n_rows": 10368, + "n_cols": 9 +} \ No newline at end of file diff --git a/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/train_20260429_061314.log b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/train_20260429_061314.log new file mode 100644 index 0000000000000000000000000000000000000000..6b247d09b96afd4c14d366cc4e7eaf666815f21e --- /dev/null +++ b/timecost/c7/tabpfgen/tabpfgen-c7-20260429_061314/train_20260429_061314.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73ec563d826ab6396f832e3e1487289794c7c2a744680d747abf25ceb3611619 +size 595 diff --git a/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/_tabsyn_sample.py b/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..ac414667eb089c0d6abb603dde4e3c855b25bc66 --- /dev/null +++ b/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c7/tabsyn/tabsyn-c7-20260429_052312" +dataname = "tabsyn_c7" +output_csv = "/work/output-Benchmark-trainonly-v1/c7/tabsyn/tabsyn-c7-20260429_052312/tabsyn-c7-10368-20260429_061302.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 10368 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/_tabsyn_train.py b/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a9f348266ba8291c4e3298592ef99cf71b0fe44a --- /dev/null +++ b/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c7/tabsyn/tabsyn-c7-20260429_052312" +dataname = "tabsyn_c7" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/data/tabsyn_c7/X_cat_test.npy b/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/data/tabsyn_c7/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..15d20f20875bae252cf0a056e5ee5571e0584d8b --- /dev/null +++ b/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/data/tabsyn_c7/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 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0000000000000000000000000000000000000000..401a57f7c0a1fea7d1a3d47f888c863ea754b11f --- /dev/null +++ b/timecost/c7/tabsyn/tabsyn-c7-20260429_052312/train_20260429_052312.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68c2938cba689f704152160f6c88cb60bb429cdc47dea88b574d9ba45e91c756 +size 2890992 diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/_tvae_generate.py b/timecost/c7/tvae/tvae-c7-20260429_031938/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..97b054d359c203f5bce5035c35154d549095b67b --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/models_300epochs/tvae_300epochs.pt") +total = 10368 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/tvae-c7-10368-20260429_032040.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/tvae-c7-10368-20260429_032040.csv") diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/_tvae_train.py b/timecost/c7/tvae/tvae-c7-20260429_031938/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..dda2fe3921491373c214bd0c9bf435073726f059 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/gen_20260429_032040.log b/timecost/c7/tvae/tvae-c7-20260429_031938/gen_20260429_032040.log new file mode 100644 index 0000000000000000000000000000000000000000..e277329c4693c928dc023bf08d72a6214a0ae213 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/gen_20260429_032040.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c45cf869318530600b5a089b84c6097d5e4d771329802a000b5d533b807c905 +size 406 diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/input_snapshot.json b/timecost/c7/tvae/tvae-c7-20260429_031938/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..efb8007253c004156220915598545e76948d4591 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv", + "exists": true, + "size": 107327, + "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json", + "exists": true, + "size": 4014, + "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json", + "exists": true, + "size": 4759, + "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd" + } + } +} \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/models_300epochs/train_20260429_031938.log b/timecost/c7/tvae/tvae-c7-20260429_031938/models_300epochs/train_20260429_031938.log new file mode 100644 index 0000000000000000000000000000000000000000..9b08c40324004a6f8d14dc8dfb5aab190b8a3dfb --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/models_300epochs/train_20260429_031938.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94baa6cd196f33c6c0081588c7f11bf5cef9900053a9d40ed0160ba78905dacd +size 532 diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/models_300epochs/tvae_300epochs.pt b/timecost/c7/tvae/tvae-c7-20260429_031938/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..5d475fabe77ac188f1a244be981232f456f9092c --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a36dbd63973bfdd6efec40fdb099a0fc4626fd524c7234762020278fb774d10b +size 536620 diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/normalized_schema_snapshot.json b/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + 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"missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/public_gate_report.json b/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "class", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/staged_input_manifest.json b/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7ab2b329f608b6a24df5af80e502eb2eb473ca84 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + 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"profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/runtime_result.json b/timecost/c7/tvae/tvae-c7-20260429_031938/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..f666455a68f6a098cbd3a1732a5834986c6819a7 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/runtime_result.json @@ -0,0 +1,15 @@ +{ + "dataset_id": "c7", + "model": "tvae", + "run_id": "tvae-c7-20260429_031938", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/tvae-c7-10368-20260429_032040.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/models_300epochs/tvae_300epochs.pt" + } +} \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/staged_features.json b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/test.csv b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/train.csv b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a +size 847349 diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/val.csv b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..55792963756b1ae35b4d79dee11881372962b8d5 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780 +size 106192 diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/adapter_report.json b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..591f29e2fbfcd23d39ee5083b8559fcd7db9d897 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/adapter_transforms_applied.json b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/model_input_manifest.json b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bb3374680825526492c584ebf05b449b6282472b --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/staged/tvae/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "tvae", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/tvae/tvae-c7-20260429_031938/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/tvae-c7-10368-20260429_032040.csv b/timecost/c7/tvae/tvae-c7-20260429_031938/tvae-c7-10368-20260429_032040.csv new file mode 100644 index 0000000000000000000000000000000000000000..e20ad8c7b629289649d146e05fca745faeecf1f2 --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/tvae-c7-10368-20260429_032040.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fea94190d7dac2c3e390ca3e1dab13ade59799d1372ec4bd405be7a1cf733b94 +size 855825 diff --git a/timecost/c7/tvae/tvae-c7-20260429_031938/tvae_metadata.json b/timecost/c7/tvae/tvae-c7-20260429_031938/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..83f41ad233860beaed71ee8e4637e99ce522d64a --- /dev/null +++ b/timecost/c7/tvae/tvae-c7-20260429_031938/tvae_metadata.json @@ -0,0 +1,40 @@ +{ + "columns": [ + { + "name": "parents", + "type": "categorical" + }, + { + "name": "has_nurs", + "type": "categorical" + }, + { + "name": "form", + "type": "categorical" + }, + { + "name": "children", + "type": "categorical" + }, + { + "name": "housing", + "type": "categorical" + }, + { + "name": "finance", + "type": "categorical" + }, + { + "name": "social", + "type": "categorical" + }, + { + "name": "health", + "type": "categorical" + }, + { + "name": "class", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/arf/arf-m4-20260501_224942/_arf_generate.py b/timecost/m4/arf/arf-m4-20260501_224942/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..6ede30bf008eb9e0f05aa6309cdc69dc89bd1e98 --- /dev/null +++ b/timecost/m4/arf/arf-m4-20260501_224942/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(2217) +c_csv = "/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260501_224942/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260501_224942/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm4' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260501_224942/arf-m4-2217-20260501_224949.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260501_224942/arf-m4-2217-20260501_224949.csv") diff --git a/timecost/m4/arf/arf-m4-20260501_224942/_arf_train.py b/timecost/m4/arf/arf-m4-20260501_224942/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..663e9e0a52e5edb72ecb5fec34e517b043418b01 --- /dev/null +++ b/timecost/m4/arf/arf-m4-20260501_224942/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260501_224942/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260501_224942/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m4/arf/arf-m4-20260501_224942/arf_model.pkl") diff --git a/timecost/m4/arf/arf-m4-20260501_224942/arf-m4-2217-20260501_224949.csv b/timecost/m4/arf/arf-m4-20260501_224942/arf-m4-2217-20260501_224949.csv new file mode 100644 index 0000000000000000000000000000000000000000..2d8ae2c97966c31f15fa067f708a6d303e098b5e --- /dev/null +++ b/timecost/m4/arf/arf-m4-20260501_224942/arf-m4-2217-20260501_224949.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7020d7227c97a8e05595b969c7357dfa24a4313baf6ddd2bf49c12c0d11d1fe +size 191309 diff --git a/timecost/m4/arf/arf-m4-20260501_224942/arf_model.pkl b/timecost/m4/arf/arf-m4-20260501_224942/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..1f1cca8fec1e8037f26845f1db4242ff7b1b2970 --- /dev/null +++ b/timecost/m4/arf/arf-m4-20260501_224942/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9353291c6ae2f9063d32dab25c4a37822a2cb4584e8fde22d25106dfefb4c58 +size 6642917 diff --git 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index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/arf/arf-m4-20260501_224942/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/arf/arf-m4-20260501_224942/staged/public/val.csv b/timecost/m4/arf/arf-m4-20260501_224942/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/arf/arf-m4-20260501_224942/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/arf/arf-m4-20260501_224942/train_20260501_224942.log b/timecost/m4/arf/arf-m4-20260501_224942/train_20260501_224942.log new file mode 100644 index 0000000000000000000000000000000000000000..dff1204ec9e4cacef03b27edc89a49297aa102cf --- /dev/null +++ b/timecost/m4/arf/arf-m4-20260501_224942/train_20260501_224942.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f8307dfa571ae3a8b84004182dccf39d1539db0bac436880907dcc5af5d746f +size 495 diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/_bayesnet_generate.py b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..bd55afcca3270a8189bb3d88017c201330279e32 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(2217) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet-m4-2217-20260501_225008.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet-m4-2217-20260501_225008.csv") diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/_bayesnet_train.py b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..da81eca4a76d1f309d1238362e22e77a75036965 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_model.pkl") diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet-m4-2217-20260501_225008.csv b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet-m4-2217-20260501_225008.csv new file mode 100644 index 0000000000000000000000000000000000000000..a205f33b33e9634efeb5bbe41ff9ba95999cdfe0 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet-m4-2217-20260501_225008.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4dc959faeb4ed98c85dfa08b08771d30677013b1d259393fd37593c71b2a2330 +size 207284 diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_coltypes.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..295bb70445b63eea5719ae7c1f72bcdf61849c8d --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_coltypes.json @@ -0,0 +1,33 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_model.pkl b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..5cec3423f81fcddaac8143cd28b8892056252bfc --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b0159fa6db32a24c098240969438a6189b99a41564cffd3766b12aee069f9135 +size 7202 diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/const_cols.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/gen_20260501_225008.log b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/gen_20260501_225008.log new file mode 100644 index 0000000000000000000000000000000000000000..ce0ec254ec00a88f70e5a99f2cacb77a8ba2c59a --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/gen_20260501_225008.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d51af08b778e3d79298c3765603ef72648c0659f1a0516ba3d3b39fe5a66c51a +size 3661 diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/input_snapshot.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..80a11fda595a83a7857ca988e84c480002e80ddc --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/normalized_schema_snapshot.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/public_gate_report.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/staged_input_manifest.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b76841ca1ebb0185e476c2ed8c9b82d633091cad --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": 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"impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/runtime_result.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..ac44c1ab24318dcbf29281e36b2dfa3b5cac2d65 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "run_id": "bayesnet-m4-20260501_224959", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet-m4-2217-20260501_225008.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-01T22:49:59", + "ended_at": "2026-05-01T22:50:08", + "duration_sec": 8.203 + }, + "generate": { + "started_at": "2026-05-01T22:50:08", + "ended_at": "2026-05-01T22:50:13", + "duration_sec": 5.386 + } + } +} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/adapter_report.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2c2d535c274055b550ca68ab2aeb34f24ef09bc2 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/adapter_transforms_applied.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/model_input_manifest.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7cc6312e585fd5688202840b93e3a99c875fa2d5 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "bayesnet", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + 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"unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/bayesnet/bayesnet-m4-20260501_224959/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/staged_features.json b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/test.csv b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/train.csv b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/val.csv b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/train_20260501_224959.log b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/train_20260501_224959.log new file mode 100644 index 0000000000000000000000000000000000000000..c5a7791086f44bb7fd3faf021a00160501504c95 --- /dev/null +++ b/timecost/m4/bayesnet/bayesnet-m4-20260501_224959/train_20260501_224959.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9028b3cbf752de48d3e72989c4933fffd2ddbd9d8d9f1aac6de1d571e14847d3 +size 3738 diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/_ctgan_generate.py b/timecost/m4/ctgan/ctgan-m4-20260501_005319/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..67d0dff7e211262c70ea184863e1ec0ff317f87e --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260501_005319/models_300epochs/ctgan_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260501_005319/ctgan-m4-2217-20260501_005357.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260501_005319/ctgan-m4-2217-20260501_005357.csv") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/ctgan-m4-2217-20260501_005357.csv b/timecost/m4/ctgan/ctgan-m4-20260501_005319/ctgan-m4-2217-20260501_005357.csv new file mode 100644 index 0000000000000000000000000000000000000000..4b7802a17da7367a716b5c90ffd32ccdfd089bf4 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/ctgan-m4-2217-20260501_005357.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5c8c7341ffb00ab61712df4a680a498018c4b055e21115e17af39f9f573de3e8 +size 135690 diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/ctgan_metadata.json b/timecost/m4/ctgan/ctgan-m4-20260501_005319/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/gen_20260501_005357.log b/timecost/m4/ctgan/ctgan-m4-20260501_005319/gen_20260501_005357.log new file mode 100644 index 0000000000000000000000000000000000000000..611c6f429da6ca41495cb66d06d380630e1655f9 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/gen_20260501_005357.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ec8a0834349502d3ac5a92adc918f52c147d215b61a063e2a73d81a537df15f +size 560 diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/input_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260501_005319/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/models_300epochs/ctgan_300epochs.pt b/timecost/m4/ctgan/ctgan-m4-20260501_005319/models_300epochs/ctgan_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..9edafc7993600acc052f7948120ce587abf78625 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/models_300epochs/ctgan_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1a3a7bf168cae7aea5ffe1f2665ab950e673295c028d44f1a8a5dbf0bf894a08 +size 938531 diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/models_300epochs/train_20260501_005319.log b/timecost/m4/ctgan/ctgan-m4-20260501_005319/models_300epochs/train_20260501_005319.log new file mode 100644 index 0000000000000000000000000000000000000000..cb708f2cf9da62e736fd2b4e3f3c0b6eb23cccd8 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/models_300epochs/train_20260501_005319.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66f22b081707c0d5ff4c2ad6ae85dadb1e184f8787f65058d06222084fa5c73f +size 1836 diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/public_gate/normalized_schema_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260501_005319/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + 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} + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260501_005319/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260501_005319/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260501_005319/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260501_005319/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260501_005319/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/staged_features.json b/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/test.csv b/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/train.csv b/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/val.csv b/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260501_005319/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_generate.py b/timecost/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..563f6bea6aa8d6a1a16da4850ba8a90c0cfc96a1 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_train.py b/timecost/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..90d9736982b710db248ddd882246ba9562d061c8 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=8, + generator_dim=(16, 16), + discriminator_dim=(16, 16), + batch_size=32, + pac=1, + epochs=50, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv new file mode 100644 index 0000000000000000000000000000000000000000..5c047df18e289162d7342334349e940f451dc505 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9d5b1fd2a8f637e700f3164b1aa7833a9eeba0084237d900006fbf35829a1009 +size 135963 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/ctgan_metadata.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/gen_20260504_162609.log b/timecost/m4/ctgan/ctgan-m4-20260504_162455/gen_20260504_162609.log new file mode 100644 index 0000000000000000000000000000000000000000..4dec0f96e5a80b73bcae9d62fe3758231d7ad60d --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/gen_20260504_162609.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:631cad7bb802b05edec1212c5654bc3cf2b87f637cb37bb0269c684a86a0a78b +size 297 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/input_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt b/timecost/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..74d60aada6757cb50756979c746af4977a01a3d5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0fcbc1eca52b300da8de0000993e73b4d58d44408c58f5e94824b040a9312f97 +size 280461 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/train_20260504_162455.log b/timecost/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/train_20260504_162455.log new file mode 100644 index 0000000000000000000000000000000000000000..b862c69bf2d88bf63062508f45c2c94c1b1f0a63 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/train_20260504_162455.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:421ee609e428857108a8ef1840b7d88d9b1a05532c27ba8b20235b9b582126f8 +size 10928 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/normalized_schema_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..de8fec055242a76a46100b112369488b7a1a3b54 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + 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"nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/run_config.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..65b52b73614d057167e888ab36f2fa74e106b161 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:24:55", + "dataset_id": "m4", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "32", + "CTGAN_DEFAULT_EPOCHS": "50", + "CTGAN_DISCRIMINATOR_DIMS": "16,16", + "CTGAN_EMBEDDING_DIM": "8", + "CTGAN_GENERATOR_DIMS": "16,16", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/runtime_result.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..68cb13745d1370732ba3f4f2b3936fde950009e4 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "run_id": "ctgan-m4-20260504_162455", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/ctgan-m4-2217-20260504_162609.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/models_50epochs/ctgan_50epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:24:55", + "ended_at": "2026-05-04T16:26:09", + "duration_sec": 74.098 + }, + "generate": { + "started_at": "2026-05-04T16:26:09", + "ended_at": "2026-05-04T16:26:15", + "duration_sec": 5.73 + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..b9d0293168cdd6f75a7982799521ddd525f21883 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_transforms_applied.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..113d24a0e03711e2cee69ee4ebfa63e6f8632a90 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/ctgan/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": 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"nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162455/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162455/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_generate.py b/timecost/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..cdef9a9fbb89b21fb93106f5cba82484dc209f91 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_train.py b/timecost/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..641f6347bdb1791cfb4d69ab521cbb669f678005 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=64, + pac=1, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv new file mode 100644 index 0000000000000000000000000000000000000000..324aa30fc8c04f9c70c6622270c46f790870065b --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4ad246572c9acdde64b136e255d90992eee5bac1408041de15f542602d5f7c5 +size 135924 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/ctgan_metadata.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/gen_20260504_162744.log b/timecost/m4/ctgan/ctgan-m4-20260504_162628/gen_20260504_162744.log new file mode 100644 index 0000000000000000000000000000000000000000..e98d9fa7af6300f34a8e3494acba4027d4a29136 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/gen_20260504_162744.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4078474fb8d9cc0dc9820a2d898eb02a6930796acfda171c2dca2cae431ca45 +size 297 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/input_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt b/timecost/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c5854a45badcccb4d8052678fe5b87d7fd145019 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29799bc301c47b2edeb651132d03a7091181e08c93c9f9fa3187b2f78aba517c +size 296675 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/train_20260504_162628.log b/timecost/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/train_20260504_162628.log new file mode 100644 index 0000000000000000000000000000000000000000..f77332fe3e25a652b5ed704200376dd7167d6802 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/train_20260504_162628.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8b60ecb9e48817e50d61f794e78bfddf29823d27d102e8ead5ed0b3069ad070 +size 20368 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/normalized_schema_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/public_gate_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/staged_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..951a91deb548648001acebfdf0cf14ab1aac1da0 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv", + "val_csv": 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+ "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/run_config.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..99605f6c543fb6ce9f66a0a604a21f03d390d63f --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:26:28", + "dataset_id": "m4", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "64", + "CTGAN_DEFAULT_EPOCHS": "100", + "CTGAN_DISCRIMINATOR_DIMS": "32,32", + "CTGAN_EMBEDDING_DIM": "16", + "CTGAN_GENERATOR_DIMS": "32,32", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/runtime_result.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..b2c7143635c4f190a941973d83a91fa0e27ac498 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "run_id": "ctgan-m4-20260504_162628", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/ctgan-m4-2217-20260504_162744.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/models_100epochs/ctgan_100epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:26:28", + "ended_at": "2026-05-04T16:27:44", + "duration_sec": 76.234 + }, + "generate": { + "started_at": "2026-05-04T16:27:44", + "ended_at": "2026-05-04T16:27:50", + "duration_sec": 5.742 + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/adapter_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..9d5f3fbb9ed2a280ba0125577a7ef0295565b112 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/adapter_transforms_applied.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/model_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..643730be588f337790c3330908ab3b54be6a6105 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/ctgan/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162628/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/test.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/val.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162628/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_generate.py b/timecost/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..56154cfced3a7c24b394769111b46be23741c05f --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_train.py b/timecost/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b461b498a3b3fe0aa2e514b9cbe2736069c01a87 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=128, + pac=1, + epochs=150, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv new file mode 100644 index 0000000000000000000000000000000000000000..50880bc8444a2e81313982a10d35af8dd5ecc7c4 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:384120f54add6d314fafdc5938da22645316ce9201f4847812c063015d105e86 +size 135501 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/ctgan_metadata.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/gen_20260504_162901.log b/timecost/m4/ctgan/ctgan-m4-20260504_162802/gen_20260504_162901.log new file mode 100644 index 0000000000000000000000000000000000000000..2c8740823b955b36adbc1e5f820b0819bea3b9bf --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/gen_20260504_162901.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53c84f13415a31957dfe62320cf0a57fc913c8b4622039b492c928ea4ee09517 +size 297 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/input_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt b/timecost/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..217f090ca1b40d37dd79db4d92718f4b8ade52d1 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5f0ddb51de8e66d1bb73ba247bb8d3781b7eb35a4fbd45c3e76624bbf2adb18 +size 339811 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/train_20260504_162802.log b/timecost/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/train_20260504_162802.log new file mode 100644 index 0000000000000000000000000000000000000000..c61563e139d4cee1fd2b83fbc0d7e3c8fdf9d1a4 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/train_20260504_162802.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:621ab279edd3d52657cce8c48d67b29b97b292b746ad59317fcc87329dbf9f98 +size 29629 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/normalized_schema_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a7acbe7288ea212067cd072ed988c4511e9ae1c5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/run_config.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..7199dfd8d3dc5af78632834ddd98a1a2b0af2579 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:28:02", + "dataset_id": "m4", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "128", + "CTGAN_DEFAULT_EPOCHS": "150", + "CTGAN_DISCRIMINATOR_DIMS": "64,64", + "CTGAN_EMBEDDING_DIM": "32", + "CTGAN_GENERATOR_DIMS": "64,64", + "CTGAN_PAC": "1" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/runtime_result.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..1d742290655adc88c195d0a4d1430817f337102b --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "run_id": "ctgan-m4-20260504_162802", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/ctgan-m4-2217-20260504_162901.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/models_150epochs/ctgan_150epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:28:02", + "ended_at": "2026-05-04T16:29:01", + "duration_sec": 58.433 + }, + "generate": { + "started_at": "2026-05-04T16:29:01", + "ended_at": "2026-05-04T16:29:07", + "duration_sec": 6.047 + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..c3029518606f6993d8f6e8c961a0afd76cf5989b --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_transforms_applied.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b322f966a05e8955544dcb79ac90ae5da186b160 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/ctgan/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162802/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162802/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_generate.py b/timecost/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..1c6869a3fe44882e4389b3896ee6dfd20585ca25 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_train.py b/timecost/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1b7c20d0b22c8b49a0901ad3141ca60eb2badbdf --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=80, + pac=5, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv new file mode 100644 index 0000000000000000000000000000000000000000..3e72d45b8b8b9317621e27073d49108074f9aef9 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c406571e96f9c06734e3b7b6d3ba3c06c0069415306215bf688fab81de6a3f9 +size 135713 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/ctgan_metadata.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/gen_20260504_163019.log b/timecost/m4/ctgan/ctgan-m4-20260504_162920/gen_20260504_163019.log new file mode 100644 index 0000000000000000000000000000000000000000..fbf8afe72b30dd7d2519ba324c9633ffc6d3265b --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/gen_20260504_163019.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1fff1fd92ce19744bd06b418d3b6c357dfa8b03a6e2dadb1c31c75f57762eb18 +size 297 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/input_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt b/timecost/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c0f2fdc77c4f3e0788cdd693f2f2bd2ed97a3cb4 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1735982b8a1196accf5fa3d751028415305c00286b49342dd845ccdf4171edb +size 296675 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/train_20260504_162920.log b/timecost/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/train_20260504_162920.log new file mode 100644 index 0000000000000000000000000000000000000000..6e9831c5270bf75c1e2c4eb4085454182a52e85c --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/models_100epochs/train_20260504_162920.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f12ac50c989ef88b4b247ec0de8a38cb7d48b186cd47b1ae55ce0e904fe030c +size 20346 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/normalized_schema_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/public_gate_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/staged_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..d8f24c7fe9e92f7fd58473243d225e823c6ba09e --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json", + "public_gate_report": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "80", + "CTGAN_DEFAULT_EPOCHS": "100", + "CTGAN_DISCRIMINATOR_DIMS": "32,32", + "CTGAN_EMBEDDING_DIM": "16", + "CTGAN_GENERATOR_DIMS": "32,32", + "CTGAN_PAC": "5" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/runtime_result.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..f115cc028f0b74ab7cbc3aa216d7c3eb92534435 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "run_id": "ctgan-m4-20260504_162920", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/ctgan-m4-2217-20260504_163019.csv", + "model_path": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/ctgan/adapter_transforms_applied.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/ctgan/model_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6605d154467983325f599e76a16d6ae710802bf1 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/ctgan/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": 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"missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_162920/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/test.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/val.csv b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_162920/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_generate.py b/timecost/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9bd1b474a2c4e3a5589188b211b0e6e39efb81f0 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_train.py b/timecost/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a841856a406bbb3e09660cb7d1a6488fd8eb5858 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv") +discrete_columns = ['sex', 'smoker', 'region'] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=100, + pac=10, + epochs=200, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt") \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv b/timecost/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv new file mode 100644 index 0000000000000000000000000000000000000000..02a4410b10356bd5136a2b9cbee8b59d63cb1cea --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7baf2650acdb58a6960bf3482afc14f2a59738fbe3de156152a55cc7636ab39e +size 136085 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/ctgan_metadata.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/ctgan_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/gen_20260504_163207.log b/timecost/m4/ctgan/ctgan-m4-20260504_163038/gen_20260504_163207.log new file mode 100644 index 0000000000000000000000000000000000000000..1794c145cc281f95b00fe5f46b3d2dc1ff9bc4aa --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/gen_20260504_163207.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:843b04a29b1a7c84294fbdad7fafc0ad169db22ca2b2b9ddbd5813e245293903 +size 297 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/input_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..f11278f1ffa8959e7869f70def91d0284f7dd656 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt b/timecost/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..60c1b8863a50d55ea3f90ff4f64bcf1822d472ff --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f7587270ae8535607fd4804d5bf9cf09d651bba7376264c2365fce4e6ce00c6 +size 341411 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/train_20260504_163038.log b/timecost/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/train_20260504_163038.log new file mode 100644 index 0000000000000000000000000000000000000000..de0ac05ad9d6cff23800812ff6879cb55324c8bb --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/train_20260504_163038.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:393ba9d05340b1971bf7f5b702d02a8fee4ed5303a31bd46c1803bf4d6c4ddb6 +size 39009 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/normalized_schema_snapshot.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b816cabf716c275363c4a5a402d22bf38a2108aa --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + 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"impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/run_config.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f5b9d4f9b1fb6f4e85065405913c9ceaf536244f --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:30:38", + "dataset_id": "m4", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "100", + "CTGAN_DEFAULT_EPOCHS": "200", + "CTGAN_DISCRIMINATOR_DIMS": "64,64", + "CTGAN_EMBEDDING_DIM": "32", + "CTGAN_GENERATOR_DIMS": "64,64", + "CTGAN_PAC": "10" + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/runtime_result.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7db01fc69ae2c574a3aebf602eb5ef1cb959b9e5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "run_id": "ctgan-m4-20260504_163038", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/ctgan-m4-2217-20260504_163207.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/models_200epochs/ctgan_200epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:30:38", + "ended_at": "2026-05-04T16:32:07", + "duration_sec": 88.927 + }, + "generate": { + "started_at": "2026-05-04T16:32:07", + "ended_at": "2026-05-04T16:32:13", + "duration_sec": 5.981 + } + } +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_report.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..c66b2d5d18b0bd9500d67f443aedbdf219804a82 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_transforms_applied.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..033c4ca1a435c6ec97f441f3886476742ec4be6d --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/ctgan/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "ctgan", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/ctgan/ctgan-m4-20260504_163038/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/ctgan/ctgan-m4-20260504_163038/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_X_host.npy b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..adf7790ed3b5f3fbf003c878147469425b445ed1 --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2075e38ffa201390419d8ba927a30948410d3bae79c47b2d376e0991c42ba455 +size 62204 diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_gen.py b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..99c06c9f2e40f637316b397ae4efb41f75d47b73 --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260501_180515/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(2217)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260501_180515/forest-m4-2217-20260501_180613.csv', index=False) +print("saved", len(df)) diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_meta_host.json b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..ee3e9bf77c9008274d3ca5b068e49b142b0bb624 --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["age", "sex", "bmi", "children", "smoker", "region", "charges"], "cat_indexes": [1, 4, 5]} \ No newline at end of file diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_train.py b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..be7c41b9f5840133c854a4fd69dae07d746b6a9d --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260501_180515/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260501_180515/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m4/forestdiffusion/forest-m4-20260501_180515/forestdiffusion_model.joblib') diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/forest-m4-2217-20260501_180613.csv b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/forest-m4-2217-20260501_180613.csv new file mode 100644 index 0000000000000000000000000000000000000000..d30aed8d50de2c4dbbe266ebe0078a3838f8bc0b --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/forest-m4-2217-20260501_180613.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e764181c7250d24f9cfc643be0c2f147bfa49b503a7447ef06154f46469965d +size 184302 diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/forestdiffusion_model.joblib b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..cf5dbfcc3f0cbd8622d6359a378cf96f45d9b3fb --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5441860108c9d0dc83f869bb3de0909d36382ceada4791c9e919556f81e9e5d +size 83389261 diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/gen_20260501_180613.log b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/gen_20260501_180613.log new file mode 100644 index 0000000000000000000000000000000000000000..c35ca2f556f604f7dc47fbd89beedf075e8f5e64 --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/gen_20260501_180613.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1165091351583dd9b06ceed67097a7d6651aae2370bc221259adf13f6bcfede0 +size 294 diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/input_snapshot.json b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5daa6d8c1d862349e305d0a5fcbd26d5361342b8 --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/models_fd/model.joblib b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..cf5dbfcc3f0cbd8622d6359a378cf96f45d9b3fb --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5441860108c9d0dc83f869bb3de0909d36382ceada4791c9e919556f81e9e5d +size 83389261 diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/public_gate/normalized_schema_snapshot.json b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/forestdiffusion/forest-m4-20260501_180515/public_gate/public_gate_report.json b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/forestdiffusion/forest-m4-20260501_180515/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + 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sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/adapter_report.json b/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..b445ebdebf2aa753c21b4897686760b1fb1c7da7 --- /dev/null +++ b/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/adapter_transforms_applied.json b/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/model_input_manifest.json b/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..e4b1f36314c1b9333e8fc23a286224033a9b8bc5 --- /dev/null +++ b/timecost/m4/realtabformer/rtf-m4-20260501_033611/staged/realtabformer/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "realtabformer", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": 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+ }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/realtabformer/rtf-m4-20260501_033611/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/realtabformer/rtf-m4-20260501_033611/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/realtabformer/rtf-m4-20260501_033611/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/realtabformer/rtf-m4-20260501_033611/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/realtabformer/rtf-m4-20260501_033611/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/realtabformer/rtf-m4-20260501_033611/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/realtabformer/rtf-m4-20260501_033611/train_20260501_033611.log b/timecost/m4/realtabformer/rtf-m4-20260501_033611/train_20260501_033611.log new file mode 100644 index 0000000000000000000000000000000000000000..de41aa38cc79bb8c5f8b88cd6fb369efea9c40f1 --- /dev/null +++ b/timecost/m4/realtabformer/rtf-m4-20260501_033611/train_20260501_033611.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15e03d697bcd765661b1dca3cee077bb5ebf139d16ceacb85823348d091c5b34 +size 276386 diff --git a/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_gen.py b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..d98a7b934607c36eb22a776e92df322a392ddfa1 --- /dev/null +++ b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m4/adapter_efvfm/model_300.pt", + "--num_samples_to_generate", str(int(2217)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabbyflow-m4-2217-20260505_004055.csv") diff --git a/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6218a0f02f6a9586b6b53dd56df0b58092541475 --- /dev/null +++ b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/_tabbyflow_train.py @@ -0,0 +1,24 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "300" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +os.environ.setdefault("EFVFM_EVAL_NUM_SAMPLES", "512") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/gen_20260505_004055.log b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/gen_20260505_004055.log new file mode 100644 index 0000000000000000000000000000000000000000..5acce39e217334420d18441f8bb2fd1d2cfb4ffd --- /dev/null +++ b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/gen_20260505_004055.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:28b24fc237445e5f99c53a4fd52b4c1d366b66cc7574aae7383cad03c2035040 +size 3570 diff --git a/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/input_snapshot.json b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a8e6cea3720f9c9515d0f6647d3de14221c42d45 --- /dev/null +++ b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/models_tabbyflow/trained.pt b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/models_tabbyflow/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..f063a878c470d9d40f79db79881596e1e497dffd --- /dev/null +++ b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/models_tabbyflow/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adb734fec12f2251befd371dca69e481b19464aded2a6823105a01b4be6ddbe5 +size 40 diff --git a/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/normalized_schema_snapshot.json b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/public_gate_report.json b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": 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sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git a/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4/info.json b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4/info.json new file mode 100644 index 0000000000000000000000000000000000000000..1ccad8d2dc3c60feacc99d202a75e21be3afa7b5 --- /dev/null +++ b/timecost/m4/tabbyflow/tabbyflow-m4-20260505_003507/tabular_bundle/pipeline_m4/info.json @@ -0,0 +1,92 @@ +{ + "name": "pipeline_m4", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "test_num": 2217, + "val_num": 2217, + "train_num": 2217, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "bmi", + "children", + "sex", + "smoker", + "region", + "charges" + 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b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..2e6048bab95ba3f3803c0a6104ca81669f7c384b --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 2217 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/config_sample_20260501_002103_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/tabddpm-m4-2217-20260501_002103.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/tabddpm-m4-2217-20260501_002103.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/_tabddpm_train.py b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..417fdb96cffb192101af75c03ef22fb0fe6f9c37 --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..e40773ed5a92be547e5b272aac54a1edd49a31dd --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/config_sample_20260501_002103_r0.toml b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/config_sample_20260501_002103_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..868f104506973dd113227fa7fe94789f4b3108af --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/config_sample_20260501_002103_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 6 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 2217 +batch_size = 1000 +seed = 0 diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/X_cat_train.npy b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2f6b8b23f5768174aab4879e2a30371e9a3d0ead --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c91af7ac26382e392a70536d3dbb592c04ba5e4f495d1e3c364416b9ef704a07 +size 53336 diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/X_num_train.npy b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/info.json b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..fdcf5357ff78dd47dfcca0748443a009323ebfba --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/info.json @@ -0,0 +1,29 @@ +{ + "name": "benchmark_dataset", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "bmi", + "children", + "sex", + "smoker", + "region", + "charges" + ] +} \ No newline at end of file diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/data/y_train.npy 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b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/staged/tabddpm/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/staged/tabddpm/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/staged/tabddpm/adapter_transforms_applied.json b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/staged/tabddpm/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/staged/tabddpm/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/staged/tabddpm/model_input_manifest.json b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2c83eda5e92459bea83ec899cc45e05933567c78 --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabddpm", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabddpm/tabddpm-m4-20260501_002012/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/tabddpm-m4-2217-20260501_002103.csv b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/tabddpm-m4-2217-20260501_002103.csv new file mode 100644 index 0000000000000000000000000000000000000000..5ab28a780597b227ff2e3bebbfd38a2fa7882f5d --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/tabddpm-m4-2217-20260501_002103.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75d65561f3b4531077e2145626ef2f81dd90f4be95c6bb9d62f0c97ea4bb7143 +size 102421 diff --git a/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/train_20260501_002012.log b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/train_20260501_002012.log new file mode 100644 index 0000000000000000000000000000000000000000..7a8e589b8b62ec90f2244e67df8cbfdcce45815c --- /dev/null +++ b/timecost/m4/tabddpm/tabddpm-m4-20260501_002012/train_20260501_002012.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:65856d3d7601f57664a0b451b5d8b311571553e2b88be056194e543c47267934 +size 9176 diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_gen.py b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..8558e989d232d1b665a25c80607d47a0ca9a9191 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m4/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(2217)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260501_004659/tabdiff-m4-2217-20260501_005309.csv") diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_train.py b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3b26184cee7eed4375238f2069bf1ee6309387 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m4" +src = r"/work/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/gen_20260501_005309.log b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/gen_20260501_005309.log new file mode 100644 index 0000000000000000000000000000000000000000..5a690f4fd1c621b3e91540189a5d2933fd62ad29 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/gen_20260501_005309.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d00869193a3d6da4619a20b3b2d02f2071ba38b7cf188eaebfe2e30e8e6f546d +size 4557 diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/input_snapshot.json b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..28cd3a4d75d18efdf20f8d2e0b931a2ecdef48e9 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/models_tabdiff/trained.pt b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/models_tabdiff/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc319a55a9c7152a1137f968b6fa1c735125ad11 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/models_tabdiff/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:306660b8aad4549267c68390b017e15ddb9bc06a02c80515eb72a97ae31a81eb +size 74 diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/normalized_schema_snapshot.json b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/public_gate_report.json 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"/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/staged_input_manifest.json b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1590344d2101ed87d7b9f7b7941a5c9dacad00a9 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260501_004659/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabdiff/tabdiff-m4-20260501_004659/staged/public/val.csv", + 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+ }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "numerical", + "computer_representation": "Float" + } + } + }, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "inverse_idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "idx_name_mapping": { + "0": "age", + "1": "bmi", + "2": "children", + "3": "sex", + "4": "smoker", + "5": "region", + "6": "charges" + } +} \ No newline at end of file diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/real.csv b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..85ff7f4464fa73c1d58767739e7b259f9371a55b --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e031e2816aa79e4e5fab4c7fe470741eb2d287e493d74b9cfda68441799e07fa +size 60773 diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/test.csv b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..85ff7f4464fa73c1d58767739e7b259f9371a55b --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e031e2816aa79e4e5fab4c7fe470741eb2d287e493d74b9cfda68441799e07fa +size 60773 diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/val.csv b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..85ff7f4464fa73c1d58767739e7b259f9371a55b --- /dev/null +++ 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0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_val.npy b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..717f965e62bd5595d6df01d1ad7851fc5768dc29 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/tabular_bundle/pipeline_m4/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c64fea6ad5ed2f8441ef5212c726195dbf53e1c81298427bddd18eb20e35cf2 +size 8996 diff --git a/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/train_20260501_004659.log b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/train_20260501_004659.log new file mode 100644 index 0000000000000000000000000000000000000000..ccd8d5f878ad3a19cbb3567447853c0bcf31d2c8 --- /dev/null +++ b/timecost/m4/tabdiff/tabdiff-m4-20260501_004659/train_20260501_004659.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a2a12f73486cd9d00b992f6e238a862d617a8acbb201d9e42306dc2cc94aa62c +size 292966 diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/_tabpfgen_generate.py b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b849aba2d7577b09e1ec71efab398c568f41bbb7 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv") +target_col = "charges" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(2217) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression") +X_syn, y_syn = gen.generate_regression(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv") diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/gen_20260501_005403.log b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/gen_20260501_005403.log new file mode 100644 index 0000000000000000000000000000000000000000..8dabb9bfeb17f6585cbad340648bc085e92a2a3e --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/gen_20260501_005403.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96a46fe5ac21305487900ec49903c345aa1a96959e97976873b1b757cf174d2c +size 951 diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/input_snapshot.json b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e4ec9d0bc32394ab638c94bcf784b81478f2f39 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/normalized_schema_snapshot.json b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/staged_input_manifest.json b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2f421eb9ce8e3dc6ab98883a4ccc7a24d321d222 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv", + "test_csv": 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a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/runtime_result.json b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..7fae3acd256c90f33b3674e66b3dc94ce875e757 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "run_id": "tabpfgen-m4-20260501_005403", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403" + }, + "timings": { + "train": { + "started_at": "2026-05-01T00:54:03", + "ended_at": "2026-05-01T00:54:03", + "duration_sec": 0.006 + }, + "generate": { + "started_at": "2026-05-01T00:54:03", + "ended_at": "2026-05-01T00:54:23", + "duration_sec": 19.969 + } + } +} \ No newline at end of file diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/adapter_report.json 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/dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/model_input_manifest.json b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..911959d0545938ed704c81ed6c65d04c5457cc3d --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabpfgen", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv new file mode 100644 index 0000000000000000000000000000000000000000..892796d4a836fc0927c42a7dd32ff348360a4c67 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen-m4-2217-20260501_005403.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3d6014563799a9f3a5e5a61e77d16b4e3516f68004a5bfccafb8d31b382ade8 +size 151515 diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen_meta.json b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..b7d04611615bf3b7afd5f5e71ce493a235046145 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabpfgen/tabpfgen-m4-20260501_005403/staged/public/staged_features.json", + "target_col": "charges", + "is_classification": false, + "task_type": "regression", + "n_rows": 2217, + "n_cols": 7 +} \ No newline at end of file diff --git a/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/train_20260501_005403.log b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/train_20260501_005403.log new file mode 100644 index 0000000000000000000000000000000000000000..2bc33582165aff2ed37bc5f569b0741f3ac7be47 --- /dev/null +++ b/timecost/m4/tabpfgen/tabpfgen-m4-20260501_005403/train_20260501_005403.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8cafc6bd1a580da66b0197a0595821f6de2ce962295c27af83354e897305bbcc +size 595 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_sample.py b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..3446b13345a06b8e796ba47cccc3dae0abca43c8 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118" +dataname = "tabsyn_m4" +output_csv = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/tabsyn-m4-2217-20260501_004653.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 2217 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_train.py b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4b8ffe2256df09defa4e72d758ca779366cd74d1 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118" +dataname = "tabsyn_m4" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_test.npy b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_train.npy b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..35977ccea6d36736d37a5000108e57633f2bb227 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8893cf5626d7469e90c946b0f59fca148a445e109a27fa5a2acdd425b6bb7d2 +size 53336 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_test.npy b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_train.npy b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..dc149960a879e21fd1aeed7de2419851175627e7 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4a21bd2fd5a226c07d543ddf78616a8edb2d52b51097fc7a5df5b05612267af8 +size 26732 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/info.json b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/info.json new file mode 100644 index 0000000000000000000000000000000000000000..a6affaecfbfc8f45e63d3ab86bbffb378c04caa5 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/info.json @@ -0,0 +1,91 @@ +{ + "name": "tabsyn_m4", + "task_type": "regression", + "n_num_features": 3, + "n_cat_features": 3, + "train_size": 2217, + "num_col_idx": [ + 0, + 2, + 3 + ], + "cat_col_idx": [ + 1, + 4, + 5 + ], + "target_col_idx": [ + 6 + ], + "column_names": [ + "age", + "sex", + "bmi", + "children", + "smoker", + "region", + "charges" + ], + "train_num": 2217, + "test_num": 2217, + "header": 0, + "file_type": "csv", + "data_path": "data/tabsyn_m4/train.csv", + "test_path": null, + "idx_mapping": { + "0": 0, + "1": 3, + "2": 1, + "3": 2, + "4": 4, + "5": 5, + "6": 6 + }, + "inverse_idx_mapping": { + "0": 0, + "3": 1, + "1": 2, + "2": 3, + "4": 4, + "5": 5, + "6": 6 + }, + "idx_name_mapping": { + "0": "age", + "1": "sex", + "2": "bmi", + "3": "children", + "4": "smoker", + "5": "region", + "6": "charges" + }, + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "2": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "3": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "categorical" + }, + "4": { + "sdtype": "categorical" + }, + "5": { + "sdtype": "categorical" + }, + "6": { + "sdtype": "numerical", + "computer_representation": "Float" + } + } + } +} \ No newline at end of file diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/test.csv b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/train.csv b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..152c9a200d3e5ce590ed1621c80749a36fc97e13 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f64f75063e2f8d838d7f7040ebc12f8c46df4dc54450874b851786224ba875 +size 60802 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_test.npy b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c1d23d8e35b403e2d772e5544af9d68ff32a8ea0 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d6e4ad93beee28740cc25ee808a180c01fa94a053f79ff383504888f2d8f39e +size 17864 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_train.npy b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..c1d23d8e35b403e2d772e5544af9d68ff32a8ea0 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/data/tabsyn_m4/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d6e4ad93beee28740cc25ee808a180c01fa94a053f79ff383504888f2d8f39e +size 17864 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/gen_20260501_004653.log b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/gen_20260501_004653.log new file mode 100644 index 0000000000000000000000000000000000000000..6d052f7d521e744b02159470dee052bdb17086df --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/gen_20260501_004653.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af7610447f698cf17d603bce882093da4e5c45e69d1cf8b99faad5f1e536496d +size 930 diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/input_snapshot.json b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..21fe15b6e6863f7441ca4017d82218a03e3a90b4 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tabsyn", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": 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"profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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"status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/staged_input_manifest.json b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b7c70717cfee2c8bde2c692c55e2e2627ed5f184 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260501_002118/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + 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b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/model_input_manifest.json b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1234a03c0f902a27d8b3f09a352a58b6abdb20fc --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tabsyn", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + 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a/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/train_20260501_002118.log b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/train_20260501_002118.log new file mode 100644 index 0000000000000000000000000000000000000000..1c27436cf4bfaa4e9830827e0101afdfb87664a5 --- /dev/null +++ b/timecost/m4/tabsyn/tabsyn-m4-20260501_002118/train_20260501_002118.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3c6391abc9ca72c238e7717562f1ed0ee8afb97fbb34f6e20088940e2044c724 +size 2153039 diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260501_055812/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..fd4d7affa32f0d281c334aa3ed7cf8baff921c00 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260501_055812/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ef5925ca28ee1f4772e7c03eee8d4a0207656339 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/gen_20260501_055834.log b/timecost/m4/tvae/tvae-m4-20260501_055812/gen_20260501_055834.log new file mode 100644 index 0000000000000000000000000000000000000000..f33c02adfb39603116028ea6d8a455ac9ca79881 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/gen_20260501_055834.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85af262332f60beb0aa22d3000de71b318f00926a2c27a0273b16f183329101c +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260501_055812/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": 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"c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/models_300epochs/train_20260501_055812.log b/timecost/m4/tvae/tvae-m4-20260501_055812/models_300epochs/train_20260501_055812.log new file mode 100644 index 0000000000000000000000000000000000000000..5a04b28e3bdce4d1c68ce1066d178dda6f0a7475 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/models_300epochs/train_20260501_055812.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cea974684b89ac9abfdc4feedad02c71b9abaf15de17fa78960b6447fc1157d8 +size 531 diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..843058a63404d636cb94e88b60dbef9dc99523e0 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6abd8eec6a8d217c00b45f3f0a99a036041e4856f65e0e0c9e960159f0af57dd +size 399276 diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/normalized_schema_snapshot.json b/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/public_gate_report.json b/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/staged_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..147c38bb9ea3492a7689c35828d674faad1e8895 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/runtime_result.json b/timecost/m4/tvae/tvae-m4-20260501_055812/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..d5064d887c6d82a45ee515359535ea90e87566d6 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260501_055812", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-01T05:58:12", + "ended_at": "2026-05-01T05:58:34", + "duration_sec": 22.789 + }, + "generate": { + "started_at": "2026-05-01T05:58:34", + "ended_at": "2026-05-01T05:58:39", + "duration_sec": 5.046 + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/staged/public/staged_features.json b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/staged/public/test.csv b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/public/test.csv @@ -0,0 +1,3 @@ +version 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https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/adapter_report.json b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..54901e708e565c37d4e370db466919cfd64f3518 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/adapter_transforms_applied.json b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/model_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..206bf565c629dd02b5d193b7536019b74853e6b2 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260501_055812/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv b/timecost/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv new file mode 100644 index 0000000000000000000000000000000000000000..4b6d8c9c71a48a66edcac9603890345aa2a286eb --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/tvae-m4-2217-20260501_055834.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b9348dc4652c821a0d9d0063497456c91ee369c89f18f6b1a4c8104120077d71 +size 135727 diff --git a/timecost/m4/tvae/tvae-m4-20260501_055812/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260501_055812/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260501_055812/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..64d07e128ef6dbb294fc5e3083f11bfa36692685 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d28215e962c43113cd7268680d82572ec243c015 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/gen_20260504_172948.log b/timecost/m4/tvae/tvae-m4-20260504_172924/gen_20260504_172948.log new file mode 100644 index 0000000000000000000000000000000000000000..9cb264d80125eb9865bcf1ff211a5f47e5408296 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/gen_20260504_172948.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:21074009efaaf889eeada81da04fe08875f77e84e1c9ad6863c7e7801d6f0f96 +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_172924/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/models_300epochs/train_20260504_172924.log b/timecost/m4/tvae/tvae-m4-20260504_172924/models_300epochs/train_20260504_172924.log new file mode 100644 index 0000000000000000000000000000000000000000..6c02affe89b895eda841ba107e0c4e50dd4dedbe --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/models_300epochs/train_20260504_172924.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d9cd060818ff6f5d72a3e1faf866cdd5ff16bf2b6638e45612e08b00d03d6ad +size 531 diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260504_172924/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..478a3a8f355a5b127ef8fcf413a992ab9007af33 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5aac3416f42e1ad8ca96be12c0e3109c90b138a711112aa88c40b0929a04f049 +size 399084 diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/normalized_schema_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/public_gate_report.json b/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/staged_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c0ce0d6de2936b789a292c39df659d63d078edfc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/runtime_result.json b/timecost/m4/tvae/tvae-m4-20260504_172924/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..069b138b0a41ccfe020e455a197d44d0e8e3b66c --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": 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b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git 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a/timecost/m4/tvae/tvae-m4-20260504_172924/staged/public/val.csv b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/adapter_report.json b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..d7463f2b76fda0bb44764663ad8e881e19398e60 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/adapter_transforms_applied.json b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/model_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..88352edb6469f4386dd47bc9b6c6413f5efba165 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_172924/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv b/timecost/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv new file mode 100644 index 0000000000000000000000000000000000000000..a3d27569151d13175f6106f0b9ec16a29ecd454b --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/tvae-m4-2217-20260504_172948.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc71fa06a7efd6b7984554097fbac50bc8364689311279a95ec83b921ea69076 +size 134967 diff --git a/timecost/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_172924/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_174302/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..81b1295a580f8a6f079ccfcc3c40e66fb290a4a7 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1aa090f428d3524915512adaba16283538755528 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/gen_20260504_174327.log b/timecost/m4/tvae/tvae-m4-20260504_174302/gen_20260504_174327.log new file mode 100644 index 0000000000000000000000000000000000000000..60c093f4fc68d51d4270c47f02ced7614c438a31 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/gen_20260504_174327.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af461c511bf86e5d1de31bd40d8796e533698a811681c73ac90e6480905b46d5 +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_174302/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/models_300epochs/train_20260504_174302.log b/timecost/m4/tvae/tvae-m4-20260504_174302/models_300epochs/train_20260504_174302.log new file mode 100644 index 0000000000000000000000000000000000000000..07fcade7ce7aa7bc4269426a8a925a7ef169b3ce --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/models_300epochs/train_20260504_174302.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:683a06ca92238c36301c9b1f7f7deb14006752d9a9b57b1e9313fb9933a379ef +size 620 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..16b24bb489870d2ce6bead2362af3bf11f079ff6 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51665a4e34a979ffcc118e07a61e66b7ff84eda7587c6ebd1fe903b01015b6ab +size 399148 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/normalized_schema_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/public_gate_report.json b/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..66a493d45188588761c51eab52795cfad070bf87 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": 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"impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/run_config.json b/timecost/m4/tvae/tvae-m4-20260504_174302/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..435f7cd9c975afc4a9b23a62dc58674a4b1f98c1 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:43:02", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "64,64", + "TVAE_DECOMPRESS_DIMS": "64,64", + "TVAE_EMBEDDING_DIM": "32", + "TVAE_EPOCHS": "100" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/runtime_result.json b/timecost/m4/tvae/tvae-m4-20260504_174302/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..cb553f8c827cd5d12fe1aa3ab0e87b8651244569 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_174302", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:43:02", + "ended_at": "2026-05-04T17:43:27", + "duration_sec": 24.249 + }, + "generate": { + "started_at": "2026-05-04T17:43:27", + "ended_at": "2026-05-04T17:43:32", + "duration_sec": 5.407 + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/staged_features.json b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/test.csv b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_report.json b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..5a09a4742de3a8b219cf8df46bd0846a8ec726d2 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_transforms_applied.json b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..068bb0b9d0b426739333b63a6ec98858fc30bda9 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174302/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv b/timecost/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv new file mode 100644 index 0000000000000000000000000000000000000000..cc89d367bbc77fcaa87035697f57afb63c3dd0f5 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/tvae-m4-2217-20260504_174327.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:18426a62b60277585bdaec0b71059b5102ff55a45beba0836da8fbf8759b3a39 +size 135340 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174302/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5752f6a3066cdfc5f9df8ba23e3090b04584f4d8 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..aca9f4cee652b8d957c0618750c8f79706afa308 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/gen_20260504_174408.log b/timecost/m4/tvae/tvae-m4-20260504_174345/gen_20260504_174408.log new file mode 100644 index 0000000000000000000000000000000000000000..8fc5e6292cb4d277e08212280353a2066877709f --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/gen_20260504_174408.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6a4497f79ab8d0c7ce135c4864c2a53a2e7353da7444daaf7b0e9dde72e02f5e +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_174345/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/models_300epochs/train_20260504_174345.log b/timecost/m4/tvae/tvae-m4-20260504_174345/models_300epochs/train_20260504_174345.log new file mode 100644 index 0000000000000000000000000000000000000000..d9c3fff9bb4dbe5599922e1d080551aca2b86361 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/models_300epochs/train_20260504_174345.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c494110b8fec6c70cf01186532504c957a2de78d8140a7fb363f74601b4b2a18 +size 619 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..0c28da14e1cbc85fc7636e69a71272a7c7923f92 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:81942ae13ef96a3ad65261194e36070237c40e9a5c95b8dc18ed277045304175 +size 399020 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/public_gate/normalized_schema_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_174345/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/public_gate/public_gate_report.json b/timecost/m4/tvae/tvae-m4-20260504_174345/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/public_gate/staged_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_174345/public_gate/staged_input_manifest.json new file mode 100644 index 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/run_config.json b/timecost/m4/tvae/tvae-m4-20260504_174345/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6a0d856803666f7f8f0fd1b32a1a8b71d17c669c --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:43:45", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "128,128", + "TVAE_DECOMPRESS_DIMS": "128,128", + "TVAE_EMBEDDING_DIM": "64", + "TVAE_EPOCHS": "200" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/runtime_result.json b/timecost/m4/tvae/tvae-m4-20260504_174345/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..018199b620d8be1125db7cf1c4d80366a23151f7 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_174345", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:43:45", + "ended_at": "2026-05-04T17:44:08", + "duration_sec": 23.351 + }, + "generate": { + "started_at": "2026-05-04T17:44:08", + "ended_at": "2026-05-04T17:44:13", + "duration_sec": 5.268 + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/staged_features.json b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/test.csv b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/val.csv b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/adapter_report.json b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..8c95e5df24a3ad9d5cecc4a4ab707a886869ef49 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/adapter_transforms_applied.json b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/model_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8310da218087853e3398cb155bdf4b9c096a06bc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174345/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv b/timecost/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv new file mode 100644 index 0000000000000000000000000000000000000000..57b7a745bd77beb6a86f026fe8306bda5886a559 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/tvae-m4-2217-20260504_174408.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:083f6155edb8f495beff37d43146b53d55fbeb8eedb579e53a6ff1db820317d5 +size 135606 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174345/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174426/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_174426/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..986470909887e64074ae7b5468923539ce59c61e --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174426/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae-m4-2217-20260504_174448.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..51849963b3e68038221e8e94145da119f0101e40 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174426/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log b/timecost/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log new file mode 100644 index 0000000000000000000000000000000000000000..bf3bec0ffb0b16650e96487ab6aef4b913e7cf21 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174426/gen_20260504_174448.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a129533a2ad4f0771f09c7c1113903202b4304c36ffa3260f021cba649543cbe +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174426/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_174426/models_300epochs/train_20260504_174426.log b/timecost/m4/tvae/tvae-m4-20260504_174426/models_300epochs/train_20260504_174426.log new file mode 100644 index 0000000000000000000000000000000000000000..3aaedef345ca67f0cd8136ae4946e016390d447b --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174426/models_300epochs/train_20260504_174426.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:131de4dfe1a2d5ce638b8f5ba437d185fecbcc78924c59ec2071c9ab1a059d50 +size 620 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e16266a5a93d9224447326b03ce66256a1e2b41b --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174426/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:08da8f0a648e3fdfb1e1c8b1ea7d76832c8955a5b3f523c056b91db92405d19a +size 684524 diff --git a/timecost/m4/tvae/tvae-m4-20260504_174426/public_gate/normalized_schema_snapshot.json 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b/timecost/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_174426/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b70a0b4d228b99bd6d3e71c9b31701fd7efe7eea --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..72d435e2a4019187fa552c7ef6488a04c198cb74 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/gen_20260504_175349.log b/timecost/m4/tvae/tvae-m4-20260504_175331/gen_20260504_175349.log new file mode 100644 index 0000000000000000000000000000000000000000..d80b9a01a901102579202b4593edb4c4ee21e77f --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/gen_20260504_175349.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:198de309a988fdec10f4dd85fbbc1f0f354ac22664545de9e229b6a40353e304 +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175331/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": 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a/timecost/m4/tvae/tvae-m4-20260504_175331/models_300epochs/train_20260504_175331.log b/timecost/m4/tvae/tvae-m4-20260504_175331/models_300epochs/train_20260504_175331.log new file mode 100644 index 0000000000000000000000000000000000000000..84b1f25f76db42e58f5013b6169e84d8a08f70ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/models_300epochs/train_20260504_175331.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef9355f3042f9be1f1e24f023ec141d00b5472f85f7b583ac3988e3bd9ecf510 +size 614 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e229cbd8af4127f12660e01a5dc741635c564f0e --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f00efa1443d564da74605e9f2b5702368cf97a6e2495daf901b29414f34e9ecf +size 264748 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/public_gate/normalized_schema_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175331/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": 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"2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json b/timecost/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": 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"1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/run_config.json b/timecost/m4/tvae/tvae-m4-20260504_175331/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..71797307e0a97f2af74b7c2cf967ace2547d9bad --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:53:31", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "64,64", + "TVAE_DECOMPRESS_DIMS": "64,64", + "TVAE_EMBEDDING_DIM": "32", + "TVAE_EPOCHS": "100" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/runtime_result.json b/timecost/m4/tvae/tvae-m4-20260504_175331/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..8d9fff1d4d3a73f0ab5c57ec96df348e154dbb2f --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175331", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:53:31", + "ended_at": "2026-05-04T17:53:49", + "duration_sec": 18.291 + }, + "generate": { + "started_at": "2026-05-04T17:53:49", + "ended_at": "2026-05-04T17:53:54", + "duration_sec": 4.931 + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/val.csv b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_report.json b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..470c1d0d8debe339dfbb1574cee4a3eafe1c899e --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_transforms_applied.json b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a10b01ee4c8705e73edbac3554347b0446b4fbd4 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175331/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv b/timecost/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv new file mode 100644 index 0000000000000000000000000000000000000000..b4180a57b42d16ad5884478676d597804f2a2abc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/tvae-m4-2217-20260504_175349.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b3901b669115b3a46120c956bc92e368c0b0ebe459743844f643091b7d2c6b1c +size 135479 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175331/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ce22b690c0cf1d6a0c43dff49a21a99b4a9955a0 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175407/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..d145109f933ae3ccfbc8e5969da96387a97fcd43 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175407/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175407/gen_20260504_175431.log b/timecost/m4/tvae/tvae-m4-20260504_175407/gen_20260504_175431.log new file mode 100644 index 0000000000000000000000000000000000000000..7a2bdd2a830702ca913ec67da6b9cb26556e1acc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175407/gen_20260504_175431.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2d559e320b3bee74da788cc285a06ae8720e75230a0db1eb51ca71e53784f832 +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175407/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175407/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175407/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": 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+ "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175407/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv b/timecost/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv new file mode 100644 index 0000000000000000000000000000000000000000..6782c785f5ba68b4490045ef62ffb08322d5d0f1 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175407/tvae-m4-2217-20260504_175431.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d1beaf0f631c81739f92d94d2b563d598045876727c2857bc5d562accd79a344 +size 135440 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175407/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_175449/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8ce529c5975d8135d8bc87e2d902ce1cd0a5fab7 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0ddb2b7321a12d3aff39be0c0f3c4c23e022d041 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/gen_20260504_175513.log b/timecost/m4/tvae/tvae-m4-20260504_175449/gen_20260504_175513.log new file mode 100644 index 0000000000000000000000000000000000000000..a9e912bdc4026a088ea34ccdeb07d10973cd5245 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/gen_20260504_175513.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:506bc0358a88c82bed1b729b7efcaccb73aea52807b692c21a5262b6c5c6854c +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175449/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/models_300epochs/train_20260504_175449.log b/timecost/m4/tvae/tvae-m4-20260504_175449/models_300epochs/train_20260504_175449.log new file mode 100644 index 0000000000000000000000000000000000000000..68c1a34e689a2d713886f2cc826e6c9f60bbf379 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/models_300epochs/train_20260504_175449.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e03fc03009b999dc1131d532847efc117ab94d363f9730f715066728ae278fb +size 620 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..09fa703d767213aaac45b0e81a2180a81d6e5db5 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13f1bc9bbe9511781fbaebc70c932da3e4ffb82b1eb24a2d7326a204046dc8b8 +size 684524 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/normalized_schema_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/public_gate_report.json b/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6c44a4622897b6a80eac1f92d52e00343f0eacc6 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "500", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "300" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/runtime_result.json b/timecost/m4/tvae/tvae-m4-20260504_175449/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..c3792a3ec28b2df18468254616f76eed6e51ce7f --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175449", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:54:49", + "ended_at": "2026-05-04T17:55:13", + "duration_sec": 23.522 + }, + "generate": { + "started_at": "2026-05-04T17:55:13", + "ended_at": "2026-05-04T17:55:18", + "duration_sec": 5.229 + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/staged_features.json b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/val.csv b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_report.json b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..879d3f0289b8638013ac026e0da5150aa5c0b9f0 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_transforms_applied.json b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c3c9d78fcce98d02acf18fdedf44445fe601e6b1 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175449/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv b/timecost/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv new file mode 100644 index 0000000000000000000000000000000000000000..0f5c77368d7ce704a1accc5fa2633d69ea7b94dd --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/tvae-m4-2217-20260504_175513.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e98a12bdd925dfd2cb11667b7709d30496bb352aa2a492439db83409ec7f05da +size 135526 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175449/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_175530/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5630436cb207679ca36a2faa95aed68d8b90e70c --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8bddc1e2fca3b1f61308cc45e156164c030dd8b2 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/gen_20260504_175610.log b/timecost/m4/tvae/tvae-m4-20260504_175530/gen_20260504_175610.log new file mode 100644 index 0000000000000000000000000000000000000000..4b1bb491e22a5f18459632adf927b43ede912e80 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/gen_20260504_175610.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5cf0eef736ccb09b775ee91e91bf1cecff4d38769b05d4bf2e40228f12f99156 +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175530/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/models_300epochs/train_20260504_175531.log b/timecost/m4/tvae/tvae-m4-20260504_175530/models_300epochs/train_20260504_175531.log new file mode 100644 index 0000000000000000000000000000000000000000..ab63c7540b64e9f038a5e7f537de8ffa11294c20 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/models_300epochs/train_20260504_175531.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eee2f0fc9338e7e8903f28b8c9484dae9bd01ffaaf28d9ca7d689b117e9646d2 +size 620 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..2f0cecc3a52a25f09009f0d041d9abed7a4eda90 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:312b6cfa7a04181e1c825c875e767675a86c53ef0f51a073830ebdeafb82b715 +size 765292 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/normalized_schema_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json b/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1ccf316d39cda13b861175fe16fcd94babb6fe6a --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/run_config.json b/timecost/m4/tvae/tvae-m4-20260504_175530/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..21e8788fec3a6356cd17b49ec03f3ea82d3406fa --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:55:31", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/runtime_result.json b/timecost/m4/tvae/tvae-m4-20260504_175530/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..65b42c7cb5ae1dc51773c686c000d66f14213373 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175530", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:55:31", + "ended_at": "2026-05-04T17:56:10", + "duration_sec": 39.917 + }, + "generate": { + "started_at": "2026-05-04T17:56:10", + "ended_at": "2026-05-04T17:56:16", + "duration_sec": 5.502 + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/staged/public/staged_features.json b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/staged/public/test.csv b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ 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b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_report.json b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..37f886a4c807fe8b69e99c1104f0e36d6c6adf2e --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_transforms_applied.json b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cb351c79f41d5f0ba4ad75267366eb75b2b99a89 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175530/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv b/timecost/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv new file mode 100644 index 0000000000000000000000000000000000000000..606a7d02f0adf19904c4d4ba33d3a4dfea5ca992 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/tvae-m4-2217-20260504_175610.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2fb289413b48175dafe06bf13265b44045456603a5d208e7fe589e2889bdb54b +size 135518 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175530/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py b/timecost/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..39004fb75945aac1f990221a91985deb650fa5a0 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt") +total = 2217 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py b/timecost/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..024f990670dcbc746aaa55122c04dee9d8ff3a9e --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/gen_20260504_175710.log b/timecost/m4/tvae/tvae-m4-20260504_175629/gen_20260504_175710.log new file mode 100644 index 0000000000000000000000000000000000000000..a795a8030882a84a26d73a4c573aaeb8f8e885c9 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/gen_20260504_175710.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d622877b81551b0ad684e4a5085c2911727d027af9a46fc420364324caa62b56 +size 404 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/input_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175629/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..60c34c26acabd61c3c87a32ef8cbe89659d8c727 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "exists": true, + "size": 92191, + "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "exists": true, + "size": 11482, + "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv", + "exists": true, + "size": 11559, + "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json", + "exists": true, + "size": 3336, + "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json", + "exists": true, + "size": 3810, + "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9" + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/models_300epochs/train_20260504_175629.log b/timecost/m4/tvae/tvae-m4-20260504_175629/models_300epochs/train_20260504_175629.log new file mode 100644 index 0000000000000000000000000000000000000000..27900ca9d641e9523358a5597637ab7644edf35b --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/models_300epochs/train_20260504_175629.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:320957d0779658cd00032ad2ad674c5da8cf66cf3de6c185b2919b669f0d319e +size 620 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt b/timecost/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..3cc976141c8eb3bb7f28b0ada7070e0b9b6d8632 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b777c51e837561a377d700aa332ff47b5f936225dec80dde0479ca8c348aeba5 +size 895788 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/normalized_schema_snapshot.json b/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..31ed9818686b11907df75e872e4ffbe010330880 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,147 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/public_gate_report.json b/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a25408e2574124629ea9437356551e4d6f4790 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m4", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "charges", + "task_type": "regression", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0bde7a4febf07fdb8780f652af167f5f9405b7c2 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json @@ -0,0 +1,152 @@ +{ + "dataset_id": "m4", + "target_column": "charges", + "task_type": "regression", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv", + "val_csv": 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"1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/run_config.json b/timecost/m4/tvae/tvae-m4-20260504_175629/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..705b37f42cbc60a30de4fe4a4b0df2f4a0340311 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:56:29", + "dataset_id": "m4", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "m4", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 2217, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json", + "target_column": "charges", + "task_type": "regression" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "256", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/runtime_result.json b/timecost/m4/tvae/tvae-m4-20260504_175629/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..617455803da9ab7119a01451262c6b607ec6b6c0 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "run_id": "tvae-m4-20260504_175629", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:56:29", + "ended_at": "2026-05-04T17:57:10", + "duration_sec": 40.056 + }, + "generate": { + "started_at": "2026-05-04T17:57:10", + "ended_at": "2026-05-04T17:57:15", + "duration_sec": 5.089 + } + } +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..d6acd098c35111f423f3ffff2cfd1754bc5dfebc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json @@ -0,0 +1,37 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sex", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "bmi", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "smoker", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "region", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "charges", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..c88a2e7cfcd715bb421cb56eb078033da45c4dc8 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea +size 11298 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..5a05019d786529e0617291766c4fbe016ca01bf5 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837 +size 90069 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..d78fe5ad1537c437ba9ac2629f2aa60a6c17e708 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7 +size 11218 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_report.json b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e86089d7f904cc28c7d67d3d0e6ee530bd1bcf07 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_transforms_applied.json b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a7e71fa71d160ed2e4c98843a542042bbf3ca4d6 --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/staged/tvae/model_input_manifest.json @@ -0,0 +1,154 @@ +{ + "dataset_id": "m4", + "model": "tvae", + "target_column": "charges", + "task_type": "regression", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.0212, + "example_values": [ + "46", + "38", + "19", + "27", + "26" + ] + } + }, + { + "name": "sex", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "female", + "male" + ] + } + }, + { + "name": "bmi", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 538, + "unique_ratio": 0.24267, + "example_values": [ + "23.655", + "19.3", + "30.59", + "32.67", + "29.45" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.002706, + "example_values": [ + "1", + "0", + "3", + "2", + "5" + ] + } + }, + { + "name": "smoker", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000902, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "region", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.001804, + "example_values": [ + "northwest", + "southwest", + "southeast", + "northeast" + ] + } + }, + { + "name": "charges", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1281, + "unique_ratio": 0.577808, + "example_values": [ + "21677.28345", + "15820.699", + "1639.5631", + "2497.0383", + "2897.3235" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tvae/tvae-m4-20260504_175629/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv b/timecost/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv new file mode 100644 index 0000000000000000000000000000000000000000..b76f452e50bc1efbd6d3d66b11321686efd20c3f --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/tvae-m4-2217-20260504_175710.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:67f6b6e7690fa478a62874ea75e9b98054f344b22f6d643f1a56f557b739bfe1 +size 135661 diff --git a/timecost/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json b/timecost/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d89b1e0b3095638c982d4d3321d21a8bedcd33ae --- /dev/null +++ b/timecost/m4/tvae/tvae-m4-20260504_175629/tvae_metadata.json @@ -0,0 +1,32 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "sex", + "type": "categorical" + }, + { + "name": "bmi", + "type": "continuous" + }, + { + "name": "children", + "type": "continuous" + }, + { + "name": "smoker", + "type": "categorical" + }, + { + "name": "region", + "type": "categorical" + }, + { + "name": "charges", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/m6/arf/arf-m6-20260429_032047/_arf_generate.py b/timecost/m6/arf/arf-m6-20260429_032047/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..6cb528453a62781087fcdee2e0ef2bfe9fc937a8 --- /dev/null +++ b/timecost/m6/arf/arf-m6-20260429_032047/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(9864) +c_csv = "/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm6' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv") diff --git a/timecost/m6/arf/arf-m6-20260429_032047/_arf_train.py b/timecost/m6/arf/arf-m6-20260429_032047/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4c94a2c5482897564db4c8d2062ea1537941cfad --- /dev/null +++ b/timecost/m6/arf/arf-m6-20260429_032047/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf_model.pkl") diff --git a/timecost/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv b/timecost/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv new file mode 100644 index 0000000000000000000000000000000000000000..a277909403d58ec16ee3d3127ec7a09d7532186e --- /dev/null +++ b/timecost/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e131b12db9695b205eb97e35ed4baf665c6de67084e3117089dd0d357fa53710 +size 922076 diff --git a/timecost/m6/arf/arf-m6-20260429_032047/arf_model.pkl b/timecost/m6/arf/arf-m6-20260429_032047/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..1e6a10f077a99b9ce2a537daaa5677ec473dd5d3 --- /dev/null +++ b/timecost/m6/arf/arf-m6-20260429_032047/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "VisitorType", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m6/arf/arf-m6-20260429_032047/public_gate/staged_input_manifest.json b/timecost/m6/arf/arf-m6-20260429_032047/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0893a4ceeba7a84df644f2ded26836b6f19a86ce --- /dev/null +++ b/timecost/m6/arf/arf-m6-20260429_032047/public_gate/staged_input_manifest.json @@ -0,0 +1,382 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + 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sha256:d16d0389cb0fe4b23bab344dc10070de6678357a9452f9f620d0eeba66a6b12d +size 116376 diff --git a/timecost/m6/arf/arf-m6-20260429_032047/staged/public/train.csv b/timecost/m6/arf/arf-m6-20260429_032047/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae3a266ed7bebf02bc27c50b9dea6e8c5fe71c10 --- /dev/null +++ b/timecost/m6/arf/arf-m6-20260429_032047/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e1f0338c367408dc21d5a4ec9cdc5d3fe8188916db6085f3fd326304a55551e1 +size 924849 diff --git a/timecost/m6/arf/arf-m6-20260429_032047/staged/public/val.csv b/timecost/m6/arf/arf-m6-20260429_032047/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..60be57a3609a30d56d27e37e49a3aedfa6294db5 --- /dev/null +++ b/timecost/m6/arf/arf-m6-20260429_032047/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/timecost/m6/arf/arf-m6-20260429_032047/train_20260429_032047.log b/timecost/m6/arf/arf-m6-20260429_032047/train_20260429_032047.log new file mode 100644 index 0000000000000000000000000000000000000000..3b773cb689f7da622f857def3fc046a5d355ddd4 --- /dev/null +++ b/timecost/m6/arf/arf-m6-20260429_032047/train_20260429_032047.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:298d13ce543b6bb0c550ea0a1193e8cc1a62b93915e781e52ee34396c14bc66a +size 733 diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_generate.py b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5bc8001cc4b4804555ffa3cfb61d1a3c696e0e62 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(9864) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv") diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_train.py b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0af87da8451508afc336041db8cf5a90f796f660 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl") diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv new file mode 100644 index 0000000000000000000000000000000000000000..d249fa18a3d2288f5a5229407773be778d4ba173 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ba3d31608bb7a67ddbbd04f4fa684bc6292eec77aa0698fe01f8342e22d6c00 +size 2945866 diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_coltypes.json b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..23844347edd6e059f6368acfef454dd5cf85076d --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_coltypes.json @@ -0,0 +1,77 @@ +{ + "columns": [ + { + "name": "Administrative", + "type": "continuous" + }, + { + "name": "Administrative_Duration", + "type": "continuous" + }, + { + "name": "Informational", + "type": "continuous" + }, + { + "name": "Informational_Duration", + "type": "continuous" + }, + { + "name": "ProductRelated", + "type": "continuous" + }, + { + "name": "ProductRelated_Duration", + "type": "continuous" + }, + { + "name": "BounceRates", + "type": "continuous" + }, + { + "name": "ExitRates", + "type": "continuous" + }, + { + "name": "PageValues", + "type": "continuous" + }, + { + "name": "SpecialDay", + "type": "continuous" + }, + { + "name": "Month", + "type": "categorical" + }, + { + "name": "OperatingSystems", + "type": "continuous" + }, + { + "name": "Browser", + "type": "continuous" + }, + { + "name": "Region", + "type": "continuous" + }, + { + "name": "TrafficType", + "type": "continuous" + }, + { + "name": "VisitorType", + "type": "categorical" + }, + { + "name": "Weekend", + "type": "categorical" + }, + { + "name": "Revenue", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..24891982bad281214bb0ff39a863156193e2362b --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5d6d1d27e02dfc4fd43778d7bcec5491a42dd3303826202c9476a8a1b40e4703 +size 15894 diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/const_cols.json b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/gen_20260429_032644.log b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/gen_20260429_032644.log new file mode 100644 index 0000000000000000000000000000000000000000..bd55c0d1d3195a62b44ffd7e2f15aef1957a9d7c --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/gen_20260429_032644.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:09c4419abd6f561b2527256742c2e0f043a6e227668e28786eaabf42321caba5 +size 3661 diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/input_snapshot.json b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..99f410ec3031ca9da543eec56186120ddd23b682 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/public_gate/normalized_schema_snapshot.json b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0d48abfe61f58323fd4a425d175b354d51045ae1 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,377 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "columns": [ + { + "name": "Administrative", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/staged_features.json b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..49b25bd1347f910a2b95ef0c137d1a64bf69e0cb --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/staged_features.json @@ -0,0 +1,92 @@ +[ + { + "feature_name": "Administrative", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Administrative_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Informational", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Informational_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ProductRelated", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ProductRelated_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "BounceRates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ExitRates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "PageValues", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "SpecialDay", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "OperatingSystems", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Browser", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Region", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "TrafficType", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "VisitorType", + "data_type": "categorical", + "is_target": true + }, + { + "feature_name": "Weekend", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Revenue", + "data_type": "binary", + "is_target": false + } +] \ No newline at end of file diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/test.csv b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..3944bd554327e948b8bda8c383bd581f3fc26ec6 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d16d0389cb0fe4b23bab344dc10070de6678357a9452f9f620d0eeba66a6b12d +size 116376 diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/train.csv b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae3a266ed7bebf02bc27c50b9dea6e8c5fe71c10 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e1f0338c367408dc21d5a4ec9cdc5d3fe8188916db6085f3fd326304a55551e1 +size 924849 diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/val.csv b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..60be57a3609a30d56d27e37e49a3aedfa6294db5 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/train_20260429_032624.log b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/train_20260429_032624.log new file mode 100644 index 0000000000000000000000000000000000000000..242af21350b814cbc4b5d25dcaff811b63775008 --- /dev/null +++ b/timecost/m6/bayesnet/bayesnet-m6-20260429_032623/train_20260429_032624.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b94705f16f56f9935999c102f8a4da125287e066433fdddb3f37497ef8278d9f +size 3791 diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/_ctgan_generate.py b/timecost/m6/ctgan/ctgan-m6-20260429_032145/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..cd962c7d70b43c09c205e11c9a5f99d56451dd83 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/models_300epochs/ctgan_300epochs.pt") +total = 9864 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv") \ No newline at end of file diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv b/timecost/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv new file mode 100644 index 0000000000000000000000000000000000000000..4805c5549b3abea2b7811a1602b78c3b388702f3 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:438cc75c1fd73ef405de318fd74e2e9996bed3e8b618759f064c8f2339c86ccf +size 1829798 diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/ctgan_metadata.json b/timecost/m6/ctgan/ctgan-m6-20260429_032145/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bd02cd3078ddd1c4e6c63a87889218d62f25b7c8 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/ctgan_metadata.json @@ -0,0 +1,76 @@ +{ + "columns": [ + { + "name": "Administrative", + "type": "continuous" + }, + { + "name": "Administrative_Duration", + "type": "continuous" + }, + { + "name": "Informational", + "type": "continuous" + }, + { + "name": "Informational_Duration", + "type": "continuous" + }, + { + "name": "ProductRelated", + "type": "continuous" + }, + { + "name": "ProductRelated_Duration", + "type": "continuous" + }, + { + "name": "BounceRates", + "type": "continuous" + }, + { + "name": "ExitRates", + "type": "continuous" + }, + { + "name": "PageValues", + "type": "continuous" + }, + { + "name": "SpecialDay", + "type": "continuous" + }, + { + "name": "Month", + "type": "categorical" + }, + { + "name": "OperatingSystems", + "type": "continuous" + }, + { + "name": "Browser", + "type": "continuous" + }, + { + "name": "Region", + "type": "continuous" + }, + { + "name": "TrafficType", + "type": "continuous" + }, + { + "name": "VisitorType", + "type": "categorical" + }, + { + "name": "Weekend", + "type": "categorical" + }, + { + "name": "Revenue", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/gen_20260429_032657.log b/timecost/m6/ctgan/ctgan-m6-20260429_032145/gen_20260429_032657.log new file mode 100644 index 0000000000000000000000000000000000000000..2a441a2bce3ea18d8654bbf19f5bd32b15d2b1aa --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/gen_20260429_032657.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19d47b8669a9e287c4c692efd3927f6d70881c595e01df8aad995a20e4d4152d +size 561 diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/input_snapshot.json b/timecost/m6/ctgan/ctgan-m6-20260429_032145/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..96d3c92e8ca24bcac92a2b04acf9589941cfbef1 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/models_300epochs/ctgan_300epochs.pt b/timecost/m6/ctgan/ctgan-m6-20260429_032145/models_300epochs/ctgan_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..20cf9189fc728db47c055e943c95bcde8160da99 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/models_300epochs/ctgan_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:241807a5f1a5e8e4b1c00fff112e27a888c7bc1752c5c78733b23b64fe95a6d4 +size 1830563 diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/models_300epochs/train_20260429_032145.log b/timecost/m6/ctgan/ctgan-m6-20260429_032145/models_300epochs/train_20260429_032145.log new file mode 100644 index 0000000000000000000000000000000000000000..19f91216940699a13718bc8d7827d29ff1baf374 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/models_300epochs/train_20260429_032145.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:493afcee1881a840880ee1f60c756fd92acc2e64c7413859ff3b145813976daa +size 3487 diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/public_gate/normalized_schema_snapshot.json b/timecost/m6/ctgan/ctgan-m6-20260429_032145/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0d48abfe61f58323fd4a425d175b354d51045ae1 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,377 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "columns": [ + { + "name": "Administrative", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 26, + "unique_ratio": 0.002636, + "example_values": [ + "0", + "3", + "2", + "6", + "1" + ] + } + }, + { + "name": "Administrative_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2789, + "unique_ratio": 0.282745, + "example_values": [ + "0", + "45.8", + "77.7", + "52", + "46.33333333" + ] + } + }, + { + "name": "Informational", + "role": "feature", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/staged_features.json b/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..49b25bd1347f910a2b95ef0c137d1a64bf69e0cb --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/staged_features.json @@ -0,0 +1,92 @@ +[ + { + "feature_name": "Administrative", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Administrative_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Informational", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Informational_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ProductRelated", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ProductRelated_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "BounceRates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ExitRates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "PageValues", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "SpecialDay", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "OperatingSystems", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Browser", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Region", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "TrafficType", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "VisitorType", + "data_type": "categorical", + "is_target": true + }, + { + "feature_name": "Weekend", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Revenue", + "data_type": "binary", + "is_target": false + } +] \ No newline at end of file diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/test.csv b/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..3944bd554327e948b8bda8c383bd581f3fc26ec6 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d16d0389cb0fe4b23bab344dc10070de6678357a9452f9f620d0eeba66a6b12d +size 116376 diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/train.csv b/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae3a266ed7bebf02bc27c50b9dea6e8c5fe71c10 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e1f0338c367408dc21d5a4ec9cdc5d3fe8188916db6085f3fd326304a55551e1 +size 924849 diff --git a/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/val.csv b/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..60be57a3609a30d56d27e37e49a3aedfa6294db5 --- /dev/null +++ b/timecost/m6/ctgan/ctgan-m6-20260429_032145/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..f9898e1189639328928f0b5de8c60707b257fa19 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce27b51e592355e14fee8f6d47e08e3f99736fcde567190e0e180a1656361059 +size 710336 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_gen.py b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..b88ba963d6d245c135ef011507e934db80fca36c --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(9864)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv', index=False) +print("saved", len(df)) diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..61976a1eee68519b8cf984e34d7a66b73b1e0a85 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["Administrative", "Administrative_Duration", "Informational", "Informational_Duration", "ProductRelated", "ProductRelated_Duration", "BounceRates", "ExitRates", "PageValues", "SpecialDay", "Month", "OperatingSystems", "Browser", "Region", "TrafficType", "Weekend", "Revenue", "VisitorType"], "cat_indexes": [10, 15, 16]} \ No newline at end of file diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_train.py b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..cfba2859ac03543dd3c16c9f2b85ae440f6bf708 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib') diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv new file mode 100644 index 0000000000000000000000000000000000000000..c7f682c5b3cb22dc5333b047335b8ad33fb75e7b --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7faf5b259fda52ff22517e77518e378b91030f3cb80a92b0f6eb6e9b35bcc216 +size 2609459 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..654c882509b6183f8c5548fa3699ec14a6e4acee --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72b48bad6de7a535f72fcdeddfceebe8a0c6146586d37ab93dff734bb9f971df +size 243056863 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/gen_20260430_054654.log b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/gen_20260430_054654.log new file mode 100644 index 0000000000000000000000000000000000000000..d27f949f80c14afae5755b6b72cd36e4d04b7613 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/gen_20260430_054654.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31252164f9e40ecd1845fe1b4af7576ffc653afed5086861d8ba6ee3c3fee702 +size 294 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/input_snapshot.json b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..4b451031aca86c57ce9fc57940bc857161b3d84f --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/models_fd/model.joblib b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..654c882509b6183f8c5548fa3699ec14a6e4acee --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72b48bad6de7a535f72fcdeddfceebe8a0c6146586d37ab93dff734bb9f971df +size 243056863 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/normalized_schema_snapshot.json b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0d48abfe61f58323fd4a425d175b354d51045ae1 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,377 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "columns": [ + { + "name": "Administrative", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 26, + "unique_ratio": 0.002636, + "example_values": [ + "0", + "3", + "2", + "6", + "1" + ] + } + }, + { + "name": "Administrative_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2789, + "unique_ratio": 0.282745, + "example_values": [ + "0", + "45.8", + "77.7", + "52", + "46.33333333" + ] + } + }, + { + "name": "Informational", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 17, + "unique_ratio": 0.001723, + "example_values": [ + "0", + "1", + "3", + "5", + "4" + ] + } + }, + { + "name": "Informational_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1074, + "unique_ratio": 0.108881, + "example_values": [ + "0", + "2", + "24", + "86.75", + "62.5" + ] + } + }, + { + "name": "ProductRelated", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 286, + "unique_ratio": 0.028994, + "example_values": [ + "13", + "8", + "63", + "15", + "3" + ] + } + }, + { + "name": "ProductRelated_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7769, + "unique_ratio": 0.787612, + "example_values": [ + "549.2", + "64", + "2435.697531", + "267.7333333", + "57.4" + ] + } + }, + { + "name": "BounceRates", + "role": 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0.215835, + "example_values": [ + "0", + "7.945368291", + "10.37042373", + "18.99269231", + "44.33548922" + ] + } + }, + { + "name": "SpecialDay", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.000608, + "example_values": [ + "0", + "0.4", + "0.2", + "0.8", + "1" + ] + } + }, + { + "name": "Month", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 10, + "unique_ratio": 0.001014, + "example_values": [ + "Nov", + "Dec", + "Jul", + "Aug", + "Mar" + ] + } + }, + { + "name": "OperatingSystems", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/staged_features.json b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..49b25bd1347f910a2b95ef0c137d1a64bf69e0cb --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/staged_features.json @@ -0,0 +1,92 @@ +[ + { + "feature_name": "Administrative", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Administrative_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Informational", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Informational_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ProductRelated", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ProductRelated_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "BounceRates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ExitRates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "PageValues", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "SpecialDay", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "OperatingSystems", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Browser", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Region", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "TrafficType", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "VisitorType", + "data_type": "categorical", + "is_target": true + }, + { + "feature_name": "Weekend", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Revenue", + "data_type": "binary", + "is_target": false + } +] \ No newline at end of file diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/test.csv b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..3944bd554327e948b8bda8c383bd581f3fc26ec6 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d16d0389cb0fe4b23bab344dc10070de6678357a9452f9f620d0eeba66a6b12d +size 116376 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/train.csv b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae3a266ed7bebf02bc27c50b9dea6e8c5fe71c10 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e1f0338c367408dc21d5a4ec9cdc5d3fe8188916db6085f3fd326304a55551e1 +size 924849 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/val.csv b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..60be57a3609a30d56d27e37e49a3aedfa6294db5 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_054459/train_20260430_054459.log b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/train_20260430_054459.log new file mode 100644 index 0000000000000000000000000000000000000000..46f00a83010e634522da8b14eef6bb05f3833b7d --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_054459/train_20260430_054459.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8173d211b9289155d4a0196a0d614b0dfb6e8797604c78d862fd30a3a6f57f46 +size 424 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_X_host.npy b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..f9898e1189639328928f0b5de8c60707b257fa19 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce27b51e592355e14fee8f6d47e08e3f99736fcde567190e0e180a1656361059 +size 710336 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_gen.py b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..b9a2a962dfa0a7c943ad1491170d5771d0ed3e2a --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(9864)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/forest-m6-9864-20260430_060226.csv', index=False) +print("saved", len(df)) diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_meta_host.json b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..61976a1eee68519b8cf984e34d7a66b73b1e0a85 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["Administrative", "Administrative_Duration", "Informational", "Informational_Duration", "ProductRelated", "ProductRelated_Duration", "BounceRates", "ExitRates", "PageValues", "SpecialDay", "Month", "OperatingSystems", "Browser", "Region", "TrafficType", "Weekend", "Revenue", "VisitorType"], "cat_indexes": [10, 15, 16]} \ No newline at end of file diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_train.py b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5ae23b74b949b85045f1bea228331d51b6c65386 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/forestdiffusion_model.joblib') diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/forest-m6-9864-20260430_060226.csv b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/forest-m6-9864-20260430_060226.csv new file mode 100644 index 0000000000000000000000000000000000000000..8fa2fddcb9a0194a109ac8622a21facccd18824b --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/forest-m6-9864-20260430_060226.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05fba7034232c9c42b466c3e9e97ddc8b27f37e7f5ea06d28d3212606c654293 +size 2610626 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/forestdiffusion_model.joblib b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..654c882509b6183f8c5548fa3699ec14a6e4acee --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72b48bad6de7a535f72fcdeddfceebe8a0c6146586d37ab93dff734bb9f971df +size 243056863 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/gen_20260430_060226.log b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/gen_20260430_060226.log new file mode 100644 index 0000000000000000000000000000000000000000..643a4a2ec8073c83dc12dd161db4de2d20760476 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/gen_20260430_060226.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d251e71349595f4888a8e24f01938bfcc659b2c60b8bfed60bf34e568ce8793b +size 294 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/input_snapshot.json b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..4b451031aca86c57ce9fc57940bc857161b3d84f --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/models_fd/model.joblib b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..654c882509b6183f8c5548fa3699ec14a6e4acee --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72b48bad6de7a535f72fcdeddfceebe8a0c6146586d37ab93dff734bb9f971df +size 243056863 diff --git a/timecost/m6/forestdiffusion/forest-m6-20260430_060004/public_gate/normalized_schema_snapshot.json b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0d48abfe61f58323fd4a425d175b354d51045ae1 --- /dev/null +++ b/timecost/m6/forestdiffusion/forest-m6-20260430_060004/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,377 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "columns": [ + { + "name": "Administrative", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 26, + "unique_ratio": 0.002636, + "example_values": [ + "0", + "3", + "2", + "6", + "1" + ] + } + }, + { + "name": "Administrative_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2789, + "unique_ratio": 0.282745, + "example_values": [ + "0", + "45.8", + "77.7", + "52", + "46.33333333" + ] + } + }, + { + "name": "Informational", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 17, + "unique_ratio": 0.001723, + "example_values": [ + "0", + "1", + "3", + "5", + "4" + ] + } + }, + { + "name": "Informational_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1074, + "unique_ratio": 0.108881, + "example_values": [ + "0", + "2", + "24", + "86.75", + "62.5" + ] + } + }, + { + "name": "ProductRelated", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 286, + "unique_ratio": 0.028994, + "example_values": [ + "13", + "8", + "63", + "15", + "3" + ] + } + }, + { + "name": "ProductRelated_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7769, + "unique_ratio": 0.787612, + "example_values": [ + "549.2", + "64", + "2435.697531", + "267.7333333", + "57.4" + ] + } + }, + { + "name": "BounceRates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1574, + "unique_ratio": 0.15957, + "example_values": [ + "0.019230769", + "0", + "0.00952381", + "0.022857143", + "0.004166667" + ] + } + }, + { + "name": "ExitRates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3953, + "unique_ratio": 0.40075, + "example_values": [ + "0.045421245", + "0.05", + "0.017460317", + "0.040606061", + "0.133333333" + ] + } + }, + { + "name": "PageValues", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2129, + "unique_ratio": 0.215835, + "example_values": [ + "0", + "7.945368291", + "10.37042373", + "18.99269231", + "44.33548922" + ] + } + }, + { + "name": "SpecialDay", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 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a/timecost/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/adapter_transforms_applied.json b/timecost/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/model_input_manifest.json b/timecost/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2f7e72effffc8694b850b636d97e87458a3bdee9 --- /dev/null +++ b/timecost/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/model_input_manifest.json @@ -0,0 +1,384 @@ +{ + "dataset_id": "m6", + "model": "realtabformer", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/realtabformer/rtf-m6-20260429_070344/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/realtabformer/rtf-m6-20260429_070344/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/realtabformer/rtf-m6-20260429_070344/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/realtabformer/rtf-m6-20260429_070344/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m6/realtabformer/rtf-m6-20260429_070344/train_20260429_070345.log b/timecost/m6/realtabformer/rtf-m6-20260429_070344/train_20260429_070345.log new file mode 100644 index 0000000000000000000000000000000000000000..fcc6b039b4e502119d478e2ee1928f92b4ee0140 --- /dev/null +++ b/timecost/m6/realtabformer/rtf-m6-20260429_070344/train_20260429_070345.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f0a24e9dd084e3b7415a7336b230837fd0b7d5b1df0aaf178e247c26cc463e7 +size 1315977 diff --git a/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_gen.py b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..2e66829e5f7859200f843199de97279266604d05 --- /dev/null +++ b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m6" +src = r"/work/output-Benchmark-trainonly-v1/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m6/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(9864)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabbyflow-m6-9864-20260429_042639.csv") diff --git a/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_train.py b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b476dece61a4f64634ae360b705233d34d361b1a --- /dev/null +++ b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m6" +src = r"/work/output-Benchmark-trainonly-v1/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/gen_20260429_042639.log b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/gen_20260429_042639.log new file mode 100644 index 0000000000000000000000000000000000000000..ebb4436cc214f8438425a01d32b03fe02a6e70e6 --- /dev/null +++ b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/gen_20260429_042639.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:538973c4de222ddfe910ca4c660e3d86f483297f671bfdc8908796020b4aee66 +size 3072 diff --git a/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/input_snapshot.json b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c11f5d9fc1b1146cb69cdc138f15cc818de081c7 --- /dev/null +++ b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "tabbyflow", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/models_tabbyflow/trained.pt b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/models_tabbyflow/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..f063a878c470d9d40f79db79881596e1e497dffd --- /dev/null +++ b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/models_tabbyflow/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adb734fec12f2251befd371dca69e481b19464aded2a6823105a01b4be6ddbe5 +size 40 diff --git a/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/public_gate/normalized_schema_snapshot.json b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0d48abfe61f58323fd4a425d175b354d51045ae1 --- /dev/null +++ b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,377 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "columns": [ + { + "name": "Administrative", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 26, + "unique_ratio": 0.002636, + "example_values": [ + "0", + "3", + "2", + "6", + "1" + ] + } + }, + { + "name": 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sha256:4495cd81dd4e25e4cece22309308cf6ac32d53e46ab3410a78c7e12900bd8f9e +size 79040 diff --git a/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6/y_train.npy b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d23ecacfe3b5a6516ea0c56d2ab30a4cbe101fd --- /dev/null +++ b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4495cd81dd4e25e4cece22309308cf6ac32d53e46ab3410a78c7e12900bd8f9e +size 79040 diff --git a/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6/y_val.npy b/timecost/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d23ecacfe3b5a6516ea0c56d2ab30a4cbe101fd --- /dev/null +++ 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0000000000000000000000000000000000000000..4989c94cb87185dd827fcbdbcb4332d02db7ba8b --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 9864 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/config_sample_20260429_052144_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/tabddpm-m6-9864-20260429_052144.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/tabddpm-m6-9864-20260429_052144.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_train.py b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c43239108b866cd1e3bd5bb14d3986e4bd9e935d --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..815be0376b6ad58f43a29712d029bb70d6aa78cb --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/data" +model_type = "mlp" +num_numerical_features = 14 +device = "cuda:0" + +[model_params] +d_in = 17 +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/config_sample_20260429_052144_r0.toml b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/config_sample_20260429_052144_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..7a58c59c63eb2ca63dd026a2599232ea472f298e --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/config_sample_20260429_052144_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/data" +model_type = "mlp" +num_numerical_features = 14 +device = "cuda:0" + +[model_params] +d_in = 17 +num_classes = 3 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 9864 +batch_size = 1000 +seed = 0 diff --git a/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/data/X_cat_train.npy 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b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..394febfeaea0c4c7a021b6ec83331f017340dec5 --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/data/info.json @@ -0,0 +1,52 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 14, + "n_cat_features": 3, + "train_size": 9864, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13 + ], + "cat_col_idx": [ + 14, + 15, + 16 + ], + "target_col_idx": [ + 17 + ], + "column_names": [ + "Administrative", + "Administrative_Duration", + "Informational", + "Informational_Duration", + "ProductRelated", + "ProductRelated_Duration", + "BounceRates", + "ExitRates", + "PageValues", + "SpecialDay", + "OperatingSystems", + "Browser", + "Region", + "TrafficType", + "Month", + "Weekend", + "Revenue", + "VisitorType" + ], + "num_classes": 3 +} \ No newline at end of file diff --git a/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/data/y_train.npy b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d23ecacfe3b5a6516ea0c56d2ab30a4cbe101fd --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4495cd81dd4e25e4cece22309308cf6ac32d53e46ab3410a78c7e12900bd8f9e +size 79040 diff --git a/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/gen_20260429_052144_r0.log b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/gen_20260429_052144_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..84edf52be8f0f2c39fbe286db75b901c7e342838 --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/gen_20260429_052144_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7872d5ab28a8c448b2b34eebec9a24c1020aa9581ab8cd58392793602116e5db +size 210523 diff 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"unique_count": 2, + "unique_ratio": 0.000203, + "example_values": [ + "FALSE", + "TRUE" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/tabddpm-m6-9864-20260429_052144.csv b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/tabddpm-m6-9864-20260429_052144.csv new file mode 100644 index 0000000000000000000000000000000000000000..a9516dc43e5d9fca7b263e4b426143044893e78f --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/tabddpm-m6-9864-20260429_052144.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d475389e311c4299c328469b46c4e3282789ebf111cf67e98e6ad63601a2d59 +size 1188819 diff --git a/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/train_20260429_052038.log b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/train_20260429_052038.log new file mode 100644 index 0000000000000000000000000000000000000000..8e5f76b3de4efcab4456a129d194981e3a3402a3 --- /dev/null +++ b/timecost/m6/tabddpm/tabddpm-m6-20260429_052038/train_20260429_052038.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:488c7c8b427e9c663b6b12b4e009ba080c2b26a0a83f6f8cd2a3023e97532281 +size 1153 diff --git a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_gen.py b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..1d84f1aa900f80f3bf11a8051db5759d352a303f --- /dev/null +++ b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m6" +src = r"/work/output-Benchmark-trainonly-v1/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m6/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(9864)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m6/tabdiff/tabdiff-m6-20260429_042700/tabdiff-m6-9864-20260429_043402.csv") diff --git a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_train.py b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a68c6a882e2ddef4634feb29817cd820ef424006 --- /dev/null +++ b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m6" +src = r"/work/output-Benchmark-trainonly-v1/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/gen_20260429_043402.log b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/gen_20260429_043402.log new file mode 100644 index 0000000000000000000000000000000000000000..a344ca6abb21968f4809751834f7d5a97c793b69 --- /dev/null +++ b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/gen_20260429_043402.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe1a358e1512516c736c71ce06d078eebb0619b7a50a4ffe8d81da33d1a568ac +size 4744 diff --git a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/input_snapshot.json b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/input_snapshot.json new 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[ + "FALSE", + "TRUE" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/public_gate/public_gate_report.json b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..99a260799c8356f445bdc0b33ccce8b459ba1938 --- /dev/null +++ b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m6", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": 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"Weekend", + "Revenue", + "VisitorType" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "2": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "3": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "4": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "5": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "6": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "7": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "8": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "9": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "10": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "11": { + 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a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_test.npy b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d23ecacfe3b5a6516ea0c56d2ab30a4cbe101fd --- /dev/null +++ b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4495cd81dd4e25e4cece22309308cf6ac32d53e46ab3410a78c7e12900bd8f9e +size 79040 diff --git a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_train.npy b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d23ecacfe3b5a6516ea0c56d2ab30a4cbe101fd --- /dev/null +++ b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4495cd81dd4e25e4cece22309308cf6ac32d53e46ab3410a78c7e12900bd8f9e +size 79040 diff --git a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_val.npy b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..8d23ecacfe3b5a6516ea0c56d2ab30a4cbe101fd --- /dev/null +++ b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4495cd81dd4e25e4cece22309308cf6ac32d53e46ab3410a78c7e12900bd8f9e +size 79040 diff --git a/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/train_20260429_042701.log b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/train_20260429_042701.log new file mode 100644 index 0000000000000000000000000000000000000000..a2a526641f857082a5244b05b3e233cd1d264f4f --- /dev/null +++ b/timecost/m6/tabdiff/tabdiff-m6-20260429_042700/train_20260429_042701.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4335aee0b3501b5df97d1dc245f7249e7c2f16190805842a0891e45c31c1fe5d +size 450169 diff --git a/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/_tabpfgen_generate.py b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..6959482ccde3d2e68d476cdc72babe8915d7b6f8 --- /dev/null +++ b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/train.csv") +target_col = "VisitorType" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(9864) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv") diff --git a/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/gen_20260429_061037.log b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/gen_20260429_061037.log new file mode 100644 index 0000000000000000000000000000000000000000..6099a453eeb443247b283676095a122ba7abaa18 --- /dev/null +++ b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/gen_20260429_061037.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1298acab3bbb43c47659b717ab2fc82eefbb9b5e3f3fe5b0bdefee94ea6ce77 +size 1009 diff --git a/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/input_snapshot.json b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8508a20b6499aa7c3d1c2d992165b962275eb5f8 --- /dev/null +++ b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/public_gate/normalized_schema_snapshot.json b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0d48abfe61f58323fd4a425d175b354d51045ae1 --- /dev/null +++ b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,377 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "columns": [ + { + "name": "Administrative", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 26, + "unique_ratio": 0.002636, + "example_values": [ + "0", + "3", + "2", + "6", + "1" + ] + } + }, + { + "name": "Administrative_Duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2789, + "unique_ratio": 0.282745, + "example_values": [ + "0", + "45.8", + "77.7", + "52", + "46.33333333" + ] + } + }, + { + "name": "Informational", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 17, + "unique_ratio": 0.001723, + "example_values": [ + "0", + "1", + "3", + "5", + "4" + ] + } + }, + { + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv new file mode 100644 index 0000000000000000000000000000000000000000..27f88c81d471d41817e16176e616a321845721a5 --- /dev/null +++ b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen-m6-9864-20260429_061037.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:add4f4a878f040f1c7d2159a18e9300a83135f301ad1a81224dd9c17e49f14b0 +size 1969051 diff --git a/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen_meta.json b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..1b37a108dd2d0f81f90a19c2823cbb8308fd8b80 --- /dev/null +++ b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tabpfgen/tabpfgen-m6-20260429_061036/staged/public/staged_features.json", + "target_col": "VisitorType", + "is_classification": true, + "task_type": "classification", + "n_rows": 9864, + "n_cols": 18 +} \ No newline at end of file diff --git a/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/train_20260429_061037.log b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/train_20260429_061037.log new file mode 100644 index 0000000000000000000000000000000000000000..c24d9e48eb43c865da36ad40085790e47b2b23e9 --- /dev/null +++ b/timecost/m6/tabpfgen/tabpfgen-m6-20260429_061036/train_20260429_061037.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb1ebbf34920b287311be084a383babac0d04e9f6379859eeb08115d9c1ee42e +size 595 diff --git a/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_sample.py b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..3aa741b6000357c1afaa1b62f5387887e827589f --- /dev/null +++ b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m6/tabsyn/tabsyn-m6-20260429_052236" +dataname = "tabsyn_m6" +output_csv = "/work/output-Benchmark-trainonly-v1/m6/tabsyn/tabsyn-m6-20260429_052236/tabsyn-m6-9864-20260429_061024.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 9864 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_train.py b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5046457476de92de33f824cb443cd014d5641228 --- /dev/null +++ b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m6/tabsyn/tabsyn-m6-20260429_052236" +dataname = "tabsyn_m6" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_test.npy b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..8884f48287fb9f926c3463bfbdd06705e40c69d6 --- /dev/null +++ b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00c2416031b4d6f8d108691a3b9c7b3009c7e0fdc44ddca005fbecf399ae2435 +size 236864 diff --git a/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_train.npy b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..8884f48287fb9f926c3463bfbdd06705e40c69d6 --- /dev/null +++ b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00c2416031b4d6f8d108691a3b9c7b3009c7e0fdc44ddca005fbecf399ae2435 +size 236864 diff --git a/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_num_test.npy b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e00fb621bec79b3db12b58f7a335ce049229c0e9 --- /dev/null +++ b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:960545f531401f06b1ef44d43f0e45228b36d96b6f6dbf87b8f6ad8578e915f5 +size 552512 diff --git a/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_num_train.npy b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e00fb621bec79b3db12b58f7a335ce049229c0e9 --- /dev/null +++ b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:960545f531401f06b1ef44d43f0e45228b36d96b6f6dbf87b8f6ad8578e915f5 +size 552512 diff --git a/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/data/tabsyn_m6/info.json 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@@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5f99d4efa88d96ad863394ad3c0e5a6dcdf8ca9c70764313637c163d5693a4e +size 867308 diff --git a/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/train_20260429_052236.log b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/train_20260429_052236.log new file mode 100644 index 0000000000000000000000000000000000000000..1661c0b8e9e39252475b2790a3cb4d2000df2c0f --- /dev/null +++ b/timecost/m6/tabsyn/tabsyn-m6-20260429_052236/train_20260429_052236.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f70864510a8ef8d700522aafa4562dd525139e29bc76893fe6bafd64fc1a85b +size 2842852 diff --git a/timecost/m6/tvae/tvae-m6-20260429_031938/_tvae_generate.py b/timecost/m6/tvae/tvae-m6-20260429_031938/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..37b58cd796975596ffd7f432f841924d50d476a2 --- /dev/null +++ b/timecost/m6/tvae/tvae-m6-20260429_031938/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/models_300epochs/tvae_300epochs.pt") +total = 9864 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv") diff --git a/timecost/m6/tvae/tvae-m6-20260429_031938/_tvae_train.py b/timecost/m6/tvae/tvae-m6-20260429_031938/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0c65c809cb6f1c9b97162d55cf1324379aff4516 --- /dev/null +++ b/timecost/m6/tvae/tvae-m6-20260429_031938/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m6/tvae/tvae-m6-20260429_031938/gen_20260429_032133.log b/timecost/m6/tvae/tvae-m6-20260429_031938/gen_20260429_032133.log new file mode 100644 index 0000000000000000000000000000000000000000..1786b469ae95cf0b3571826ec266454743ba4f48 --- /dev/null +++ b/timecost/m6/tvae/tvae-m6-20260429_031938/gen_20260429_032133.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d80e04f06bf6bdcbec201e9b4b89c27e26de949b4882aff0b9b4113300fab1f6 +size 404 diff --git a/timecost/m6/tvae/tvae-m6-20260429_031938/input_snapshot.json b/timecost/m6/tvae/tvae-m6-20260429_031938/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..9c9d287628147605b63b7b41f25188b9a5501d2a --- /dev/null +++ b/timecost/m6/tvae/tvae-m6-20260429_031938/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json", + "exists": true, + "size": 8990, + "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90" + } + } +} \ No newline at end of file diff --git a/timecost/m6/tvae/tvae-m6-20260429_031938/models_300epochs/train_20260429_031938.log b/timecost/m6/tvae/tvae-m6-20260429_031938/models_300epochs/train_20260429_031938.log new file mode 100644 index 0000000000000000000000000000000000000000..7166f142765e24631ef4b6021957a676efb5b04e --- /dev/null +++ b/timecost/m6/tvae/tvae-m6-20260429_031938/models_300epochs/train_20260429_031938.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39b224b2563292b02cc5b0938648776beaa3d9f12ce923c2c55d7880dba8e6b6 +size 533 diff --git a/timecost/m6/tvae/tvae-m6-20260429_031938/models_300epochs/tvae_300epochs.pt b/timecost/m6/tvae/tvae-m6-20260429_031938/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..b0009fa7690ff08681bbc4ee985630fed5f82eb8 --- /dev/null +++ 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m6/tvae/tvae-m6-20260429_031938/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv b/timecost/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv new file mode 100644 index 0000000000000000000000000000000000000000..286865aca5dd43f1bae88892d6eb3a35174d6b70 --- /dev/null +++ b/timecost/m6/tvae/tvae-m6-20260429_031938/tvae-m6-9864-20260429_032133.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:42f4087e20eadff632116bbb1279355449c382151938523899c903ef3655cac6 +size 1849329 diff --git a/timecost/m6/tvae/tvae-m6-20260429_031938/tvae_metadata.json b/timecost/m6/tvae/tvae-m6-20260429_031938/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bd02cd3078ddd1c4e6c63a87889218d62f25b7c8 --- /dev/null +++ b/timecost/m6/tvae/tvae-m6-20260429_031938/tvae_metadata.json @@ -0,0 +1,76 @@ +{ + "columns": [ + { + "name": "Administrative", + "type": "continuous" + }, + { + "name": "Administrative_Duration", + "type": "continuous" + }, + { + "name": "Informational", + "type": "continuous" + }, + { + "name": "Informational_Duration", + "type": "continuous" + }, + { + "name": "ProductRelated", + "type": "continuous" + }, + { + "name": "ProductRelated_Duration", + "type": "continuous" + }, + { + "name": "BounceRates", + "type": "continuous" + }, + { + "name": "ExitRates", + "type": "continuous" + }, + { + "name": "PageValues", + "type": "continuous" + }, + { + "name": "SpecialDay", + "type": "continuous" + }, + { + "name": "Month", + "type": "categorical" + }, + { + "name": "OperatingSystems", + "type": "continuous" + }, + { + "name": "Browser", + "type": "continuous" + }, + { + "name": "Region", + "type": "continuous" + }, + { + "name": "TrafficType", + "type": "continuous" + }, + { + "name": "VisitorType", + "type": "categorical" + }, + { + "name": "Weekend", + "type": "categorical" + }, + { + "name": "Revenue", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/m8/arf/arf-m8-20260502_160718/_arf_generate.py b/timecost/m8/arf/arf-m8-20260502_160718/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d2585456a1fd57d88b1203dd7edd152f65a6ee9 --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(36168) +c_csv = "/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm8' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv") diff --git a/timecost/m8/arf/arf-m8-20260502_160718/_arf_train.py b/timecost/m8/arf/arf-m8-20260502_160718/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c37ba4da79f6dbc5a26d82db3c3dd58b6d168dc9 --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf_model.pkl") diff --git a/timecost/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv b/timecost/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv new file mode 100644 index 0000000000000000000000000000000000000000..3f8cee5fe8d1730184388d1ef18d4bb144cabf16 --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba443b107a71b5faa2d7231d61078f719b9b9d1a399531e7f7c648d886139ff1 +size 6040860 diff --git a/timecost/m8/arf/arf-m8-20260502_160718/arf_model.pkl b/timecost/m8/arf/arf-m8-20260502_160718/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..19b6f0fcca1c8aef257d8c7596c6b0c9bf236081 --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8242a5bf5f6e3d6ebaa2cd60d719b19cb7c480da867300fb8a50f13018a0ef7a +size 170779501 diff --git a/timecost/m8/arf/arf-m8-20260502_160718/gen_20260502_160912.log b/timecost/m8/arf/arf-m8-20260502_160718/gen_20260502_160912.log new file mode 100644 index 0000000000000000000000000000000000000000..21ff0cd7b36248b85489bd71a16902ece0695c6f --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/gen_20260502_160912.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d09af1cac5fbd3b9e677fa7fd88f0c0da7f9daa4d65001f9d074137d60ccb56e +size 3567 diff --git a/timecost/m8/arf/arf-m8-20260502_160718/input_snapshot.json b/timecost/m8/arf/arf-m8-20260502_160718/input_snapshot.json new file mode 100644 index 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"binary", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m8/arf/arf-m8-20260502_160718/staged/public/test.csv b/timecost/m8/arf/arf-m8-20260502_160718/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1c4866e541b88f8bcd3b88381c7e56d4df06443 --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310 +size 370991 diff --git a/timecost/m8/arf/arf-m8-20260502_160718/staged/public/train.csv b/timecost/m8/arf/arf-m8-20260502_160718/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd7d8791840b6811b098820db4ed9e74cf5d627a --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833 +size 2964802 diff --git a/timecost/m8/arf/arf-m8-20260502_160718/staged/public/val.csv b/timecost/m8/arf/arf-m8-20260502_160718/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..09f0041420455c56eb1f79c62940d135e8bc6ea3 --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525 +size 370535 diff --git a/timecost/m8/arf/arf-m8-20260502_160718/train_20260502_160718.log b/timecost/m8/arf/arf-m8-20260502_160718/train_20260502_160718.log new file mode 100644 index 0000000000000000000000000000000000000000..008efedaf40e83c263d2ea0582f2ffcd5f857136 --- /dev/null +++ b/timecost/m8/arf/arf-m8-20260502_160718/train_20260502_160718.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0ef062957d75d8a0ed6fa7ada811780c5a2d77ef3d7a6e054db4341c85e241f +size 498 diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_generate.py b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d7f6e475d0830b8fda888c7d90c82188cf78950b --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(36168) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet-m8-36168-20260502_161000.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet-m8-36168-20260502_161000.csv") diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_train.py b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f6a114133c832da5a4dcbf2debd6b836bd01186b --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl") diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet-m8-36168-20260502_161000.csv b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet-m8-36168-20260502_161000.csv new file mode 100644 index 0000000000000000000000000000000000000000..edbe8342638545db20fd28c0caeb03c095933049 --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet-m8-36168-20260502_161000.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1a92aef9a4b8cf1a1ac5bb91aefa9624cebcab08d6eeded6ed9da65a4885d608 +size 6906618 diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_coltypes.json b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..452c32e6e0fbb315c4802d60a82ba93edd250e8b --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_coltypes.json @@ -0,0 +1,73 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "job", + "type": "categorical" + }, + { + "name": "marital", + "type": "categorical" + }, + { + "name": "education", + "type": "categorical" + }, + { + "name": "default", + "type": "categorical" + }, + { + "name": "balance", + "type": "continuous" + }, + { + "name": "housing", + "type": "categorical" + }, + { + "name": "loan", + "type": "categorical" + }, + { + "name": "contact", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "categorical" + }, + { + "name": "duration", + "type": "continuous" + }, + { + "name": "campaign", + "type": "continuous" + }, + { + "name": "pdays", + "type": "continuous" + }, + { + "name": "previous", + "type": "continuous" + }, + { + "name": "poutcome", + "type": "categorical" + }, + { + "name": "y", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7c1e102b8d4aa09b19f3e38853f2a47850ac4235 --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7488697f7835be919cb0a9525b58d8cf8f1dcc8efd4f06b6cb3fcc989aed6fc4 +size 17119 diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/const_cols.json b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/gen_20260502_161000.log b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/gen_20260502_161000.log new file mode 100644 index 0000000000000000000000000000000000000000..97b33058e8180fee9ae92003bb581748f586e269 --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/gen_20260502_161000.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1a932dda821e4157b7ad56de0f1527ed6ca3a7bac9055a5531bfed13507c42d +size 3665 diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/input_snapshot.json b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8b9057d11fa60b3e081565eae17affd688aa1a1d --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m8", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "exists": true, + "size": 2964802, + "sha256": "f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "exists": true, + "size": 370535, + "sha256": "5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-test.csv", + "exists": true, + "size": 370991, + "sha256": "6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m8/m8-dataset_profile.json", + "exists": true, + "size": 6553, + "sha256": "44f883858641584035a0a8859cb95dbcd3a023c03cbc76931aadfc4c70ef871f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m8/m8-dataset_contract_v1.json", + "exists": true, + "size": 8214, + "sha256": "e76df134780ec9b6c6c625a54e5d0c1935e9f4a7d09320ad19279a0492438d92" + } + } +} \ No newline at end of file diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/public_gate/normalized_schema_snapshot.json b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5e616edd2b7924c5e07f02ccbbb842529655f52a --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,346 @@ +{ + "dataset_id": "m8", + "target_column": "y", + "task_type": "classification", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 76, + "unique_ratio": 0.002101, + "example_values": [ + "40", + "52", + "31", + "51", + "44" + ] + } + }, + { + "name": "job", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 12, + "unique_ratio": 0.000332, + "example_values": [ + "admin.", + "technician", + "entrepreneur", + "blue-collar", + "services" + ] + } + }, + { + "name": "marital", + "role": "feature", + "semantic_type": 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b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/staged_features.json @@ -0,0 +1,87 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "job", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "marital", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "education", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "default", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "balance", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "loan", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "contact", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "day", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "campaign", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pdays", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "previous", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "poutcome", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "y", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/test.csv b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1c4866e541b88f8bcd3b88381c7e56d4df06443 --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310 +size 370991 diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/train.csv b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd7d8791840b6811b098820db4ed9e74cf5d627a --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833 +size 2964802 diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/val.csv b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..09f0041420455c56eb1f79c62940d135e8bc6ea3 --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525 +size 370535 diff --git a/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/train_20260502_160947.log b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/train_20260502_160947.log new file mode 100644 index 0000000000000000000000000000000000000000..ee89073abfbb25577f1e96948d7e68ea61d7c843 --- /dev/null +++ b/timecost/m8/bayesnet/bayesnet-m8-20260502_160947/train_20260502_160947.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79c8b4760cbfb5fcc21f6af595b51b78fba1a0989b80868b61d5d1f7f1cfe041 +size 3805 diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/_ctgan_generate.py b/timecost/m8/ctgan/ctgan-m8-20260501_011445/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c88a1da8b3ab3d318a694cb7f7fba311ffc058ab --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/models_300epochs/ctgan_300epochs.pt") +total = 36168 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/ctgan-m8-36168-20260501_012600.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/ctgan-m8-36168-20260501_012600.csv") \ No newline at end of file diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/ctgan-m8-36168-20260501_012600.csv b/timecost/m8/ctgan/ctgan-m8-20260501_011445/ctgan-m8-36168-20260501_012600.csv new file mode 100644 index 0000000000000000000000000000000000000000..1b8941697f0cc6516d9057e2fd0f5762568fe4b3 --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/ctgan-m8-36168-20260501_012600.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:affed8d8e31123fc53bcb1c1aa6ae3f61e63242e1afe98a8e75e50205da8e05f +size 2931388 diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/ctgan_metadata.json b/timecost/m8/ctgan/ctgan-m8-20260501_011445/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e4311b6e09b701b44e2d352b6d6f50ef5702793a --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/ctgan_metadata.json @@ -0,0 +1,72 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "job", + "type": "categorical" + }, + { + "name": "marital", + "type": "categorical" + }, + { + "name": "education", + "type": "categorical" + }, + { + "name": "default", + "type": "categorical" + }, + { + "name": "balance", + "type": "continuous" + }, + { + "name": "housing", + "type": "categorical" + }, + { + "name": "loan", + "type": "categorical" + }, + { + "name": "contact", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "categorical" + }, + { + "name": "duration", + "type": "continuous" + }, + { + "name": "campaign", + "type": "continuous" + }, + { + "name": "pdays", + "type": "continuous" + }, + { + "name": "previous", + "type": "continuous" + }, + { + "name": "poutcome", + "type": "categorical" + }, + { + "name": "y", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/gen_20260501_012600.log b/timecost/m8/ctgan/ctgan-m8-20260501_011445/gen_20260501_012600.log new file mode 100644 index 0000000000000000000000000000000000000000..b800177fd6813194b3c8fb58d380596a1aaa7210 --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/gen_20260501_012600.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ccb815d03d2b0ebe16d9efdbeb6e8eab509ea50d98d25d98556b037cd198ba59 +size 562 diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/input_snapshot.json b/timecost/m8/ctgan/ctgan-m8-20260501_011445/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..4d68c64fa62a61ed4f7d248d9cde8b58f20aca12 --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m8", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "exists": true, + "size": 2964802, + "sha256": "f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833" + }, + "val_csv": { + "path": 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a/timecost/m8/ctgan/ctgan-m8-20260501_011445/models_300epochs/ctgan_300epochs.pt b/timecost/m8/ctgan/ctgan-m8-20260501_011445/models_300epochs/ctgan_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..7bb7fb7864264183865fba6dc459901fda3c0fd0 --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/models_300epochs/ctgan_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b9e4e9a0f8e3c300c849a62387a02e3b2406c8ec6f63b4f19997a1ea06eb840 +size 4538659 diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/models_300epochs/train_20260501_011446.log b/timecost/m8/ctgan/ctgan-m8-20260501_011445/models_300epochs/train_20260501_011446.log new file mode 100644 index 0000000000000000000000000000000000000000..59bd3784eda2565385c7371e4efbd241205e716f --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/models_300epochs/train_20260501_011446.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/staged_features.json b/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..11323e8dc5c8a2eaafa8e404e60d3db7e244452a --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/staged_features.json @@ -0,0 +1,87 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "job", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "marital", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "education", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "default", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "balance", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "loan", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "contact", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "day", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "campaign", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pdays", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "previous", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "poutcome", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "y", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/test.csv b/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1c4866e541b88f8bcd3b88381c7e56d4df06443 --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310 +size 370991 diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/train.csv b/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd7d8791840b6811b098820db4ed9e74cf5d627a --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833 +size 2964802 diff --git a/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/val.csv b/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..09f0041420455c56eb1f79c62940d135e8bc6ea3 --- /dev/null +++ b/timecost/m8/ctgan/ctgan-m8-20260501_011445/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525 +size 370535 diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_X_host.npy b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..edc15f1ab2ac289d7b316a7eb8e41a86a10c1f7a --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19689a29b7db49f458dde36a8be7e74ee096d0134dfc6d01cb0425e80ac18ad2 +size 69760 diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_gen.py b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..4f7a9824d9a790b6a76c5ed76527489ddf327fa7 --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(36168)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv', index=False) +print("saved", len(df)) diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..0eaf90aa76960829366f6a0c7bf34e8ee4b6ed8a --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["age", "job", "marital", "education", "default", "balance", "housing", "loan", "contact", "day", "month", "duration", "campaign", "pdays", "previous", "poutcome", "y"], "cat_indexes": [1, 2, 3, 4, 6, 7, 8, 10, 15]} \ No newline at end of file diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_train.py b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a7bf1ad5b84180a21592bbaa81da0b6384970bfe --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=2 " + f"n_estimators=1 duplicate_K=1 n_jobs=1 " + f"max_depth=2 xgb_verbosity=0 xgb_nthread=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=2, n_estimators=1, duplicate_K=1, n_jobs=1, + model="xgboost", max_depth=2, tree_method="hist", cat_indexes=cat_indexes, + verbosity=0, nthread=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib') diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv new file mode 100644 index 0000000000000000000000000000000000000000..1e30c1ec44403df4e6f7b3d95b12be578a14840b --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf142eb2539fa3e7d6597b66b92d4432dcfd752ec97da6ee2731901a5d941258 +size 4953970 diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..cacc8913290a2169f9c66335d845f87e70bee969 --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8e8d50cb17cc21e98475209d4e80427db8a56fcd28ed90a24382abfea909ad7 +size 443637 diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/gen_20260506_045105.log b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/gen_20260506_045105.log new file mode 100644 index 0000000000000000000000000000000000000000..066b3954bc03c682a65697365ab33fcbc7b85d37 --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/gen_20260506_045105.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b80ba12b25116c990e84eae2654b82c6c5771e846b5a71d7cacdfe498b657cfa +size 295 diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/input_snapshot.json b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8ba0d11191c9dcbcf06fc13256cf789d71452b89 --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m8", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "exists": true, + "size": 2964802, + "sha256": "f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "exists": true, + "size": 370535, + "sha256": "5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525" + }, + "test_csv": { + "path": 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0000000000000000000000000000000000000000..cacc8913290a2169f9c66335d845f87e70bee969 --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8e8d50cb17cc21e98475209d4e80427db8a56fcd28ed90a24382abfea909ad7 +size 443637 diff --git a/timecost/m8/forestdiffusion/forest-m8-20260506_045103/public_gate/normalized_schema_snapshot.json b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5e616edd2b7924c5e07f02ccbbb842529655f52a --- /dev/null +++ b/timecost/m8/forestdiffusion/forest-m8-20260506_045103/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,346 @@ +{ + "dataset_id": "m8", + "target_column": "y", + "task_type": "classification", + "columns": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 76, + "unique_ratio": 0.002101, + "example_values": [ + "40", + "52", + "31", + "51", + "44" + ] + } + }, + { + "name": "job", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 12, + "unique_ratio": 0.000332, + "example_values": [ + "admin.", + "technician", + "entrepreneur", + "blue-collar", + "services" + ] + } + }, + { + "name": "marital", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 8.3e-05, + "example_values": [ + "single", + "married", + "divorced" + ] + } + }, + { + "name": "education", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000111, + "example_values": [ + "secondary", + "tertiary", + "primary", + "unknown" + ] + } + }, + { + "name": "default", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 5.5e-05, + "example_values": [ + "no", + "yes" + ] + } + }, + { + "name": "balance", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6604, + "unique_ratio": 0.182592, + "example_values": [ + "419", + "31", + "7567", + "315", + "737" + ] + } + }, + { + "name": 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--git a/timecost/m8/realtabformer/rtf-m8-20260501_010220/train_20260501_010220.log b/timecost/m8/realtabformer/rtf-m8-20260501_010220/train_20260501_010220.log new file mode 100644 index 0000000000000000000000000000000000000000..6ab6b59905a502408838f57cdd0b254b2fa9193b --- /dev/null +++ b/timecost/m8/realtabformer/rtf-m8-20260501_010220/train_20260501_010220.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0427314165c5bae27586175d44179266ad56e7e28645b17f64e35dea3a1cc580 +size 4395012 diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_gen.py b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..316d01cb18e4758a8b93290e2a38fae0f79ca8a9 --- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_012923/tabular_bundle/pipeline_m8" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m8/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(36168)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_012923/tabbyflow-m8-36168-20260501_014108.csv") diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_train.py b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..53dd60fba1a50953b40ceef15d6023e266f8dc3f --- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_012923/tabular_bundle/pipeline_m8" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/gen_20260501_014108.log b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/gen_20260501_014108.log new file mode 100644 index 0000000000000000000000000000000000000000..b7fcf91837f2bae294a952a1c85a4b1f6ed0a9b8 --- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/gen_20260501_014108.log @@ -0,0 +1,3 @@ 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--- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/tabular_bundle/pipeline_m8/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/train_20260501_012924.log b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/train_20260501_012924.log new file mode 100644 index 0000000000000000000000000000000000000000..a08b04e0ea0996790305ff3c2031cc9b614d31a8 --- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_012923/train_20260501_012924.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ec8d3e122ce392c7b91f99d55bdb1fd29b29ac29fed015559f9f7317940f608 +size 1062721 diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_gen.py b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..b7666c6ae07cf2a6cade6eb1938ca3d36ea06c79 --- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m8/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(36168)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabbyflow-m8-36168-20260501_035503.csv") diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_train.py b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3433fc95ca11c236c7b7cf5d6ccaed92f627c183 --- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/gen_20260501_035503.log 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sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8/y_val.npy b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..6de42d9b9d2e98ac13fbeb62391fe185ffdd6210 --- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/tabular_bundle/pipeline_m8/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/train_20260501_034537.log b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/train_20260501_034537.log new file mode 100644 index 0000000000000000000000000000000000000000..1a54e09fb8b828056967a234a466af6801368206 --- /dev/null +++ b/timecost/m8/tabbyflow/tabbyflow-m8-20260501_034536/train_20260501_034537.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:91d5fc185b9bcd99cf2c5a0cd9dd917fb0374178061eed8797dcb047400ec818 +size 1005474 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_sample_r0.py b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..1bd717269bf6985a22ab81027f21e9239655f40a --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 36168 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/config_sample_20260501_000111_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/tabddpm-m8-36168-20260501_000111.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/tabddpm-m8-36168-20260501_000111.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_train.py b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5b98669dc3848f2c9d39f3862ac6d02a94a19806 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..cc2a001fa80e7432aef0b648366e77c1f07445e4 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/data" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/config_sample_20260501_000111_r0.toml b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/config_sample_20260501_000111_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..fd6486b26d126844611145f94060a2a9200bebf8 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/config_sample_20260501_000111_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_000003/data" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 36168 +batch_size = 1000 +seed = 0 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/X_cat_train.npy b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..593b28d49c204d0e1075baeb8750f38436a36e50 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7a7263704577346e726c9bbb028fcff4c26bb80a5b0bb53ba51f837bb032fc3 +size 2604224 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/X_num_train.npy b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..07e2a13424a0435006d25dfc6527e9c7d0d87f40 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae3f0ae4e117a2087b337ccec94a15b29288bb82a257e7e365f10bae858de8ec +size 1012832 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/info.json b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0c2916af96017606ff0f750eacc05dd5fa103ae0 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_000003/data/info.json @@ -0,0 +1,50 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 7, + "n_cat_features": 9, + "train_size": 36168, + "num_col_idx": [ + 0, + 1, + 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b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..4c0174996b0c9b9cca57dc568b6502824dd0d4bb --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 36168 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/config_sample_20260501_035632_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/tabddpm-m8-36168-20260501_035632.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/tabddpm-m8-36168-20260501_035632.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_train.py b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c2a062abf0c95d17cc50cffc19e3299faaa292db --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..592394c25eb16729219034995a9cf4362670d247 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/data" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/config_sample_20260501_035632_r0.toml b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/config_sample_20260501_035632_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..05ae1029cd98311b38fb41a47d8e80a280f9f1ed --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/config_sample_20260501_035632_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/data" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 36168 +batch_size = 1000 +seed = 0 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/X_cat_train.npy b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..593b28d49c204d0e1075baeb8750f38436a36e50 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7a7263704577346e726c9bbb028fcff4c26bb80a5b0bb53ba51f837bb032fc3 +size 2604224 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/X_num_train.npy b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..07e2a13424a0435006d25dfc6527e9c7d0d87f40 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae3f0ae4e117a2087b337ccec94a15b29288bb82a257e7e365f10bae858de8ec +size 1012832 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/info.json b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..0c2916af96017606ff0f750eacc05dd5fa103ae0 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/info.json @@ -0,0 +1,50 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 7, + "n_cat_features": 9, + "train_size": 36168, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6 + ], + "cat_col_idx": [ + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15 + ], + "target_col_idx": [ + 16 + ], + "column_names": [ + "age", + "balance", + "day", + "duration", + "campaign", + "pdays", + "previous", + "job", + "marital", + "education", + "default", + "housing", + "loan", + "contact", + "month", + "poutcome", + "y" + ], + "num_classes": 2 +} \ No newline at end of file diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/y_train.npy b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6de42d9b9d2e98ac13fbeb62391fe185ffdd6210 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/gen_20260501_035632_r0.log b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/gen_20260501_035632_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..99bdf188650b024b979c7b3053609643602911a8 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/gen_20260501_035632_r0.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39f96985219441faea18522fce7ec5eda5331e02bc674cb6855f68350d88e0ae +size 777507 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/input_snapshot.json b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..eabdf74e07206e255d84fe9f8f9499d5948c5373 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m8", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "exists": true, + "size": 2964802, + "sha256": "f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "exists": true, + "size": 370535, + "sha256": "5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525" + }, + "test_csv": { + "path": 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0000000000000000000000000000000000000000..c9c55287cf0b79a010654724b9dc5348b78bfc69 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a8b8ec8162a6587447114e257972d04200523cd6221d40c5731488d9d3163d73 +size 651959 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/output/X_cat_unnorm.npy b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..635cd0b378a65e7f251464fd6d73cc1f7c724414 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fb91c104e69e662e8cff10a7f10fc0c94854dc6428113bbdcd25255fa54ca721 +size 2604224 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/output/X_num_train.npy b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/output/X_num_train.npy new file mode 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file mode 100644 index 0000000000000000000000000000000000000000..05ae1029cd98311b38fb41a47d8e80a280f9f1ed --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/data" +model_type = "mlp" +num_numerical_features = 7 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 36168 +batch_size 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"binary", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/staged/public/test.csv b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1c4866e541b88f8bcd3b88381c7e56d4df06443 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310 +size 370991 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/staged/public/train.csv b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd7d8791840b6811b098820db4ed9e74cf5d627a --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabddpm/tabddpm-m8-20260501_035531/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/tabddpm-m8-36168-20260501_035632.csv b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/tabddpm-m8-36168-20260501_035632.csv new file mode 100644 index 0000000000000000000000000000000000000000..12bf7f4ecf4e17e3db4d9f60bb405a3a6d64f0ac --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/tabddpm-m8-36168-20260501_035632.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d8775c053ec70d2e26ca6814c110d9a91eb180246f2420a51e84780f0bd2c5a2 +size 3449494 diff --git a/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/train_20260501_035532.log b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/train_20260501_035532.log new file mode 100644 index 0000000000000000000000000000000000000000..ea6bc30d04054f854d50203409ed863cdf37a648 --- /dev/null +++ b/timecost/m8/tabddpm/tabddpm-m8-20260501_035531/train_20260501_035532.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e077e14a66395c6d1d7c9dfc49530844988502ff1867f11c0e1ec4091a7bfa2 +size 1158 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_gen.py b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..1836e1538bd9e65918bbc093b04185589df1f4b4 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_010036/tabular_bundle/pipeline_m8" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m8/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(36168)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_010036/tabdiff-m8-36168-20260501_011423.csv") diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_train.py b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..508e8e7e8b3bd48863c0b417c32ffe35a3748e93 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_010036/tabular_bundle/pipeline_m8" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/gen_20260501_011423.log b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/gen_20260501_011423.log new file mode 100644 index 0000000000000000000000000000000000000000..928e31cd242f18b98a01584a254e965b3e727e85 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/gen_20260501_011423.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c750198858df23f4b23371a37ee3bb1eab3e59c7f7db237a06f85f863e736e53 +size 9620 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/input_snapshot.json b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e95698a9aba414b9dc6c3c8093e5c9611db69565 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m8", + "model": "tabdiff", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "exists": true, + "size": 2964802, + "sha256": "f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "exists": true, + "size": 370535, + "sha256": "5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-test.csv", + "exists": true, + "size": 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b/timecost/m8/tabdiff/tabdiff-m8-20260501_010036/train_20260501_010037.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6725e0d8a189e38fcae9a0944c90d93381668aac8a507352115ce526fff6a314 +size 1094773 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_gen.py b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..03ae207e8fe16b4dea0c42509f67b0e1f9d1c89d --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m8/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(36168)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_035904/tabdiff-m8-36168-20260501_040953.csv") diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_train.py b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ed0bf514e6bb98c8a5c9b95c11bba4e13150eb22 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m8" +src = r"/work/output-Benchmark-trainonly-v1/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/gen_20260501_040953.log b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/gen_20260501_040953.log new file mode 100644 index 0000000000000000000000000000000000000000..d11024e9b42a2de1e6318684aca20f5c09e02700 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/gen_20260501_040953.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bbec1f7cf441711463762d9e69c5edd6ad21954f5eab830d543e58e617313f0e +size 7904 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/input_snapshot.json 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}, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m8/m8-dataset_profile.json", + "exists": true, + "size": 6553, + "sha256": "44f883858641584035a0a8859cb95dbcd3a023c03cbc76931aadfc4c70ef871f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m8/m8-dataset_contract_v1.json", + "exists": true, + "size": 8214, + "sha256": "e76df134780ec9b6c6c625a54e5d0c1935e9f4a7d09320ad19279a0492438d92" + } + } +} \ No newline at end of file diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/models_tabdiff/trained.pt b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/models_tabdiff/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc319a55a9c7152a1137f968b6fa1c735125ad11 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/models_tabdiff/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "y", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/public_gate/staged_input_manifest.json b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/public_gate/staged_input_manifest.json new file mode 100644 index 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/dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/X_num_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae3f0ae4e117a2087b337ccec94a15b29288bb82a257e7e365f10bae858de8ec +size 1012832 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/info.json b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/info.json new file mode 100644 index 0000000000000000000000000000000000000000..779aa13573a20a8c073a142023a2900dfbcade7c --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/info.json @@ -0,0 +1,176 @@ +{ + "name": "pipeline_m8", + "task_type": "binclass", + "n_num_features": 7, + "n_cat_features": 9, + "train_size": 36168, + "test_num": 36168, + "val_num": 36168, + "train_num": 36168, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6 + ], + "cat_col_idx": [ + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15 + ], + "target_col_idx": [ + 16 + ], + "column_names": [ + "age", + "balance", + "day", + "duration", + "campaign", + "pdays", + "previous", + "job", + "marital", + "education", + "default", + "housing", + "loan", + "contact", + "month", + "poutcome", + "y" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "2": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "3": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "4": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "5": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "6": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "7": { + "sdtype": "categorical" + }, + "8": { + "sdtype": "categorical" + }, + "9": { + "sdtype": "categorical" + }, + "10": { + "sdtype": "categorical" + }, + "11": { + "sdtype": "categorical" + }, + "12": { + "sdtype": "categorical" + }, + "13": { + "sdtype": "categorical" + }, + "14": { + "sdtype": "categorical" + }, + "15": { + "sdtype": "categorical" + }, + "16": { + "sdtype": "categorical" + } + } + }, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6, + "7": 7, + "8": 8, + "9": 9, + "10": 10, + "11": 11, + "12": 12, + "13": 13, + "14": 14, + "15": 15, + "16": 16 + }, + "inverse_idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6, + "7": 7, + "8": 8, + "9": 9, + "10": 10, + "11": 11, + "12": 12, + "13": 13, + "14": 14, + "15": 15, + "16": 16 + }, + "idx_name_mapping": { + "0": "age", + "1": "balance", + "2": "day", + "3": "duration", + "4": "campaign", + "5": "pdays", + "6": "previous", + "7": "job", + "8": "marital", + "9": "education", + "10": "default", + "11": "housing", + "12": "loan", + "13": "contact", + "14": "month", + "15": "poutcome", + "16": "y" + }, + "n_classes": 2 +} \ No newline at end of file diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/real.csv b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..89f045df89f474d0b98875260fa3dadcbead70a1 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c901e8058d778b30427b02e99359688644f7ea1cad9d3d04a55fdf3881158d60 +size 1517968 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/test.csv b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/test.csv new file mode 100644 index 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b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6de42d9b9d2e98ac13fbeb62391fe185ffdd6210 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_train.npy b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6de42d9b9d2e98ac13fbeb62391fe185ffdd6210 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_val.npy b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..6de42d9b9d2e98ac13fbeb62391fe185ffdd6210 --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/tabular_bundle/pipeline_m8/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3fe448bbe15f46b1714e7d0ebca94f3df952a9ef5364159dda69ccefcdce02c6 +size 289472 diff --git a/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/train_20260501_035905.log b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/train_20260501_035905.log new file mode 100644 index 0000000000000000000000000000000000000000..0cb7247bfb789e3deef6aab7198b1770d6b42cfc --- /dev/null +++ b/timecost/m8/tabdiff/tabdiff-m8-20260501_035904/train_20260501_035905.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bc2d3ec932d73ac9b7bf6f6c42dddda146dc6291ab972c1ef9c1d9904b3c0d25 +size 1075205 diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/_tabpfgen_generate.py b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..dff1a68b0f83f3aba8492758878992e74e83ef9d --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/train.csv") +target_col = "y" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(36168) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen-m8-36168-20260501_012611.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> 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+size 370991 diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/train.csv b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd7d8791840b6811b098820db4ed9e74cf5d627a --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833 +size 2964802 diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/val.csv b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..09f0041420455c56eb1f79c62940d135e8bc6ea3 --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525 +size 370535 diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/adapter_report.json b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..a735d8c3423ec3540b065e0f659e7b22a43b8129 --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/adapter_transforms_applied.json b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/model_input_manifest.json b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3fbbe2034a0553b34767ff698b8d8fc13d9dcfde --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,353 @@ +{ + "dataset_id": "m8", + "model": "tabpfgen", + "target_column": "y", + "task_type": "classification", + "column_schema": [ + { + "name": "age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 76, + "unique_ratio": 0.002101, + "example_values": [ + "40", + "52", + "31", + "51", + "44" + ] + } + }, + { + "name": "job", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 12, + "unique_ratio": 0.000332, + "example_values": [ + "admin.", + "technician", + "entrepreneur", + "blue-collar", + "services" + ] + } + }, + { + "name": "marital", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 8.3e-05, + "example_values": [ + "single", + "married", + "divorced" + ] + } + }, + { + "name": "education", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000111, + "example_values": [ + "secondary", + "tertiary", + "primary", + "unknown" + ] + } + }, + { + "name": "default", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 5.5e-05, + "example_values": [ + "no", + "yes" + ] + } + }, + { + "name": "balance", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6604, + "unique_ratio": 0.182592, + "example_values": [ + "419", + "31", + "7567", + "315", + "737" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 5.5e-05, + "example_values": [ + "no", + "yes" + ] + } + }, + { + "name": "loan", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 5.5e-05, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "contact", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 8.3e-05, + "example_values": [ + "cellular", + "unknown", + "telephone" + ] + } + }, + { + "name": "day", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 31, + "unique_ratio": 0.000857, + "example_values": [ + "28", + "7", + "11", + "12", + "14" + ] + } + }, + { + "name": "month", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 12, + "unique_ratio": 0.000332, + "example_values": [ + "jul", + "may", + "aug", + "oct", + "feb" + ] + } + }, + { + "name": "duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1507, + "unique_ratio": 0.041667, + "example_values": [ + "100", + "120", + "70", + "291", + "102" + ] + } + }, + { + "name": "campaign", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.001299, + "example_values": [ + "16", + "1", + "2", + "5", + "4" + ] + } + }, + { + "name": "pdays", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 524, + "unique_ratio": 0.014488, + "example_values": [ + "-1", + "91", + "365", + "189", + "117" + ] + } + }, + { + "name": "previous", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 38, + "unique_ratio": 0.001051, + "example_values": [ + "0", + "4", + "1", + "2", + "3" + ] + } + }, + { + "name": "poutcome", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000111, + "example_values": [ + "unknown", + "failure", + "other", + "success" + ] + } + }, + { + "name": "y", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 5.5e-05, + "example_values": [ + "no", + "yes" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen-m8-36168-20260501_012611.csv b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen-m8-36168-20260501_012611.csv new file mode 100644 index 0000000000000000000000000000000000000000..0a0b0e09dbb7f554c8873d5fe0036e06c5ccdda7 --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen-m8-36168-20260501_012611.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f172221760be02b18035f31f3467249ff5a3a13ebd395d865bc545e3ef3190af +size 4801373 diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen_meta.json b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..914c7266339fe818144f4654caf7ab243a09873b --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_012611/staged/public/staged_features.json", + "target_col": "y", + "is_classification": true, + "task_type": "classification", + "n_rows": 36168, + "n_cols": 17 +} \ No newline at end of file diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/train_20260501_012611.log b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/train_20260501_012611.log new file mode 100644 index 0000000000000000000000000000000000000000..88537154202a98ca178f9870e6e4d428164dc669 --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_012611/train_20260501_012611.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5927a1d9e97d405c0bf308d01bca24b77253fa8c84043d31cd0ca2d7c624af5 +size 597 diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/_tabpfgen_generate.py b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..65c854c132d9f6d26fbe156c838c0083a2072849 --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/public/train.csv") +target_col = "y" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(36168) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen-m8-36168-20260501_041015.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen-m8-36168-20260501_041015.csv") diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/gen_20260501_041015.log b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/gen_20260501_041015.log new file mode 100644 index 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b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/tabpfgen/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/tabpfgen/adapter_transforms_applied.json b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen-m8-36168-20260501_041015.csv b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen-m8-36168-20260501_041015.csv new file mode 100644 index 0000000000000000000000000000000000000000..de325374928fe0c690a7467d3ba43824dbec2c60 --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen-m8-36168-20260501_041015.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73544a68ce12d9a3954ea8187523cfb72ebfda7d909e0678bc9c594b6c1f249d +size 4800851 diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen_meta.json b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..5d2d274c29f58951749fc387b61f9ceae0b38c4f --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m8/tabpfgen/tabpfgen-m8-20260501_041014/staged/public/staged_features.json", + "target_col": "y", + "is_classification": true, + "task_type": "classification", + "n_rows": 36168, + "n_cols": 17 +} \ No newline at end of file diff --git a/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/train_20260501_041015.log b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/train_20260501_041015.log new file mode 100644 index 0000000000000000000000000000000000000000..75dfdf806a4337531097c304ca46843f3c2ef6f7 --- /dev/null +++ b/timecost/m8/tabpfgen/tabpfgen-m8-20260501_041014/train_20260501_041015.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adf60cf4bb06aa19e6e6234ca72b24e46110e57714d374ed37dff34ceb1a38d8 +size 595 diff --git a/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/_tabsyn_sample.py b/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..72470aa2de30fbca27af07b6ccf097b3a05f1aaa --- /dev/null +++ b/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m8/tabsyn/tabsyn-m8-20260501_041249" +dataname = "tabsyn_m8" +output_csv = "/work/output-Benchmark-trainonly-v1/m8/tabsyn/tabsyn-m8-20260501_041249/tabsyn-m8-36168-20260501_052934.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 36168 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/_tabsyn_train.py b/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1d9ba5dc060c64f0a613f74a493785f1ec730055 --- /dev/null +++ b/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/m8/tabsyn/tabsyn-m8-20260501_041249" +dataname = "tabsyn_m8" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/X_cat_test.npy b/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..dfaa4e2bc4a57a6eb51233a33b8a5fd7f2f93a4a --- /dev/null +++ b/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 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0000000000000000000000000000000000000000..2616afb916a485273206f1e68a181d57d1bdc4b0 --- /dev/null +++ b/timecost/m8/tabsyn/tabsyn-m8-20260501_041249/train_20260501_041250.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bca6471647633c03de726a570dd88454c7ca77e42c73cc12c9cc51c1cf6049eb +size 5116388 diff --git a/timecost/m8/tvae/tvae-m8-20260501_055847/_tvae_generate.py b/timecost/m8/tvae/tvae-m8-20260501_055847/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..f47b334efe1734d573bbf45d3fafae0128d146c6 --- /dev/null +++ b/timecost/m8/tvae/tvae-m8-20260501_055847/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/m8/tvae/tvae-m8-20260501_055847/models_300epochs/tvae_300epochs.pt") +total = 36168 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/m8/tvae/tvae-m8-20260501_055847/tvae-m8-36168-20260501_060307.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/m8/tvae/tvae-m8-20260501_055847/tvae-m8-36168-20260501_060307.csv") diff --git a/timecost/m8/tvae/tvae-m8-20260501_055847/_tvae_train.py b/timecost/m8/tvae/tvae-m8-20260501_055847/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..69eef92bbb44aaab7bb99b98a6d725b1f7f45645 --- /dev/null +++ b/timecost/m8/tvae/tvae-m8-20260501_055847/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/m8/tvae/tvae-m8-20260501_055847/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/m8/tvae/tvae-m8-20260501_055847/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/m8/tvae/tvae-m8-20260501_055847/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/m8/tvae/tvae-m8-20260501_055847/gen_20260501_060307.log b/timecost/m8/tvae/tvae-m8-20260501_055847/gen_20260501_060307.log new file mode 100644 index 0000000000000000000000000000000000000000..07b374bf268db958ab89fd4172f989abee2f50c8 --- /dev/null +++ b/timecost/m8/tvae/tvae-m8-20260501_055847/gen_20260501_060307.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:178a801c5e38643c3ac46d8b92c798424648b05e57d99eb37979e38c392b3331 +size 406 diff --git a/timecost/m8/tvae/tvae-m8-20260501_055847/input_snapshot.json b/timecost/m8/tvae/tvae-m8-20260501_055847/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..bcb7e1b12eb711001b34b13d667b25bea1fd4143 --- /dev/null +++ b/timecost/m8/tvae/tvae-m8-20260501_055847/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m8", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "exists": true, + "size": 2964802, + "sha256": "f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "exists": true, + "size": 370535, + "sha256": "5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-test.csv", + "exists": true, + "size": 370991, + "sha256": "6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m8/m8-dataset_profile.json", + "exists": true, + "size": 6553, + "sha256": "44f883858641584035a0a8859cb95dbcd3a023c03cbc76931aadfc4c70ef871f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m8/m8-dataset_contract_v1.json", + "exists": true, + "size": 8214, + "sha256": "e76df134780ec9b6c6c625a54e5d0c1935e9f4a7d09320ad19279a0492438d92" + } + } +} \ No newline at end of file diff --git a/timecost/m8/tvae/tvae-m8-20260501_055847/models_300epochs/train_20260501_055847.log b/timecost/m8/tvae/tvae-m8-20260501_055847/models_300epochs/train_20260501_055847.log new file mode 100644 index 0000000000000000000000000000000000000000..6133ad7d7ab48e6f6d2b2d7e4e87cbacf5b4b91c --- /dev/null +++ b/timecost/m8/tvae/tvae-m8-20260501_055847/models_300epochs/train_20260501_055847.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ab01e5b026033ca675c354883d94ba705e6ebdda5e7468552ed5c712e682803 +size 534 diff --git a/timecost/m8/tvae/tvae-m8-20260501_055847/models_300epochs/tvae_300epochs.pt b/timecost/m8/tvae/tvae-m8-20260501_055847/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c224eed937039daa1b52090a44f65b1ae9ffea59 --- /dev/null +++ b/timecost/m8/tvae/tvae-m8-20260501_055847/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40d98c4f727dab4f20ea419a7cf2e9a4803b1cf08526b24dab806ad1984d5afc +size 1252844 diff --git a/timecost/m8/tvae/tvae-m8-20260501_055847/public_gate/normalized_schema_snapshot.json 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"PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "y", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-test.csv" + } +} \ No newline at end of file diff --git a/timecost/m8/tvae/tvae-m8-20260501_055847/public_gate/staged_input_manifest.json b/timecost/m8/tvae/tvae-m8-20260501_055847/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c7b268a4769d9364444095855ca9f3ba8d57a54b --- /dev/null +++ b/timecost/m8/tvae/tvae-m8-20260501_055847/public_gate/staged_input_manifest.json @@ -0,0 +1,351 @@ +{ + "dataset_id": "m8", 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0000000000000000000000000000000000000000..2b1904fec133cb0f932f407a49bf7752a57cb918 --- /dev/null +++ b/timecost/n11/arf/arf-n11-20260502_020245/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(15215) +c_csv = "/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n11' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv") diff --git a/timecost/n11/arf/arf-n11-20260502_020245/_arf_train.py b/timecost/n11/arf/arf-n11-20260502_020245/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4ce16cabc4f6c1831eaa33eac8f957443c73ba48 --- /dev/null +++ b/timecost/n11/arf/arf-n11-20260502_020245/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n11/arf/arf-n11-20260502_020245/arf_model.pkl") diff --git a/timecost/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv b/timecost/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv new file mode 100644 index 0000000000000000000000000000000000000000..90463a037b8a46ff98d58427157c3414f92c7ac2 --- /dev/null +++ b/timecost/n11/arf/arf-n11-20260502_020245/arf-n11-15215-20260502_020453.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76d0cfea51042bc9cd99095c47750a816024ecbbcdb76202611fdd96da8693b9 +size 2889646 diff --git a/timecost/n11/arf/arf-n11-20260502_020245/arf_model.pkl b/timecost/n11/arf/arf-n11-20260502_020245/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..74e633e4811b68e0fa02873d37867f4bd607dfde --- /dev/null +++ b/timecost/n11/arf/arf-n11-20260502_020245/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bfc763f614d8a9f81206ff66aa4671d26df40569390d33dfa150637eb402e69d +size 74492762 diff --git 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b/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(15215) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet-n11-15215-20260502_020516.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet-n11-15215-20260502_020516.csv") diff --git a/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_train.py b/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e2b4dc1751fbd47c8b722ca403c82a24ad9c972d --- /dev/null +++ b/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_model.pkl") diff --git a/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet-n11-15215-20260502_020516.csv b/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet-n11-15215-20260502_020516.csv new file mode 100644 index 0000000000000000000000000000000000000000..024b5ad871eeb40512f65550fa9180aff4dd0791 --- /dev/null +++ b/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet-n11-15215-20260502_020516.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8189810af01e33b01a0d7559e3200509fb97faa34883804976557a7886f97e2f +size 2890271 diff --git a/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_coltypes.json b/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/bayesnet_coltypes.json new file mode 100644 index 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b/timecost/n11/bayesnet/bayesnet-n11-20260502_020508/train_20260502_020508.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44f315797a0a5bdcbc9f9bac9dbd4b6524fe22b7c1beaf97d8ebafa9868afea1 +size 3744 diff --git a/timecost/n11/ctgan/ctgan-n11-20260430_235732/_ctgan_generate.py b/timecost/n11/ctgan/ctgan-n11-20260430_235732/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..82700d451beb5cd941aac600c02778e846bfb546 --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260430_235732/models_300epochs/ctgan_300epochs.pt") +total = 15215 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv") \ No newline at end of file diff --git a/timecost/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv b/timecost/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv new file mode 100644 index 0000000000000000000000000000000000000000..498a2fac48e3bcf352aee174d135a3474271f6bc --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/ctgan-n11-15215-20260501_000111.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ac8fe60ab2beab737319060ed95f1d397e18cbadd382fb6552c7d6cb6e16f487 +size 2885629 diff --git a/timecost/n11/ctgan/ctgan-n11-20260430_235732/ctgan_metadata.json b/timecost/n11/ctgan/ctgan-n11-20260430_235732/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cb19699b85fe49965d86e54abd3c67a8ec6163a8 --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/ctgan_metadata.json @@ -0,0 +1,48 @@ +{ + "columns": [ + { + "name": "28.7967", + "type": "continuous" + }, + { + "name": "16.0021", + "type": "continuous" + }, + { + "name": "2.6449", + "type": "continuous" + }, + { + "name": "0.3918", + "type": "continuous" + }, + { + "name": "0.1982", + "type": "continuous" + }, + { + "name": "27.7004", + "type": "continuous" + }, + { + "name": "22.011", + "type": "continuous" + }, + { + "name": "-8.2027", + "type": "continuous" + }, + { + "name": "40.092", + "type": "continuous" + }, + { + "name": "81.8828", + "type": "continuous" + }, + { + "name": "g", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/n11/ctgan/ctgan-n11-20260430_235732/gen_20260501_000111.log b/timecost/n11/ctgan/ctgan-n11-20260430_235732/gen_20260501_000111.log new file mode 100644 index 0000000000000000000000000000000000000000..c2e4de1b169963f41dee908af3b0f6ad710b14a0 --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/gen_20260501_000111.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:008febb552773f18eee9bca44dd9c372995b1d303111f6dd8777bb5fe92c68a3 +size 565 diff --git a/timecost/n11/ctgan/ctgan-n11-20260430_235732/input_snapshot.json b/timecost/n11/ctgan/ctgan-n11-20260430_235732/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..d0df46d6279ceed752adf467715dbe95c309efe1 --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n11", + "model": "ctgan", + "inputs": { + "train_csv": { 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index 0000000000000000000000000000000000000000..bdb9c28b9d52f06368851a08caadc305035fc1b7 --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/staged_features.json @@ -0,0 +1,57 @@ +[ + { + "feature_name": "28.7967", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "16.0021", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "2.6449", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "0.3918", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "0.1982", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "27.7004", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "22.011", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "-8.2027", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "40.092", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "81.8828", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "g", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/test.csv b/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..59e413eb15b50a2ac49cd95bbe57b0d0018ccf95 --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf4e935f84569b849b662718eaa19be605037d46a5f7391f164728d6d9c3bb50 +size 148017 diff --git a/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/train.csv b/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..cc5acd9fe00e8a233592b4da9797300adc83744b --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ab621ac8239797506ce800c1409422452b8127da93add91dd9e6d63ddeec6f7 +size 1182326 diff --git a/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/val.csv b/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..e0a3b6ab9c53a7f8d97dff3290e643431f9ad023 --- /dev/null +++ b/timecost/n11/ctgan/ctgan-n11-20260430_235732/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:374e44edf25e58ca37eabbef0485c98cecdbdfa9d24da738937f404840bfad44 +size 147784 diff --git a/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_X_host.npy b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..271241f78130b5517767b66686f871e64bdd15ee --- /dev/null +++ b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0346bf8583b64028d74f6afb3d6f48466083db517af967cfb868f8f8fcced3ee +size 669588 diff --git a/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_gen.py b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..6d1c50a612ab03c3591050beb2931c7d8715e3f9 --- /dev/null +++ b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(15215)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/forest-n11-15215-20260503_211948.csv', index=False) +print("saved", len(df)) diff --git a/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_meta_host.json b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..80c3c7cdd1edd7a8bff257ae8e3d367944ff3e82 --- /dev/null +++ b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["28.7967", "16.0021", "2.6449", "0.3918", "0.1982", "27.7004", "22.011", "-8.2027", "40.092", "81.8828", "g"], "cat_indexes": []} \ No newline at end of file diff --git a/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_train.py b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..59fe16770473595dcd58ca3d086209df5fbe1200 --- /dev/null +++ b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n11/forestdiffusion/forest-n11-20260503_195211/forestdiffusion_model.joblib') diff --git a/timecost/n11/forestdiffusion/forest-n11-20260503_195211/forest-n11-15215-20260503_211948.csv b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/forest-n11-15215-20260503_211948.csv new file mode 100644 index 0000000000000000000000000000000000000000..e7c10a97d90ef0195cca483aa8606a57f686c600 --- /dev/null +++ b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/forest-n11-15215-20260503_211948.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5306a03af445c940f39afbb0ad66801284fb99a411f176ad2ca86dd929ff521d +size 3039046 diff --git a/timecost/n11/forestdiffusion/forest-n11-20260503_195211/forestdiffusion_model.joblib b/timecost/n11/forestdiffusion/forest-n11-20260503_195211/forestdiffusion_model.joblib 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sha256:6ddfba563c13321e71bd11530877eef18b9dadefd6acf7e4373e9f5f77c7570e +size 1784852 diff --git a/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/_tabbyflow_gen.py b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..f48bfd7c789c3ee98d8f015da8a3b05e337148f0 --- /dev/null +++ b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n11" +src = r"/work/output-Benchmark-trainonly-v1/n11/tabbyflow/tabbyflow-n11-20260501_000210/tabular_bundle/pipeline_n11" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n11/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(15215)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n11/tabbyflow/tabbyflow-n11-20260501_000210/tabbyflow-n11-15215-20260501_000941.csv") diff --git a/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/_tabbyflow_train.py b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2ec729d06ff321aa6995180adbbf27e2d0b61c1c --- /dev/null +++ b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n11" +src = r"/work/output-Benchmark-trainonly-v1/n11/tabbyflow/tabbyflow-n11-20260501_000210/tabular_bundle/pipeline_n11" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/gen_20260501_000941.log b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/gen_20260501_000941.log new file mode 100644 index 0000000000000000000000000000000000000000..4ebf08cbc0028b02d8d115475614e5020a54e071 --- /dev/null +++ b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/gen_20260501_000941.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d19cdbb6d0cecc0c344bad4e07c12df30793d8994759d2855bb4ff92b66531d +size 3065 diff --git a/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/input_snapshot.json b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/input_snapshot.json new file mode 100644 index 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+++ b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_000210/train_20260501_000210.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d28858e427269843d994e6532347987c862c60e1cec7b826cc0935c0ba0829ec +size 516222 diff --git a/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/_tabbyflow_gen.py b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..f5848f4a7ba7a90bf5e791cdce018d187ac36cb8 --- /dev/null +++ b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n11" +src = r"/work/output-Benchmark-trainonly-v1/n11/tabbyflow/tabbyflow-n11-20260501_035405/tabular_bundle/pipeline_n11" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n11/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(15215)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n11/tabbyflow/tabbyflow-n11-20260501_035405/tabbyflow-n11-15215-20260501_040149.csv") diff --git a/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/_tabbyflow_train.py b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..59e6143ed556cb26e90deb1410a6796ff0118752 --- /dev/null +++ b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n11" +src = r"/work/output-Benchmark-trainonly-v1/n11/tabbyflow/tabbyflow-n11-20260501_035405/tabular_bundle/pipeline_n11" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/gen_20260501_040149.log b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/gen_20260501_040149.log new file mode 100644 index 0000000000000000000000000000000000000000..ce70067514a4915758933bd7a3e2cefed9ccbb42 --- /dev/null +++ b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/gen_20260501_040149.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f90efc0e932b63b5d1e11d7ce2d52a66db00a8b39c475c72035c888a978908c +size 3066 diff --git 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a/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/train_20260501_035405.log b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/train_20260501_035405.log new file mode 100644 index 0000000000000000000000000000000000000000..96c27e48de2d51769399925c5a6e17de7a1d5e7f --- /dev/null +++ b/timecost/n11/tabbyflow/tabbyflow-n11-20260501_035405/train_20260501_035405.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:909303fe46899c17626d1d87ee607a0b01886541743bf51c10b679044237ed92 +size 516560 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/_tabddpm_sample_r0.py b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..49e0b2bc51320fcd11a4aaf9d17f09ca7a97c17d --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 15215 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/config_sample_20260430_231138_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/tabddpm-n11-15215-20260430_231138.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/tabddpm-n11-15215-20260430_231138.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/_tabddpm_train.py b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2e720ea747bf44eb62aa7b7e41ec9aa33b9928b4 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/config.toml b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..145e18de046bd826ecdad48743b6b4a8f061e064 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/data" +model_type = "mlp" +num_numerical_features = 10 +device = "cuda:0" + +[model_params] +d_in = 10 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/config_sample_20260430_231138_r0.toml b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/config_sample_20260430_231138_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..3cdcd718779794ec2e38c96e5559b1f9e2fecf57 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/config_sample_20260430_231138_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260430_231115/data" +model_type = "mlp" +num_numerical_features = 10 +device = "cuda:0" + +[model_params] +d_in = 10 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 15215 +batch_size = 1000 +seed = 0 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/X_num_train.npy b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..cda65645f7509c4b59dba4a620ca6db54dcdbe84 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:013e32900eef106da63d972b40b61296d91c04d1ce8196fd0cb8d54081f58c59 +size 608728 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/info.json b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ad93acde945d46916d99f9a52fb19bb740a3d107 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/info.json @@ -0,0 +1,37 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 15215, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9 + ], + "cat_col_idx": [], + "target_col_idx": [ + 10 + ], + "column_names": [ + "28.7967", + "16.0021", + "2.6449", + "0.3918", + "0.1982", + "27.7004", + "22.011", + "-8.2027", + "40.092", + "81.8828", + "g" + ], + "num_classes": 2 +} \ No newline at end of file diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/y_train.npy b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b523a92a6e5006ca3c440fc79afd02297be1f7ee --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95905dd042874f4c4e7523f7525b2864e220ef3c6fcdc58877ca3944645899cf +size 121848 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/gen_20260430_231138_r0.log b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/gen_20260430_231138_r0.log new file mode 100644 index 0000000000000000000000000000000000000000..735f1dc30761e28f9bdb7fbb43ea6c1c114904ea --- /dev/null +++ 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+cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 15215 +batch_size = 1000 +seed = 0 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/output/info.json b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ad93acde945d46916d99f9a52fb19bb740a3d107 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/output/info.json @@ -0,0 +1,37 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 15215, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9 + ], + "cat_col_idx": [], + "target_col_idx": [ + 10 + ], + "column_names": [ + "28.7967", + "16.0021", + "2.6449", + "0.3918", + "0.1982", + "27.7004", + "22.011", + "-8.2027", + "40.092", + "81.8828", + "g" + ], + "num_classes": 2 +} \ No newline at end of file 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b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/tabddpm-n11-15215-20260430_231138.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7a17fcdcc57fead07cd2244a7a951be4903af25b2db1d300566afcf2b5998365 +size 2909435 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/train_20260430_231115.log b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/train_20260430_231115.log new file mode 100644 index 0000000000000000000000000000000000000000..38f415bd7448e6037073f3ae2ef70aa020342356 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260430_231115/train_20260430_231115.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e57dcf25d39069140e75289c1b815ac106901118c56eaef39a3badb0da3ca641 +size 1070 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/_tabddpm_sample_r0.py b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..18f19fa2fffb24c497523ccaddd3c30f5ebd089b --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 15215 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/config_sample_20260501_040230_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/tabddpm-n11-15215-20260501_040230.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/tabddpm-n11-15215-20260501_040230.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/_tabddpm_train.py b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3ebc291becaccd52078510a369a2795499393b62 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/config.toml b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..88e19579fc251d9263f4888feff3d55beb32c169 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/data" +model_type = "mlp" +num_numerical_features = 10 +device = "cuda:0" + +[model_params] +d_in = 10 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/config_sample_20260501_040230_r0.toml b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/config_sample_20260501_040230_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..bdd183695470ccc2a57e9e49ed353d334b492e34 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/config_sample_20260501_040230_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/data" +model_type = "mlp" +num_numerical_features = 10 +device = "cuda:0" + +[model_params] +d_in = 10 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 15215 +batch_size = 1000 +seed = 0 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/X_num_train.npy b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..cda65645f7509c4b59dba4a620ca6db54dcdbe84 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:013e32900eef106da63d972b40b61296d91c04d1ce8196fd0cb8d54081f58c59 +size 608728 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/info.json b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..ad93acde945d46916d99f9a52fb19bb740a3d107 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/info.json @@ -0,0 +1,37 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 10, + "n_cat_features": 0, + "train_size": 15215, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9 + ], + "cat_col_idx": [], + "target_col_idx": [ + 10 + ], + "column_names": [ + "28.7967", + "16.0021", + "2.6449", + "0.3918", + "0.1982", + "27.7004", + "22.011", + "-8.2027", + "40.092", + "81.8828", + "g" + ], + "num_classes": 2 +} \ No newline at end of file diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/y_train.npy b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b523a92a6e5006ca3c440fc79afd02297be1f7ee --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95905dd042874f4c4e7523f7525b2864e220ef3c6fcdc58877ca3944645899cf +size 121848 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/gen_20260501_040230_r0.log 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+version https://git-lfs.github.com/spec/v1 +oid sha256:5f7afc6a344481c6233b9cad5a6c46c9e9ddc2ce22376e42bf385a06e41ca1c5 +size 1217328 diff --git a/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/output/config.toml b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..bdd183695470ccc2a57e9e49ed353d334b492e34 --- /dev/null +++ b/timecost/n11/tabddpm/tabddpm-n11-20260501_040207/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n11/tabddpm/tabddpm-n11-20260501_040207/data" +model_type = "mlp" +num_numerical_features = 10 +device = "cuda:0" + +[model_params] +d_in = 10 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] 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a/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/_tabdiff_gen.py b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..26883d5f2725ca698b7c8f931e0a12c3fa7af912 --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n11" +src = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260430_234952/tabular_bundle/pipeline_n11" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n11/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(15215)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260430_234952/tabdiff-n11-15215-20260430_235717.csv") diff --git a/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/_tabdiff_train.py b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..eb9f605f157e9a4241a34352a2798a079e66f010 --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n11" +src = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260430_234952/tabular_bundle/pipeline_n11" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/gen_20260430_235717.log b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/gen_20260430_235717.log new file mode 100644 index 0000000000000000000000000000000000000000..e3f8e07a46fb3562e493c2e1d8c8ce68da14136e --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/gen_20260430_235717.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a187b75dc57c0e9de8da75b6c28dd71213398633e32406853dfa00327d85b6b +size 5234 diff --git a/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/input_snapshot.json b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e86e031cc316e596dc2a104c4580467a1554cd05 --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n11", + "model": 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a/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/train_20260430_234953.log b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/train_20260430_234953.log new file mode 100644 index 0000000000000000000000000000000000000000..3cfec3f6bf644df4cfeae0e2797ff65a1f39a2b6 --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260430_234952/train_20260430_234953.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a49888b32205527eafc4ffb5eba65de4c5caf474e94ca2890a41b99d0c0e2e4f +size 523301 diff --git a/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/_tabdiff_gen.py b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..809de068f16506c7cee58d03ebaf82e49dee8590 --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n11" +src = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260501_040258/tabular_bundle/pipeline_n11" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n11/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(15215)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260501_040258/tabdiff-n11-15215-20260501_041026.csv") diff --git a/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/_tabdiff_train.py b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..94dc7809d0ecd56e23f1083d62d52826290360ea --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n11" +src = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260501_040258/tabular_bundle/pipeline_n11" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/gen_20260501_041026.log b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/gen_20260501_041026.log new file mode 100644 index 0000000000000000000000000000000000000000..9701a22cbf2b0bebbb2a5e537e45d3bbe3593b10 --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/gen_20260501_041026.log @@ -0,0 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a/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/tabular_bundle/pipeline_n11/y_val.npy b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/tabular_bundle/pipeline_n11/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b523a92a6e5006ca3c440fc79afd02297be1f7ee --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/tabular_bundle/pipeline_n11/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95905dd042874f4c4e7523f7525b2864e220ef3c6fcdc58877ca3944645899cf +size 121848 diff --git a/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/train_20260501_040258.log b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/train_20260501_040258.log new file mode 100644 index 0000000000000000000000000000000000000000..9fd2528173426ec90da5bc04c5a48537be0a80d8 --- /dev/null +++ b/timecost/n11/tabdiff/tabdiff-n11-20260501_040258/train_20260501_040258.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1764cc5d7712c6440ab22a5f8591e7d131aca91949b3b159b5fc72971cd89e3 +size 523020 diff --git a/timecost/n11/tabpfgen/tabpfgen-n11-20260501_000122/_tabpfgen_generate.py b/timecost/n11/tabpfgen/tabpfgen-n11-20260501_000122/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..547bff5a218e2526f1bcbe1c053a66d5272fa0f0 --- /dev/null +++ b/timecost/n11/tabpfgen/tabpfgen-n11-20260501_000122/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n11/tabpfgen/tabpfgen-n11-20260501_000122/staged/public/train.csv") +target_col = "g" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(15215) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n11/tabpfgen/tabpfgen-n11-20260501_000122/tabpfgen-n11-15215-20260501_000122.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> 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a/timecost/n11/tabpfgen/tabpfgen-n11-20260501_041043/_tabpfgen_generate.py b/timecost/n11/tabpfgen/tabpfgen-n11-20260501_041043/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b7ce6f849942260b7433bee678c13f122411f1fc --- /dev/null +++ b/timecost/n11/tabpfgen/tabpfgen-n11-20260501_041043/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n11/tabpfgen/tabpfgen-n11-20260501_041043/staged/public/train.csv") +target_col = "g" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(15215) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n11/tabpfgen/tabpfgen-n11-20260501_041043/tabpfgen-n11-15215-20260501_041043.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n11/tabpfgen/tabpfgen-n11-20260501_041043/tabpfgen-n11-15215-20260501_041043.csv") diff 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n11/tabpfgen/tabpfgen-n11-20260501_041043/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n11/tabpfgen/tabpfgen-n11-20260501_041043/staged/public/staged_features.json", + "target_col": "g", + "is_classification": true, + "task_type": "classification", + "n_rows": 15215, + "n_cols": 11 +} \ No newline at end of file diff --git a/timecost/n11/tabpfgen/tabpfgen-n11-20260501_041043/train_20260501_041043.log b/timecost/n11/tabpfgen/tabpfgen-n11-20260501_041043/train_20260501_041043.log new file mode 100644 index 0000000000000000000000000000000000000000..b9488d95e12ca00f0a0af5224c15bc10602ed391 --- /dev/null +++ b/timecost/n11/tabpfgen/tabpfgen-n11-20260501_041043/train_20260501_041043.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44e0b37f3bc90344efa38e7e431ff51921f5feade218f90087aeb795839a92a2 +size 599 diff --git a/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/_tabsyn_sample.py b/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..35f02f29ab2ec2ef0f12102be57d6fbaeebe2697 --- /dev/null +++ b/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n11/tabsyn/tabsyn-n11-20260430_231204" +dataname = "tabsyn_n11" +output_csv = "/work/output-Benchmark-trainonly-v1/n11/tabsyn/tabsyn-n11-20260430_231204/tabsyn-n11-15215-20260430_234940.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 15215 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/_tabsyn_train.py b/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..579340f05078b12cf6884c840409796d8fe3c273 --- /dev/null +++ b/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n11/tabsyn/tabsyn-n11-20260430_231204" +dataname = "tabsyn_n11" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/data/tabsyn_n11/X_cat_test.npy b/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/data/tabsyn_n11/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..65f01f4aaf9b5e6ac281ddfd05f178b82dc24ba9 --- /dev/null +++ b/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/data/tabsyn_n11/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab6735c7290e805ffeb57d96cdb652930cd19766131eb333e144f301ca5c5d90 +size 128 diff --git a/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/data/tabsyn_n11/X_cat_train.npy b/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/data/tabsyn_n11/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..65f01f4aaf9b5e6ac281ddfd05f178b82dc24ba9 --- /dev/null +++ b/timecost/n11/tabsyn/tabsyn-n11-20260430_231204/data/tabsyn_n11/X_cat_train.npy @@ -0,0 +1,3 @@ +version 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"tabsyn_n11" +output_csv = "/work/output-Benchmark-trainonly-v1/n11/tabsyn/tabsyn-n11-20260501_041133/tabsyn-n11-15215-20260501_045031.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 15215 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/n11/tabsyn/tabsyn-n11-20260501_041133/_tabsyn_train.py b/timecost/n11/tabsyn/tabsyn-n11-20260501_041133/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b4e9d4b9bd3fa23cd3d4f9989e70587f9979798e --- /dev/null +++ b/timecost/n11/tabsyn/tabsyn-n11-20260501_041133/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n11/tabsyn/tabsyn-n11-20260501_041133" +dataname = "tabsyn_n11" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/n11/tabsyn/tabsyn-n11-20260501_041133/data/tabsyn_n11/X_cat_test.npy b/timecost/n11/tabsyn/tabsyn-n11-20260501_041133/data/tabsyn_n11/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..65f01f4aaf9b5e6ac281ddfd05f178b82dc24ba9 --- /dev/null +++ b/timecost/n11/tabsyn/tabsyn-n11-20260501_041133/data/tabsyn_n11/X_cat_test.npy @@ -0,0 +1,3 @@ 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a/timecost/n11/tvae/tvae-n11-20260501_060415/_tvae_generate.py b/timecost/n11/tvae/tvae-n11-20260501_060415/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9e4b856dcbc0c03be55a28643e593faa566fb676 --- /dev/null +++ b/timecost/n11/tvae/tvae-n11-20260501_060415/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260501_060415/models_300epochs/tvae_300epochs.pt") +total = 15215 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260501_060415/tvae-n11-15215-20260501_060612.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260501_060415/tvae-n11-15215-20260501_060612.csv") diff --git a/timecost/n11/tvae/tvae-n11-20260501_060415/_tvae_train.py b/timecost/n11/tvae/tvae-n11-20260501_060415/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..80f11947200160a432862709610101f0a5f04466 --- /dev/null +++ b/timecost/n11/tvae/tvae-n11-20260501_060415/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260501_060415/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n11/tvae/tvae-n11-20260501_060415/tvae_metadata.json" +save_path = 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a/timecost/n11/tvae/tvae-n11-20260501_060415/tvae-n11-15215-20260501_060612.csv b/timecost/n11/tvae/tvae-n11-20260501_060415/tvae-n11-15215-20260501_060612.csv new file mode 100644 index 0000000000000000000000000000000000000000..8882c4f61e9a4b87850220f407fd91f40acc7de4 --- /dev/null +++ b/timecost/n11/tvae/tvae-n11-20260501_060415/tvae-n11-15215-20260501_060612.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b16c71741840bc635f0e86a02736b1e041e81c258f32848e9f0329becbcda31 +size 2883511 diff --git a/timecost/n11/tvae/tvae-n11-20260501_060415/tvae_metadata.json b/timecost/n11/tvae/tvae-n11-20260501_060415/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..cb19699b85fe49965d86e54abd3c67a8ec6163a8 --- /dev/null +++ b/timecost/n11/tvae/tvae-n11-20260501_060415/tvae_metadata.json @@ -0,0 +1,48 @@ +{ + "columns": [ + { + "name": "28.7967", + "type": "continuous" + }, + { + "name": "16.0021", + "type": "continuous" + }, + { + "name": "2.6449", + "type": "continuous" + }, + { + "name": "0.3918", + "type": "continuous" + }, + { + "name": "0.1982", + "type": "continuous" + }, + { + "name": "27.7004", + "type": "continuous" + }, + { + "name": "22.011", + "type": "continuous" + }, + { + "name": "-8.2027", + "type": "continuous" + }, + { + "name": "40.092", + "type": "continuous" + }, + { + "name": "81.8828", + "type": "continuous" + }, + { + "name": "g", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/arf/arf-n3-20260501_225023/_arf_generate.py b/timecost/n3/arf/arf-n3-20260501_225023/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e96b4b1cd9495971fc9fe271d54266725c3ad34a --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(3918) +c_csv = "/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n3' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv") diff --git a/timecost/n3/arf/arf-n3-20260501_225023/_arf_train.py b/timecost/n3/arf/arf-n3-20260501_225023/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..da3bae6bf5ab0806c16069e5f00dd55deafac2fe --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf_model.pkl") diff --git a/timecost/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv b/timecost/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv new file mode 100644 index 0000000000000000000000000000000000000000..c13e43168f0bc389492c40106d5226be37884904 --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv @@ -0,0 +1,3 @@ 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"dataset_id": "n3", + "model": "arf", + "run_id": "arf-n3-20260501_225023", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-01T22:50:23", + "ended_at": "2026-05-01T22:50:52", + "duration_sec": 28.875 + }, + "generate": { + "started_at": "2026-05-01T22:50:52", + "ended_at": "2026-05-01T22:50:54", + "duration_sec": 2.591 + } + } +} \ No newline at end of file diff --git a/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_report.json b/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..0378e903f33fcfaa4f0b82a011e2cf31d64f52b6 --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_transforms_applied.json b/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/model_input_manifest.json b/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b46735d55419e5f139da6469b04c0161024a8c4a --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/staged/arf/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "arf", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + 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0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json b/timecost/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/arf/arf-n3-20260501_225023/staged/public/test.csv b/timecost/n3/arf/arf-n3-20260501_225023/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/arf/arf-n3-20260501_225023/staged/public/train.csv b/timecost/n3/arf/arf-n3-20260501_225023/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/arf/arf-n3-20260501_225023/staged/public/val.csv b/timecost/n3/arf/arf-n3-20260501_225023/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/arf/arf-n3-20260501_225023/train_20260501_225023.log b/timecost/n3/arf/arf-n3-20260501_225023/train_20260501_225023.log new file mode 100644 index 0000000000000000000000000000000000000000..f68464a938a0665daedb3ba539f3024b0452a832 --- /dev/null +++ b/timecost/n3/arf/arf-n3-20260501_225023/train_20260501_225023.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2178ce762ea2f8d576ec23371a0d6569661f9d5ac97736a0c9b02e75c175edf +size 613 diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_generate.py b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..76ecd4acf4cb2ce14551a757d0ac42cc16db4d61 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(3918) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv") diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_train.py b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6f4775148165f0c3356a0cc2ea96c6b087285084 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl") diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv new file mode 100644 index 0000000000000000000000000000000000000000..5122af8070a39856aef00fd519e4673134151cf4 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7eaef9af040116c7fea3c8e5e566ea772624f1e1548c99fa7b800a0deeac3f44 +size 884307 diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_coltypes.json b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..59893d873ed1f60d3bbb17d18abfda70729fe810 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_coltypes.json @@ -0,0 +1,53 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..6ea0fbeea7caaa2028344865d76bd34aad1eae86 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6796efb78000c399e81e0fc93d03b1d91f3c5d4aec0d0f61ad13d70100e0c1f5 +size 18294 diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/const_cols.json b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/gen_20260501_225112.log b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/gen_20260501_225112.log new file mode 100644 index 0000000000000000000000000000000000000000..3c5d91d605428a589e3c953ba0f2c1df2686c0a1 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/gen_20260501_225112.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3c998b1d75b6c0e1a4e92d1349b8faebfbc99e3baf489c9bf5c951f847863656 +size 3661 diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/input_snapshot.json b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..527cf133718a3c780f9b12547e302d03bea557f3 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/normalized_schema_snapshot.json b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/public_gate_report.json b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..93b97319f739e39079d6c6549fd2020f8a6dac25 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "n3", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "quality", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/staged_input_manifest.json b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..008dabc157f7ab7860beed06f79679e718b641a1 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/staged_features.json b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/test.csv b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/val.csv b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/train_20260501_225104.log b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/train_20260501_225104.log new file mode 100644 index 0000000000000000000000000000000000000000..b5aff868cf2aec0b81c0fc8bfef5f229545ee88e --- /dev/null +++ b/timecost/n3/bayesnet/bayesnet-n3-20260501_225103/train_20260501_225104.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:62ccdc6bb8d143b5e6ea6deccbac97f29a291610113c088a7479c26916b67c4c +size 3740 diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/_ctgan_generate.py b/timecost/n3/ctgan/ctgan-n3-20260501_005113/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d06e1f78d54c175fd2734ad4935bf3f6f30bd03d --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260501_005113/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/models_300epochs/ctgan_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/ctgan-n3-3918-20260501_005206.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/ctgan-n3-3918-20260501_005206.csv") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/ctgan-n3-3918-20260501_005206.csv b/timecost/n3/ctgan/ctgan-n3-20260501_005113/ctgan-n3-3918-20260501_005206.csv new file mode 100644 index 0000000000000000000000000000000000000000..ec32a399d834082ce20c7ca1258493e5af836ada --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260501_005113/ctgan-n3-3918-20260501_005206.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb7cca2ebbfceeae0f1a15299eca29196ab816349ca255cebba0e33c75dc726a +size 820521 diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/ctgan_metadata.json 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end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/gen_20260501_005206.log b/timecost/n3/ctgan/ctgan-n3-20260501_005113/gen_20260501_005206.log new file mode 100644 index 0000000000000000000000000000000000000000..ec6b63f77793154fb766459734950884dda24562 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260501_005113/gen_20260501_005206.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:81caeeeaeaf6811441143067d2e107b9757a8617e8ceafc51558327c2de93ab9 +size 560 diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/input_snapshot.json b/timecost/n3/ctgan/ctgan-n3-20260501_005113/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..15f60f257edd5bcdb6b9e07be8cb5182076affc1 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260501_005113/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": 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"unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260501_005113/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/staged_features.json b/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/test.csv b/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/train.csv b/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/val.csv b/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260501_005113/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_generate.py b/timecost/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b820b344bc742aa80cfe43715de01bbef1be7c65 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_train.py b/timecost/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7cdd5c75b05c8b9cba66e0199f95e5ab98dd3c05 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=8, + generator_dim=(16, 16), + discriminator_dim=(16, 16), + batch_size=32, + pac=1, + epochs=50, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv new file mode 100644 index 0000000000000000000000000000000000000000..a1d9e9c1d0add5c9ffff28dabcef3a7f5aa6950e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/ctgan-n3-3918-20260504_163441.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f14ad83416025a5455f69cec6d63e5314c344048ab5372353ae3ca5d75ce34a +size 820708 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/ctgan_metadata.json b/timecost/n3/ctgan/ctgan-n3-20260504_163226/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/gen_20260504_163441.log b/timecost/n3/ctgan/ctgan-n3-20260504_163226/gen_20260504_163441.log new file mode 100644 index 0000000000000000000000000000000000000000..8eaf0a5f880a368aac68190507b2428610e0d853 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/gen_20260504_163441.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd91dc885739943a172da5fe8f7b656b43c8c09a7a306e321dd06d8bd4651ba3 +size 297 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/input_snapshot.json b/timecost/n3/ctgan/ctgan-n3-20260504_163226/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..15f60f257edd5bcdb6b9e07be8cb5182076affc1 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt b/timecost/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..bbb0cbaacbc817307e5522ddef85dc880bb14d45 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/ctgan_50epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:703f7cca3605fab52fcc211a0b02112be0cb8cfd9b8aba51e833ec78623354a5 +size 415565 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/train_20260504_163226.log b/timecost/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/train_20260504_163226.log new file mode 100644 index 0000000000000000000000000000000000000000..21ef5600efcd897a98cbddebf0107f67b35ffa0b --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/models_50epochs/train_20260504_163226.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d7dc02e5f0a183615d8d29de65352b320938677bbc9695ddca5d5550c2338189 +size 11793 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/public_gate/normalized_schema_snapshot.json b/timecost/n3/ctgan/ctgan-n3-20260504_163226/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163226/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/staged_features.json b/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/test.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/train.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/val.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163226/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_generate.py b/timecost/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..22ab4eff2cf36a3dbade7c9717bd82f27574e27a --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_train.py b/timecost/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..efefe23c3f4c3b677bdbdd9cd49c5023cb93ec59 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=64, + pac=1, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv new file mode 100644 index 0000000000000000000000000000000000000000..6ace7e6d64bae7d23ac937fe14b1aeba2bb87411 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/ctgan-n3-3918-20260504_163716.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0af5beb641d31c47be95b72dcde43e1f2257348f4bbe820bc445b54a1373322b +size 820193 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/ctgan_metadata.json b/timecost/n3/ctgan/ctgan-n3-20260504_163500/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/gen_20260504_163716.log b/timecost/n3/ctgan/ctgan-n3-20260504_163500/gen_20260504_163716.log new file mode 100644 index 0000000000000000000000000000000000000000..69a4ef97164584d5346f7a0c46bc6e87b87e9510 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/gen_20260504_163716.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53729f3a0759d7538eadf40ce5d6b43a2f86e67ee8d865c88495bf00edbf30da +size 297 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/input_snapshot.json b/timecost/n3/ctgan/ctgan-n3-20260504_163500/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..15f60f257edd5bcdb6b9e07be8cb5182076affc1 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt b/timecost/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..8cae7c9f9e86d4a13fd9cf686d254b7c1325432b --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f6b319defdbca7ac68458f1b02a5079de9a66fb276c7e650dc5edc6341fac652 +size 441827 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/train_20260504_163500.log b/timecost/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/train_20260504_163500.log new file mode 100644 index 0000000000000000000000000000000000000000..b03100c01d1ea48000143060eedbf87f411ddfa9 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/models_100epochs/train_20260504_163500.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:07a8341d5af0560c0fdf9128b8d26b3934333edd9f985540adbecb32ad24eedf +size 21140 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/public_gate/normalized_schema_snapshot.json b/timecost/n3/ctgan/ctgan-n3-20260504_163500/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git 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index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + 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mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163500/staged/public/val.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163500/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163500/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_generate.py b/timecost/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..958966b7d26451c5cec305e5e95d971ad603c9f4 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_train.py b/timecost/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..37981c24b61b6ffbc8d764127aae6a5d20410d3e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=128, + pac=1, + epochs=150, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv new file mode 100644 index 0000000000000000000000000000000000000000..d01269246f4056aca40e395921ff81b5b22a970a --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/ctgan-n3-3918-20260504_163919.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ff18e4b52213da6adbc8c4ced644c1bc2994a3bc04c52d99bd2a9fb3fc4be71d +size 819814 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/ctgan_metadata.json b/timecost/n3/ctgan/ctgan-n3-20260504_163735/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/gen_20260504_163919.log b/timecost/n3/ctgan/ctgan-n3-20260504_163735/gen_20260504_163919.log new file mode 100644 index 0000000000000000000000000000000000000000..c15018523d9b663dbb518ee0443132e2447a3679 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/gen_20260504_163919.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d4a1b092247ba70ae7e47162bf2690c54df0f896d414770729c5056c19aacf32 +size 297 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/input_snapshot.json b/timecost/n3/ctgan/ctgan-n3-20260504_163735/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..15f60f257edd5bcdb6b9e07be8cb5182076affc1 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt b/timecost/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..4db67a4aeabd2fff80556fe2c44e53e5a8fb81c0 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/ctgan_150epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a4a33a35f9defe6e6439e7c8b121330e4d256a10c66e3a10ae961c1c85a6ec4 +size 505059 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/train_20260504_163735.log b/timecost/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/train_20260504_163735.log new file mode 100644 index 0000000000000000000000000000000000000000..6b8459c2f3154af1c9ce3e364b77a7ff421577d1 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/models_150epochs/train_20260504_163735.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:220218392a416e873cce66abedb2ed0236db16e1716439b399022e7bef8c542b +size 30539 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/normalized_schema_snapshot.json b/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/public_gate_report.json b/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..93b97319f739e39079d6c6549fd2020f8a6dac25 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "n3", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "quality", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/staged_input_manifest.json b/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f086dff7a315e8fd37f2438a7615dd2734e2c63f --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv", + "val_csv": 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a/timecost/n3/ctgan/ctgan-n3-20260504_163735/run_config.json b/timecost/n3/ctgan/ctgan-n3-20260504_163735/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..e37b55dabec637f4ec7e13855b6fb5990a44a7d8 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:37:35", + "dataset_id": "n3", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163735", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "n3", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 3918, + "model_path": 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index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/test.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/val.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163735/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_generate.py b/timecost/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..11daec1d2a737e9b2a264f4628dd15c77d08202e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_train.py b/timecost/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..932023a5c4862392815be9d0fa56d2f3dcb84b5c --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=16, + generator_dim=(32, 32), + discriminator_dim=(32, 32), + batch_size=80, + pac=5, + epochs=100, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv new file mode 100644 index 0000000000000000000000000000000000000000..5506dd849d4d391e8754bc60cff1a6da38745e26 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93d55cffc061237adad6514ad0b9d5b22ab70147932c96f5cde041ff0f2ce05c +size 821320 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/ctgan_metadata.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/ctgan_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/gen_20260504_164128.log b/timecost/n3/ctgan/ctgan-n3-20260504_163938/gen_20260504_164128.log new file mode 100644 index 0000000000000000000000000000000000000000..57879be2cdd4bf15e013811741863132c1797dba --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/gen_20260504_164128.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:92e5b04d368e83994eed81d86660bad6611d5497831634fce41b9e8e9b85993c +size 297 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/input_snapshot.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..15f60f257edd5bcdb6b9e07be8cb5182076affc1 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": 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a/timecost/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt b/timecost/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e10bb01ab5a943d3b8f5362eaeab273814da2602 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2b2544f21e5cdec77204a9e3825cdbe1d940087b75dd9cfe564b6f065929ac1d +size 441827 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/train_20260504_163938.log b/timecost/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/train_20260504_163938.log new file mode 100644 index 0000000000000000000000000000000000000000..59e77ff88703915772c30c740890d66f6dabf292 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/train_20260504_163938.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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a/timecost/n3/ctgan/ctgan-n3-20260504_163938/public_gate/public_gate_report.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..93b97319f739e39079d6c6549fd2020f8a6dac25 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "n3", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "quality", + "task_type": "classification", + "input_splits": { + "train": 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a/timecost/n3/ctgan/ctgan-n3-20260504_163938/run_config.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..3f4507d19cd409a75629d94ac8f1dafe292f4fb2 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/run_config.json @@ -0,0 +1,46 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T16:39:38", + "dataset_id": "n3", + "model": "ctgan", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "ctgan", + "dataset": "n3", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 3918, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/public/staged_features.json", + "target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_CTGAN_GPUS": "device=3", + "CTGAN_BATCH_SIZE": "80", + "CTGAN_DEFAULT_EPOCHS": "100", + "CTGAN_DISCRIMINATOR_DIMS": "32,32", + "CTGAN_EMBEDDING_DIM": "16", + "CTGAN_GENERATOR_DIMS": "32,32", + "CTGAN_PAC": "5" + } +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/runtime_result.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..14c4db9b6a71f0808c83e0d13a0a55db4a3d1f91 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "run_id": "ctgan-n3-20260504_163938", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/ctgan-n3-3918-20260504_164128.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/models_100epochs/ctgan_100epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T16:39:38", + "ended_at": "2026-05-04T16:41:28", + "duration_sec": 110.202 + }, + "generate": { + "started_at": "2026-05-04T16:41:28", + "ended_at": "2026-05-04T16:41:34", + "duration_sec": 6.041 + } + } +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/adapter_report.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..b8485d4663ea6879e2c4a49a9919b70ae9d9efd3 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/adapter_transforms_applied.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/model_input_manifest.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c1d03a3ff894a845be3787e65c922fe900543cd5 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/ctgan/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + 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"144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_163938/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/staged_features.json b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/test.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/val.csv b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_163938/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_generate.py b/timecost/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ce16fd64962b33468b0055b7bdf9bc8546696887 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/models_200epochs/ctgan_200epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_train.py b/timecost/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..74c218a06e4ff52752bd2e472c3d7754f1d49e1a --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/staged/public/train.csv") +discrete_columns = [] +model = CTGAN( + embedding_dim=32, + generator_dim=(64, 64), + discriminator_dim=(64, 64), + batch_size=100, + pac=10, + epochs=200, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/models_200epochs/ctgan_200epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/models_200epochs/ctgan_200epochs.pt") \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv b/timecost/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv new file mode 100644 index 0000000000000000000000000000000000000000..f227eb8a8114bb08526db72cb68358776eff32ca --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/ctgan-n3-3918-20260504_164434.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5cef0aba072b44c34ebff73200196215ccf66da1c54e39ea715d23d26bb30627 +size 820612 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/ctgan_metadata.json b/timecost/n3/ctgan/ctgan-n3-20260504_164147/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ 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0000000000000000000000000000000000000000..b05358b8982cb6b8643b8f1fce66f02c3b70574c --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/ctgan/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/staged/ctgan/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/ctgan/adapter_transforms_applied.json b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/ctgan/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/ctgan/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/ctgan/model_input_manifest.json b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/ctgan/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..43d4a7dc16aac46136fb461c1cc04f6ac566f186 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/ctgan/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "ctgan", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/ctgan/ctgan-n3-20260504_164147/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/staged_features.json b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/test.csv b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/train.csv b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/val.csv b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/ctgan/ctgan-n3-20260504_164147/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_X_host.npy b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..bb3be252292265fb98716334d40ccfaf8d75d244 --- /dev/null +++ b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b15fb02a2af34c0c93fdfe59170a6f8c933a7a529162825116447fdff3efa1ff +size 188192 diff --git a/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_gen.py b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..3faf692aec8e754cc83be1780f972f41046bdc13 --- /dev/null +++ b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(3918)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/forest-n3-3918-20260501_180740.csv', index=False) +print("saved", len(df)) diff --git a/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_meta_host.json b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..c65de394e081438752dd19dc6672d70748924abc --- /dev/null +++ b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol", "quality"], "cat_indexes": []} \ No newline at end of file diff --git a/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_train.py b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1c0813a1369f8a258815c4800dec650556ea556b --- /dev/null +++ b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib') diff --git a/timecost/n3/forestdiffusion/forest-n3-20260501_180619/forest-n3-3918-20260501_180740.csv b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/forest-n3-3918-20260501_180740.csv new file mode 100644 index 0000000000000000000000000000000000000000..ace52e09254eecbde1a45736816e358a97ddde24 --- /dev/null +++ b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/forest-n3-3918-20260501_180740.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0dc1c9e6161a7a3513fcbbfae9f79784fe424e8cdcbb3ea644bf6ae70f528826 +size 885780 diff --git a/timecost/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..dab33bf8088b3dc6fa4a9faafe7fa22fa997e3db --- /dev/null +++ b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66e33e576edc1764bb9aa9241fd93cc76909c92d632f5f6ec59032c1077da881 +size 112963525 diff --git a/timecost/n3/forestdiffusion/forest-n3-20260501_180619/gen_20260501_180740.log b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/gen_20260501_180740.log new file mode 100644 index 0000000000000000000000000000000000000000..b3f05143e2ba4c0228a38881e76584277577370d --- /dev/null +++ b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/gen_20260501_180740.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64470da0846551efa04a9c102196df00df47ef0fb56dda8937a30799fdcd3f94 +size 294 diff --git a/timecost/n3/forestdiffusion/forest-n3-20260501_180619/input_snapshot.json b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1706b1e21e3ea594c0730d0ec7b58ca4e0d54ac2 --- /dev/null +++ b/timecost/n3/forestdiffusion/forest-n3-20260501_180619/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/realtabformer/rtf-n3-20260501_010320/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/realtabformer/rtf-n3-20260501_010320/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/realtabformer/rtf-n3-20260501_010320/train_20260501_010320.log b/timecost/n3/realtabformer/rtf-n3-20260501_010320/train_20260501_010320.log new file mode 100644 index 0000000000000000000000000000000000000000..47d08f616d63b3731a73d0fd5cf86d85f5b9ef21 --- /dev/null +++ b/timecost/n3/realtabformer/rtf-n3-20260501_010320/train_20260501_010320.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:524c61eb693e3775beed7a4576dd3e79ea4ac03a163f7af2ee383ee6b691acbe +size 472847 diff --git a/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_gen.py b/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..cc773dcad632b3cd990c6e78e582634fc419b797 --- /dev/null +++ b/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260501_005234/tabular_bundle/pipeline_n3" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n3/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260501_005234/tabbyflow-n3-3918-20260501_005942.csv") diff --git a/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_train.py b/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..11c52de385c5406a71ac62f40888a03828ce3be8 --- /dev/null +++ b/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260501_005234/tabular_bundle/pipeline_n3" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/gen_20260501_005942.log b/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/gen_20260501_005942.log new file mode 100644 index 0000000000000000000000000000000000000000..d618b5545214c9f0244bbc1a457a80ca94330377 --- /dev/null +++ b/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/gen_20260501_005942.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06fd1176aa6aa493b82c7c6071c805ec456bbdc3f4480bcd635ee8a5b88f784d +size 3026 diff --git a/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/input_snapshot.json b/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3a0cbc32605f1212374c27c5a11c08064d5b6b53 --- /dev/null +++ b/timecost/n3/tabbyflow/tabbyflow-n3-20260501_005234/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tabbyflow", + "inputs": { + "train_csv": { + 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sha256:ca653f9d17f07836e86f33599f21c86b6963cfc556ba93395b613173b41d5637 +size 290257 diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_sample_r0.py b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..3d5fd4e093bb54b433e51d58d71710d7e8411c31 --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 3918 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/config_sample_20260501_002035_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/tabddpm-n3-3918-20260501_002035.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/tabddpm-n3-3918-20260501_002035.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_train.py b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..16db5d0522d0347df0ebb928d10a5cd3e3da9af9 --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..c774736da1e2d6dd06bb2981c8d5d527a7059c43 --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/config_sample_20260501_002035_r0.toml b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/config_sample_20260501_002035_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..00d8c1b8acc5120a9558843978768b9f459c23e0 --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/config_sample_20260501_002035_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 1000 +seed = 0 diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/data/X_num_train.npy b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..94beb8413ef55592710518365965f4cb503523ee --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:093d8f405d764277d5a007953f25ab6466f0857f71f0d8f3c437463fb5bf240a +size 172520 diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/data/info.json b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/data/info.json new file mode 100644 index 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b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/output/X_num_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..096a5edbb8961266e9ef68f32a8c7dfe547ac91d --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/output/X_num_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f0f79ea00cee09d144e3b6b4c3c4c78fc3898e91e1619bce14225d2e68fc4bf +size 344912 diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/output/config.toml b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..00d8c1b8acc5120a9558843978768b9f459c23e0 --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 1000 +seed = 0 diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/output/info.json b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/output/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + 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"classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + 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a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/tabddpm-n3-3918-20260501_002035.csv b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/tabddpm-n3-3918-20260501_002035.csv new file mode 100644 index 0000000000000000000000000000000000000000..b34cd5beb2ca50ac51ae8872c40a18add64ef325 --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/tabddpm-n3-3918-20260501_002035.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d6215ab7dae82ddeae2fc63f308c3bc59d26da30cd7e61dd046ab97b6619fef9 +size 774771 diff --git a/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/train_20260501_002012.log b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/train_20260501_002012.log new file mode 100644 index 0000000000000000000000000000000000000000..9be1c25892c24ae25207e9ee3383feac085086c7 --- /dev/null +++ b/timecost/n3/tabddpm/tabddpm-n3-20260501_002012/train_20260501_002012.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5ba05af1754eef93e2c519198aa9c11b3ded64badaf882b13abde890338e8da +size 1067 diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_gen.py b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..9def070a662335ac19866533cb6759f328f2a7de --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_004446/tabdiff-n3-3918-20260501_005103.csv") diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_train.py b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8785d95501d301a66431bbf637bbae9ad95d3ff5 --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_004446/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/gen_20260501_005103.log b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/gen_20260501_005103.log new file mode 100644 index 0000000000000000000000000000000000000000..f7600d692ca0911218d74673978f40a4a59e0e09 --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/gen_20260501_005103.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2bb6dc678842c114f1cf611551ea8e33b9c867cd1c303062c178c576a93266a3 +size 4533 diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/input_snapshot.json b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..751cd5fa38e2fbd0a4680d9f58579382404e5586 --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/input_snapshot.json 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b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/train_20260501_004446.log new file mode 100644 index 0000000000000000000000000000000000000000..2b845bdab3501091d630a028f052c02dd4ce1b81 --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_004446/train_20260501_004446.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cc36ffc16d3a5a3628708bc1337e4733f6eefc2d2eda647495b92de7f796b63e +size 291235 diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_gen.py b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..76f2f6d01190680e15e6db23b7eb4d5923a7b8c4 --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_175353/tabdiff-n3-3918-20260501_175710.csv") diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_train.py b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..33f62823f0bbcc1bf529c511776922b9648c407f --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/gen_20260501_175710.log b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/gen_20260501_175710.log new file mode 100644 index 0000000000000000000000000000000000000000..f245c4a246c56e53c45acea4c78cd9188f904962 --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/gen_20260501_175710.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3da9ef17a571d51a9b70d51526cf51996b7395611f280fe207f05ff9177a4626 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/dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/X_num_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:093d8f405d764277d5a007953f25ab6466f0857f71f0d8f3c437463fb5bf240a +size 172520 diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/info.json b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8b5f7b3a1a0c4ba2d967287826b9f5c5fd7619de --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/info.json @@ -0,0 +1,139 @@ +{ + "name": "pipeline_n3", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "test_num": 3918, + "val_num": 3918, + "train_num": 3918, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "2": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "3": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "4": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "5": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "6": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "7": { + "sdtype": "numerical", + 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b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_train.npy b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_val.npy b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/train_20260501_175353.log b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/train_20260501_175353.log new file mode 100644 index 0000000000000000000000000000000000000000..df10c37193a38e053d07c80622b7227d4213517f --- /dev/null +++ b/timecost/n3/tabdiff/tabdiff-n3-20260501_175353/train_20260501_175353.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6437c04a0e9bf2dc8ef1f75a52cdb0a0418c32ce3ad26deb0563e9e04a803e9b +size 292871 diff --git a/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/_tabpfgen_generate.py b/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c8ade1fa83cb3e83789abaa5dd376381b18ae5df --- /dev/null +++ b/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/train.csv") +target_col = "quality" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(3918) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv") diff --git a/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/gen_20260501_005212.log b/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/gen_20260501_005212.log new file mode 100644 index 0000000000000000000000000000000000000000..349d4af44544d58c87db4e1cf03ceb3b413eacc5 --- /dev/null +++ b/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/gen_20260501_005212.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f4428b3af4adbadd1e3897c129553b24bcd222d974b2b4f43ef62d9432317e1 +size 949 diff --git a/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/input_snapshot.json b/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..2df53dbad689923eb0692c0029f1d32d625f8ce4 --- /dev/null +++ b/timecost/n3/tabpfgen/tabpfgen-n3-20260501_005212/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": 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"/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260501_002047" +dataname = "tabsyn_n3" +output_csv = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260501_002047/tabsyn-n3-3918-20260501_004440.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 3918 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_train.py b/timecost/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ed08fcb958c5edfd8a5b0c44967e0ef6c676d14e --- /dev/null +++ b/timecost/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n3/tabsyn/tabsyn-n3-20260501_002047" +dataname = "tabsyn_n3" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/n3/tabsyn/tabsyn-n3-20260501_002047/data/tabsyn_n3/X_cat_test.npy b/timecost/n3/tabsyn/tabsyn-n3-20260501_002047/data/tabsyn_n3/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..24e2b0be40b07b6e79f803a82e6e38723e462320 --- /dev/null +++ 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0000000000000000000000000000000000000000..3cb0016df552216453e11ecaa99f4b9591a1d2e1 --- /dev/null +++ b/timecost/n3/tabsyn/tabsyn-n3-20260501_002047/train_20260501_002047.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1ac6f13fb22e8af5c77411e25c11061b88a678f5d0523a07ca3c68a5d5900fe6 +size 2178664 diff --git a/timecost/n3/tvae/tvae-n3-20260501_060320/_tvae_generate.py b/timecost/n3/tvae/tvae-n3-20260501_060320/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..52c562c2500b3e0dc70ac69e40dcd0b563d31d93 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260501_060320/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260501_060320/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260501_060320/tvae-n3-3918-20260501_060403.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260501_060320/tvae-n3-3918-20260501_060403.csv") diff --git a/timecost/n3/tvae/tvae-n3-20260501_060320/_tvae_train.py b/timecost/n3/tvae/tvae-n3-20260501_060320/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9ffb91d2f7992ec86bc3302a687cd09505dfa5fa --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260501_060320/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260501_060320/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260501_060320/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260501_060320/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/n3/tvae/tvae-n3-20260501_060320/gen_20260501_060403.log b/timecost/n3/tvae/tvae-n3-20260501_060320/gen_20260501_060403.log new file mode 100644 index 0000000000000000000000000000000000000000..5f22c71123057e3a567cc4b56bec9e252719dfc7 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260501_060320/gen_20260501_060403.log @@ -0,0 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b/timecost/n3/tvae/tvae-n3-20260501_060320/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/_tvae_generate.py b/timecost/n3/tvae/tvae-n3-20260504_172819/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c7b9f5dbe7772cae78842d3e657dde8a6274ea8e --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_172819/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv") diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/_tvae_train.py b/timecost/n3/tvae/tvae-n3-20260504_172819/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7c7be901c03a6afbdafae5c9fa0fdb9cbe3a66b9 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_172819/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/gen_20260504_172901.log b/timecost/n3/tvae/tvae-n3-20260504_172819/gen_20260504_172901.log new file mode 100644 index 0000000000000000000000000000000000000000..9f4c8df978304685fee287c97778601fa7ccd326 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_172819/gen_20260504_172901.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a43efa6944e23c4b948b6dcbdb07c34fa3696ecc216dd410142b5a766c516a40 +size 404 diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/input_snapshot.json b/timecost/n3/tvae/tvae-n3-20260504_172819/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddd4c8dc6c3d918927aad5bcb5dbb27f78068fe --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_172819/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/models_300epochs/train_20260504_172819.log b/timecost/n3/tvae/tvae-n3-20260504_172819/models_300epochs/train_20260504_172819.log new file mode 100644 index 0000000000000000000000000000000000000000..c9fda6dd36749071d1d84382f79794a3e6b5a34e --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_172819/models_300epochs/train_20260504_172819.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:929c00c18481d7e5c1d6ba30eacbb318985eb82602164744517e2b90b891f0c0 +size 532 diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt b/timecost/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt new file 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"pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "quality", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/public_gate/staged_input_manifest.json b/timecost/n3/tvae/tvae-n3-20260504_172819/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a9014867d4657a4fc3d9350a8e779c8589c820dc --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_172819/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_172819/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv b/timecost/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv new file mode 100644 index 0000000000000000000000000000000000000000..ce98bd29f56845d8d881c8f81d574afb9ea1db39 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1705f92cdce4d8ba19f5c7cbe746d70c8efc8448d05c10bc2f7845b2ac2570b +size 820144 diff --git a/timecost/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json b/timecost/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/_tvae_generate.py b/timecost/n3/tvae/tvae-n3-20260504_175727/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4f19e5323617cc73049297e90dab025fa598fb8d --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv") diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/_tvae_train.py b/timecost/n3/tvae/tvae-n3-20260504_175727/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5dd280a8b9572c97a00b926438610b5dcbd8f601 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/gen_20260504_175758.log b/timecost/n3/tvae/tvae-n3-20260504_175727/gen_20260504_175758.log new file mode 100644 index 0000000000000000000000000000000000000000..be2199d29a34a5d38935b76a6d7a38e67909605f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/gen_20260504_175758.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:db448ebaabbc4cd4b4983a83fcceef6c65e0ea33541705bb461c888d9975eb40 +size 404 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/input_snapshot.json b/timecost/n3/tvae/tvae-n3-20260504_175727/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddd4c8dc6c3d918927aad5bcb5dbb27f78068fe --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/models_300epochs/train_20260504_175727.log b/timecost/n3/tvae/tvae-n3-20260504_175727/models_300epochs/train_20260504_175727.log new file mode 100644 index 0000000000000000000000000000000000000000..3e3799a9880df5025cf8ccc9aa86da10e6b560fe --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/models_300epochs/train_20260504_175727.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:82a8d64b2911679c891b7ffa21f7fab5f4b3a1f34882f998869e12b416f2fa7a +size 615 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt b/timecost/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..bd6e93a7286e021ba3c52b9a2512cd47abe06ecd --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2fd1df8cb6b44f16c94bcde8d66db7e09270950305b5018e9f53a6dd2c29431 +size 483628 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/public_gate/normalized_schema_snapshot.json b/timecost/n3/tvae/tvae-n3-20260504_175727/public_gate/normalized_schema_snapshot.json new file mode 100644 index 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b/timecost/n3/tvae/tvae-n3-20260504_175727/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "n3", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "quality", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/public_gate/staged_input_manifest.json b/timecost/n3/tvae/tvae-n3-20260504_175727/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..cbbba1be64e5766989ea73aea1f9411b3baca043 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/test.csv", + "features_json": 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"schema_version": 1, + "recorded_at": "2026-05-04T17:57:27", + "dataset_id": "n3", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "n3", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 3918, + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt", + "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv" + }, + "input_artifacts": { + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/public_gate/public_gate_report.json", + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/public_gate/staged_input_manifest.json", + "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/staged_features.json", + "target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "64,64", + "TVAE_DECOMPRESS_DIMS": "64,64", + "TVAE_EMBEDDING_DIM": "32", + "TVAE_EPOCHS": "100" + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/runtime_result.json b/timecost/n3/tvae/tvae-n3-20260504_175727/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..4e7dca245a4b8faedc34e16679bc9c6526dd056b --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "run_id": "tvae-n3-20260504_175727", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:57:27", + "ended_at": "2026-05-04T17:57:58", + "duration_sec": 30.698 + }, + "generate": { + "started_at": "2026-05-04T17:57:58", + "ended_at": "2026-05-04T17:58:03", + "duration_sec": 5.076 + } + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/staged_features.json b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/test.csv b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/train.csv b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/val.csv b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/staged/tvae/adapter_report.json b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..932bdd965ac25575b89aacc386e5b63da77cd901 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + 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b/timecost/n3/tvae/tvae-n3-20260504_175727/staged/tvae/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175727/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv b/timecost/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv new file mode 100644 index 0000000000000000000000000000000000000000..0a57c670225b053fefa39b1041779c74e85b01fd --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/tvae-n3-3918-20260504_175758.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dbe1d4fdb0a9c6941467d3a92e4d25426d59c01623a0623cfcdef6d4fe7f84b9 +size 820357 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json b/timecost/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/_tvae_generate.py b/timecost/n3/tvae/tvae-n3-20260504_175816/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..24ca6e385d540d624017847f47ba243f2999bd56 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/tvae-n3-3918-20260504_175903.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/tvae-n3-3918-20260504_175903.csv") diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/_tvae_train.py b/timecost/n3/tvae/tvae-n3-20260504_175816/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6a0e13d23bea670f006bdee5b27f5ed16ac0aace --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/gen_20260504_175903.log b/timecost/n3/tvae/tvae-n3-20260504_175816/gen_20260504_175903.log new file mode 100644 index 0000000000000000000000000000000000000000..c9e9969cc7e63477a8419ee427d1b2f909f6c489 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/gen_20260504_175903.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ef6bcb79b68ef381cc6167555080df374451239d37735e6378da6ec9d372ef7 +size 404 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/input_snapshot.json b/timecost/n3/tvae/tvae-n3-20260504_175816/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddd4c8dc6c3d918927aad5bcb5dbb27f78068fe --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/tvae/model_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/public/train.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/public/staged_features.json", + "target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "256", + "TVAE_COMPRESS_DIMS": "128,128", + "TVAE_DECOMPRESS_DIMS": "128,128", + "TVAE_EMBEDDING_DIM": "64", + "TVAE_EPOCHS": "200" + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/runtime_result.json b/timecost/n3/tvae/tvae-n3-20260504_175816/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..159c4a5fd0a1edff1eac689ed7375964f539a203 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "run_id": "tvae-n3-20260504_175816", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/tvae-n3-3918-20260504_175903.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:58:16", + "ended_at": "2026-05-04T17:59:03", + "duration_sec": 46.905 + }, + "generate": { + "started_at": "2026-05-04T17:59:03", + "ended_at": "2026-05-04T17:59:08", + "duration_sec": 5.167 + } + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/staged/public/staged_features.json b/timecost/n3/tvae/tvae-n3-20260504_175816/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/staged/public/test.csv b/timecost/n3/tvae/tvae-n3-20260504_175816/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/staged/public/test.csv @@ -0,0 +1,3 @@ +version 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b/timecost/n3/tvae/tvae-n3-20260504_175816/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/staged/tvae/model_input_manifest.json b/timecost/n3/tvae/tvae-n3-20260504_175816/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7016c089e285e0a36a9663608cee98e2ce64f3ca --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/staged/tvae/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": 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"41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175816/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/tvae-n3-3918-20260504_175903.csv b/timecost/n3/tvae/tvae-n3-20260504_175816/tvae-n3-3918-20260504_175903.csv new file mode 100644 index 0000000000000000000000000000000000000000..c871e4db264e0e2b15d05c0dfec0c9dd73881acb --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/tvae-n3-3918-20260504_175903.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34093f1f385b8ca62254520bd2e29c6c778ad9d30d1dd4273306d4abe54ea110 +size 820253 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175816/tvae_metadata.json b/timecost/n3/tvae/tvae-n3-20260504_175816/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175816/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/_tvae_generate.py b/timecost/n3/tvae/tvae-n3-20260504_175920/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b7c5471b4f309613a61d50a47fc13bb5ba9a7bf0 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv") diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/_tvae_train.py b/timecost/n3/tvae/tvae-n3-20260504_175920/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..67d99ebd85e50e12aca0f56191b29ca86c4abf8f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log b/timecost/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log new file mode 100644 index 0000000000000000000000000000000000000000..6c444db1593eca9af73a52c23d9523948af9cfb9 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3258505060bad9eddfae5d88bef19d324c0af15ebd939152edb79eb981d1a7f2 +size 404 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/input_snapshot.json b/timecost/n3/tvae/tvae-n3-20260504_175920/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddd4c8dc6c3d918927aad5bcb5dbb27f78068fe --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": 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a/timecost/n3/tvae/tvae-n3-20260504_175920/models_300epochs/train_20260504_175920.log b/timecost/n3/tvae/tvae-n3-20260504_175920/models_300epochs/train_20260504_175920.log new file mode 100644 index 0000000000000000000000000000000000000000..bb635666427be4bc6541bc02b0980803dc26081b --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/models_300epochs/train_20260504_175920.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3246fcae64caa9d1f197b3c707eb5cbb869dae2b365054835064835b12f15cc4 +size 621 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt b/timecost/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..293036634de2b5a65e841089d896f1a4976dacbe --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv" + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/public_gate/staged_input_manifest.json b/timecost/n3/tvae/tvae-n3-20260504_175920/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7560f360e5cd3e3d8d65c17ab0b0b7c6073a67a1 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/public_gate/staged_input_manifest.json @@ -0,0 +1,264 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/public/train.csv", + "val_csv": 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a/timecost/n3/tvae/tvae-n3-20260504_175920/run_config.json b/timecost/n3/tvae/tvae-n3-20260504_175920/run_config.json new file mode 100644 index 0000000000000000000000000000000000000000..34e80e8598db7b9fc46661a8ead04a98458a2905 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/run_config.json @@ -0,0 +1,45 @@ +{ + "schema_version": 1, + "recorded_at": "2026-05-04T17:59:20", + "dataset_id": "n3", + "model": "tvae", + "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920", + "dataset_source_requested": "new", + "dataset_source_resolved": "new", + "cli_args": { + "model": "tvae", + "dataset": "n3", + "dataset_source": "new", + "train": true, + "generate": true, + "num_rows": 0, + "epochs": null, + "output_dir": null, + "model_dir": null, + "work_dir": null, + "resume": false, + "no_stats": false + }, + "resolved": { + "num_rows": 3918, + "model_path": 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/public/staged_features.json", + "target_column": "quality", + "task_type": "classification" + }, + "env_overrides": { + "BENCHMARK_TVAE_GPUS": "device=3", + "TVAE_BATCH_SIZE": "500", + "TVAE_COMPRESS_DIMS": "256,256", + "TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "300" + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/runtime_result.json b/timecost/n3/tvae/tvae-n3-20260504_175920/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..49fc9d14f7eebd1186ffb28e9aedc8a6c3906d77 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "run_id": "tvae-n3-20260504_175920", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/models_300epochs/tvae_300epochs.pt" + }, + "timings": { + "train": { + "started_at": "2026-05-04T17:59:20", + "ended_at": "2026-05-04T18:00:01", + "duration_sec": 40.617 + }, + "generate": { + "started_at": "2026-05-04T18:00:01", + "ended_at": "2026-05-04T18:00:06", + "duration_sec": 5.402 + } + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/staged_features.json b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/test.csv b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/train.csv b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/val.csv b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/staged/tvae/adapter_report.json b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..5e760452fe41abe214bea5ff450a4bdd298ca99f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/tvae/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/tvae/model_input_manifest.json" +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/staged/tvae/adapter_transforms_applied.json b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/staged/tvae/model_input_manifest.json 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"0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_175920/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv b/timecost/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv new file mode 100644 index 0000000000000000000000000000000000000000..10ad6aea130216e24134b4f882c5fe5f1a9f8431 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/tvae-n3-3918-20260504_180001.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8fbb4d91efeeeb1d7eeebb166c9f1c4576bf4e0bea0ff15c6104fdfade300582 +size 820263 diff --git a/timecost/n3/tvae/tvae-n3-20260504_175920/tvae_metadata.json b/timecost/n3/tvae/tvae-n3-20260504_175920/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_175920/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_180018/_tvae_generate.py b/timecost/n3/tvae/tvae-n3-20260504_180018/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4f9cf621d756127ba56d81b5d03a897854c6fb39 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv") diff --git a/timecost/n3/tvae/tvae-n3-20260504_180018/_tvae_train.py b/timecost/n3/tvae/tvae-n3-20260504_180018/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bd01c88cdd31ec29e1fb2cdeff82715de5186d16 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/n3/tvae/tvae-n3-20260504_180018/gen_20260504_180152.log b/timecost/n3/tvae/tvae-n3-20260504_180018/gen_20260504_180152.log new file mode 100644 index 0000000000000000000000000000000000000000..5dc87648379c31a774bd527b872cbf17e45a245c --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/gen_20260504_180152.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2e6ce3290ea52be4c6f716ed2e89479dad3485e76eaf753b8d4a25787114b1e +size 404 diff --git a/timecost/n3/tvae/tvae-n3-20260504_180018/input_snapshot.json b/timecost/n3/tvae/tvae-n3-20260504_180018/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddd4c8dc6c3d918927aad5bcb5dbb27f78068fe --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": 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"TVAE_DECOMPRESS_DIMS": "256,256", + "TVAE_EMBEDDING_DIM": "128", + "TVAE_EPOCHS": "500" + } +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_180018/runtime_result.json b/timecost/n3/tvae/tvae-n3-20260504_180018/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..547029ea7fa287f702ea6ab561a97791e29896a8 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "run_id": "tvae-n3-20260504_180018", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv", + "model_path": 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a/timecost/n3/tvae/tvae-n3-20260504_180018/staged/public/test.csv b/timecost/n3/tvae/tvae-n3-20260504_180018/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/timecost/n3/tvae/tvae-n3-20260504_180018/staged/public/train.csv b/timecost/n3/tvae/tvae-n3-20260504_180018/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git 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0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180018/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv b/timecost/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv new file mode 100644 index 0000000000000000000000000000000000000000..361f8e7dfa48053c9c11f830e964d2d281babccc --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/tvae-n3-3918-20260504_180152.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a7ddf27d7e67c91b0cc4320fac6c059beb9de77910a2c9914535b5db5a19fccc +size 820283 diff --git a/timecost/n3/tvae/tvae-n3-20260504_180018/tvae_metadata.json b/timecost/n3/tvae/tvae-n3-20260504_180018/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180018/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_180209/_tvae_generate.py b/timecost/n3/tvae/tvae-n3-20260504_180209/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e0bb2d7e7d2aefffd85bee311158c71d512b1134 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180209/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/models_300epochs/tvae_300epochs.pt") +total = 3918 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv") diff --git a/timecost/n3/tvae/tvae-n3-20260504_180209/_tvae_train.py b/timecost/n3/tvae/tvae-n3-20260504_180209/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..99aa02f81dd2ad71f400e0409301e5bc93d9ceaf --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180209/_tvae_train.py @@ -0,0 +1,39 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/models_300epochs/tvae_300epochs.pt" +epochs = int(os.environ.get("TVAE_EPOCHS", 300)) +batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500")) +embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128")) +def _parse_dims(name, default): + raw = (os.environ.get(name) or "").strip() + if not raw: + return default + return tuple(int(x.strip()) for x in raw.split(",") if x.strip()) +compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128)) +decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128)) + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}") +model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/n3/tvae/tvae-n3-20260504_180209/gen_20260504_180345.log b/timecost/n3/tvae/tvae-n3-20260504_180209/gen_20260504_180345.log new file mode 100644 index 0000000000000000000000000000000000000000..21c5b503a596f2d9f52385614c1465b59c012652 --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180209/gen_20260504_180345.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2bd05a84f5c2e1e31daf8000e86de8e09158f70a12f44646f86ee65c3f27ab7f +size 404 diff --git a/timecost/n3/tvae/tvae-n3-20260504_180209/input_snapshot.json b/timecost/n3/tvae/tvae-n3-20260504_180209/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..1ddd4c8dc6c3d918927aad5bcb5dbb27f78068fe --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180209/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": 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b/timecost/n3/tvae/tvae-n3-20260504_180209/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180209/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_180209/staged/tvae/model_input_manifest.json b/timecost/n3/tvae/tvae-n3-20260504_180209/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..e7ef2aa9b33d3f5c50fa61424e02c8bf5e16cd5d --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180209/staged/tvae/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "tvae", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 120, + "unique_ratio": 0.030628, + "example_values": [ + "21", + "24", + "41", + "43", + "69" + ] + } + }, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tvae/tvae-n3-20260504_180209/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/timecost/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv b/timecost/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb4001a56167f759b7d62206402096f406caea1e --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180209/tvae-n3-3918-20260504_180345.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c5849ac70605215db2f49695e46e4d45a6af7caa95083a86a1ba1d6ccc8f579 +size 820300 diff --git a/timecost/n3/tvae/tvae-n3-20260504_180209/tvae_metadata.json b/timecost/n3/tvae/tvae-n3-20260504_180209/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..40d513aa1aca4c6f5c1be8afc3d6a1a0c75aa74f --- /dev/null +++ b/timecost/n3/tvae/tvae-n3-20260504_180209/tvae_metadata.json @@ -0,0 +1,52 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ] +} \ No newline at end of file diff --git a/timecost/n6/arf/arf-n6-20260429_031623/_arf_generate.py b/timecost/n6/arf/arf-n6-20260429_031623/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..18f82663588ac8aaeca0a5ea0803aa8645d2d2b7 --- /dev/null +++ b/timecost/n6/arf/arf-n6-20260429_031623/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(6400) +c_csv = "/work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n6' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf-n6-6400-20260429_032002.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf-n6-6400-20260429_032002.csv") diff --git a/timecost/n6/arf/arf-n6-20260429_031623/_arf_train.py b/timecost/n6/arf/arf-n6-20260429_031623/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..70acf11264e3daf7ed1692b92b9b9e07f0264df1 --- /dev/null +++ b/timecost/n6/arf/arf-n6-20260429_031623/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n6/arf/arf-n6-20260429_031623/arf_model.pkl") diff --git a/timecost/n6/arf/arf-n6-20260429_031623/arf-n6-6400-20260429_032002.csv b/timecost/n6/arf/arf-n6-20260429_031623/arf-n6-6400-20260429_032002.csv new file mode 100644 index 0000000000000000000000000000000000000000..c808deef5ce6504b6dc0df95997e09ce3d3fc223 --- /dev/null +++ b/timecost/n6/arf/arf-n6-20260429_031623/arf-n6-6400-20260429_032002.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ae8ee5b84bb67e1265ade5b331c64864e14c6797e803082a475317806cd8009 +size 2028017 diff --git a/timecost/n6/arf/arf-n6-20260429_031623/arf_model.pkl b/timecost/n6/arf/arf-n6-20260429_031623/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b3b55d0f10d40bb2165f214c354ca00d8c6d8146 --- /dev/null +++ b/timecost/n6/arf/arf-n6-20260429_031623/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:543ad3fe9ba90e9e706eb17541a6a2f053c993fd4d9191b2aadbc3f4ffe02315 +size 43688366 diff --git a/timecost/n6/arf/arf-n6-20260429_031623/gen_20260429_032002.log b/timecost/n6/arf/arf-n6-20260429_031623/gen_20260429_032002.log new file mode 100644 index 0000000000000000000000000000000000000000..78f4864f790db781da486007bee0654dd1fcc22d --- /dev/null +++ b/timecost/n6/arf/arf-n6-20260429_031623/gen_20260429_032002.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3cb012c75ca4ed9a4a060e80e72256a563c8fe672017de96a2d649ccb3e57858 +size 719 diff --git a/timecost/n6/arf/arf-n6-20260429_031623/input_snapshot.json b/timecost/n6/arf/arf-n6-20260429_031623/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0ba067dbcb726fa4462b49a39eb294de6980bc91 --- /dev/null +++ b/timecost/n6/arf/arf-n6-20260429_031623/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n6", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n6/n6-train.csv", + "exists": true, + "size": 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b/timecost/n6/arf/arf-n6-20260429_031623/train_20260429_031623.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:88df5cd7df34aa12f7435d49946537a3792b88550158af905d44115fd0b43118 +size 781 diff --git a/timecost/n6/bayesnet/bayesnet-n6-20260429_032012/_bayesnet_generate.py b/timecost/n6/bayesnet/bayesnet-n6-20260429_032012/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..40bd17bfe90fc9a2f3dd2d57462a6d684c3d4368 --- /dev/null +++ b/timecost/n6/bayesnet/bayesnet-n6-20260429_032012/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(6400) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet-n6-6400-20260429_032030.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet-n6-6400-20260429_032030.csv") diff --git a/timecost/n6/bayesnet/bayesnet-n6-20260429_032012/_bayesnet_train.py b/timecost/n6/bayesnet/bayesnet-n6-20260429_032012/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ba16efc0753f1834f413c3c713a42fe350fce6d5 --- /dev/null +++ b/timecost/n6/bayesnet/bayesnet-n6-20260429_032012/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n6/bayesnet/bayesnet-n6-20260429_032012/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet_model.pkl") diff --git a/timecost/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet-n6-6400-20260429_032030.csv b/timecost/n6/bayesnet/bayesnet-n6-20260429_032012/bayesnet-n6-6400-20260429_032030.csv new file mode 100644 index 0000000000000000000000000000000000000000..374a4cc35324e831b2c0b89ce486c142891db7dd 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https://git-lfs.github.com/spec/v1 +oid sha256:30e60032716f27f589792c8c227a768b1a7147d9f660b7876fc19ab1e7d107d8 +size 3741 diff --git a/timecost/n6/ctgan/ctgan-n6-20260429_032130/_ctgan_generate.py b/timecost/n6/ctgan/ctgan-n6-20260429_032130/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..94445593fa71679e33074136de28d74632dba0d9 --- /dev/null +++ b/timecost/n6/ctgan/ctgan-n6-20260429_032130/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/n6/ctgan/ctgan-n6-20260429_032130/models_300epochs/ctgan_300epochs.pt") +total = 6400 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/n6/ctgan/ctgan-n6-20260429_032130/ctgan-n6-6400-20260429_032501.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/n6/ctgan/ctgan-n6-20260429_032130/ctgan-n6-6400-20260429_032501.csv") \ No newline at end of file diff --git a/timecost/n6/ctgan/ctgan-n6-20260429_032130/ctgan-n6-6400-20260429_032501.csv b/timecost/n6/ctgan/ctgan-n6-20260429_032130/ctgan-n6-6400-20260429_032501.csv new file mode 100644 index 0000000000000000000000000000000000000000..bee2fcf145c7b2814bb3eecf99c49b9a0d3d6486 --- /dev/null +++ b/timecost/n6/ctgan/ctgan-n6-20260429_032130/ctgan-n6-6400-20260429_032501.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5870df1fb5528c0bcacce5504981701b6ee9917461b13e3d3d56e61096cc8f54 +size 354294 diff --git 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0000000000000000000000000000000000000000..f3929e0ab2dd9b4f1f334f582838a2a963bc2f25 --- /dev/null +++ b/timecost/n6/forestdiffusion/forest-n6-20260430_054702/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_054702/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(6400)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_054702/forest-n6-6400-20260430_054812.csv', index=False) +print("saved", len(df)) diff --git a/timecost/n6/forestdiffusion/forest-n6-20260430_054702/_fd_meta_host.json b/timecost/n6/forestdiffusion/forest-n6-20260430_054702/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..8a61d40675d38a9753057b7a85db975e5e743cce --- /dev/null +++ b/timecost/n6/forestdiffusion/forest-n6-20260430_054702/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "X11", "X12", "X13", "X14", "X15", "X16", "y"], "cat_indexes": []} \ No newline at end of file diff --git a/timecost/n6/forestdiffusion/forest-n6-20260430_054702/_fd_train.py b/timecost/n6/forestdiffusion/forest-n6-20260430_054702/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..adb4d2ba4d7ab122c3aa8b113602e6319c387a73 --- /dev/null +++ b/timecost/n6/forestdiffusion/forest-n6-20260430_054702/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_054702/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_054702/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_054702/forestdiffusion_model.joblib') diff --git a/timecost/n6/forestdiffusion/forest-n6-20260430_054702/forest-n6-6400-20260430_054812.csv b/timecost/n6/forestdiffusion/forest-n6-20260430_054702/forest-n6-6400-20260430_054812.csv new file mode 100644 index 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0000000000000000000000000000000000000000..3e8c4e6425d2c57f8aa1bf14b8fe50d261bf5d4e --- /dev/null +++ b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e4ca71ef754aec60001d4541bfbd80348f65da032f4a0588c700c98f612c952 +size 435328 diff --git a/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_gen.py b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..bc02dc538b3647b7dd9be2bf73e297d50cfe7be7 --- /dev/null +++ b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_055844/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(6400)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_055844/forest-n6-6400-20260430_055957.csv', index=False) +print("saved", len(df)) diff --git a/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_meta_host.json b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..8a61d40675d38a9753057b7a85db975e5e743cce --- /dev/null +++ b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "X11", "X12", "X13", "X14", "X15", "X16", "y"], "cat_indexes": []} \ No newline at end of file diff --git a/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_train.py b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fc1af95a18401d8035aed08ede7e8d62f4ba58ce --- /dev/null +++ b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_055844/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_055844/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n6/forestdiffusion/forest-n6-20260430_055844/forestdiffusion_model.joblib') diff --git a/timecost/n6/forestdiffusion/forest-n6-20260430_055844/forest-n6-6400-20260430_055957.csv b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/forest-n6-6400-20260430_055957.csv new file mode 100644 index 0000000000000000000000000000000000000000..30e889e9dcdf5b8bfd6aa30eea5ec5aa1c0169ae --- /dev/null +++ b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/forest-n6-6400-20260430_055957.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d300de070eb8fc5a2e8c48b57f5cd5d4baa91d69f2938b850d383521042338dc +size 2050310 diff --git a/timecost/n6/forestdiffusion/forest-n6-20260430_055844/forestdiffusion_model.joblib b/timecost/n6/forestdiffusion/forest-n6-20260430_055844/forestdiffusion_model.joblib new file mode 100644 index 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b/timecost/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-Benchmark-trainonly-v1/n6/tabbyflow/tabbyflow-n6-20260429_032517/tabular_bundle/pipeline_ds" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_ds/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(6400)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n6/tabbyflow/tabbyflow-n6-20260429_032517/tabbyflow-n6-6400-20260429_033823.csv") diff --git a/timecost/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_train.py b/timecost/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fab5bc3ce3e88239d9f54eeffac04e64fe9418d8 --- /dev/null +++ b/timecost/n6/tabbyflow/tabbyflow-n6-20260429_032517/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-Benchmark-trainonly-v1/n6/tabbyflow/tabbyflow-n6-20260429_032517/tabular_bundle/pipeline_ds" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/timecost/n6/tabbyflow/tabbyflow-n6-20260429_032517/gen_20260429_033823.log b/timecost/n6/tabbyflow/tabbyflow-n6-20260429_032517/gen_20260429_033823.log new 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0000000000000000000000000000000000000000..5d3a36d1dbdabbd27854afaaa6867e941bff28b8 --- /dev/null +++ b/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 6400 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/config_sample_20260429_052109_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/tabddpm-n6-6400-20260429_052109.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/tabddpm-n6-6400-20260429_052109.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_train.py b/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..03a88ed2945f1661f4e5483b339d21755b4ea092 --- /dev/null +++ b/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/config.toml b/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..00ced7ba23a5d60ae6146e7c4005a5f7649c5148 --- /dev/null +++ b/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/data" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/config_sample_20260429_052109_r0.toml b/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/config_sample_20260429_052109_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..449b173424c65c5038bd791dd86044e5a49c6262 --- /dev/null +++ b/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/config_sample_20260429_052109_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n6/tabddpm/tabddpm-n6-20260429_052038/data" +model_type = "mlp" +num_numerical_features = 16 +device = "cuda:0" + +[model_params] +d_in = 16 +num_classes = 4 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 6400 +batch_size = 1000 +seed = 0 diff --git a/timecost/n6/tabddpm/tabddpm-n6-20260429_052038/data/X_num_train.npy 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sha256:21c37f6a1a0f20f1be0cc8a5c92d98655d87cd48206c29deb90c05d35105611f +size 1066 diff --git a/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_gen.py b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..1908fdbb327fcc5da55f1c86215db91bc4f31217 --- /dev/null +++ b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n6" +src = r"/work/output-Benchmark-trainonly-v1/n6/tabdiff/tabdiff-n6-20260429_041029/tabular_bundle/pipeline_n6" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n6/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(6400)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n6/tabdiff/tabdiff-n6-20260429_041029/tabdiff-n6-6400-20260429_042528.csv") diff --git a/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_train.py b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..aff1ad2b168d6a006f405c30b75b96ac640db953 --- /dev/null +++ b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n6" +src = r"/work/output-Benchmark-trainonly-v1/n6/tabdiff/tabdiff-n6-20260429_041029/tabular_bundle/pipeline_n6" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/gen_20260429_042528.log b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/gen_20260429_042528.log new file mode 100644 index 0000000000000000000000000000000000000000..2957adae4706b2370e748dd451182f210409f717 --- /dev/null +++ b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/gen_20260429_042528.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5a44e6efc4a2b69c9c2e9c51e4b2922ba6180b8973a80b5760f74fbcf17effae +size 4617 diff --git a/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/input_snapshot.json b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e7442d7a7b754df9e3f8cc34ca258aaf5d2c1ae2 --- /dev/null +++ b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/input_snapshot.json 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b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/train_20260429_041029.log new file mode 100644 index 0000000000000000000000000000000000000000..054fabc8014f7cc36901535ac28095a352f26d75 --- /dev/null +++ b/timecost/n6/tabdiff/tabdiff-n6-20260429_041029/train_20260429_041029.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:858a827540ba84d0e10911a5a911e4c50b57c41117336b42dbdc3590b52b3da7 +size 389062 diff --git a/timecost/n6/tabpfgen/tabpfgen-n6-20260429_060202/_tabpfgen_generate.py b/timecost/n6/tabpfgen/tabpfgen-n6-20260429_060202/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b55df02efa0425b202deef4aa28542251d2888fe --- /dev/null +++ b/timecost/n6/tabpfgen/tabpfgen-n6-20260429_060202/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n6/tabpfgen/tabpfgen-n6-20260429_060202/staged/public/train.csv") +target_col = "y" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(6400) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/n6/tabpfgen/tabpfgen-n6-20260429_060202/tabpfgen-n6-6400-20260429_064819.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n6/tabpfgen/tabpfgen-n6-20260429_060202/tabpfgen-n6-6400-20260429_064819.csv") diff --git a/timecost/n6/tabpfgen/tabpfgen-n6-20260429_060202/gen_20260429_060202.log b/timecost/n6/tabpfgen/tabpfgen-n6-20260429_060202/gen_20260429_060202.log new file mode 100644 index 0000000000000000000000000000000000000000..593fc2b9bc06af4f5ae64828565285a3d252a13e --- /dev/null +++ b/timecost/n6/tabpfgen/tabpfgen-n6-20260429_060202/gen_20260429_060202.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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https://git-lfs.github.com/spec/v1 +oid sha256:8e80a1cd11cf3475fda3886f822166c60419b46ff3e9a940b60da4ad8b9c8ce2 +size 595 diff --git a/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_sample.py b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..75373f24f10188cb46905d2b754baae28714e5f7 --- /dev/null +++ b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n6/tabsyn/tabsyn-n6-20260429_052136" +dataname = "tabsyn_n6" +output_csv = "/work/output-Benchmark-trainonly-v1/n6/tabsyn/tabsyn-n6-20260429_052136/tabsyn-n6-6400-20260429_060149.csv" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Ensure data symlink exists +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +print(f"[TabSyn] Sampling 6400 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_train.py b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..08044c22b6890ca9db858fbf146b52b583c7e5e9 --- /dev/null +++ b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/n6/tabsyn/tabsyn-n6-20260429_052136" +dataname = "tabsyn_n6" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/data/tabsyn_n6/X_cat_test.npy b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/data/tabsyn_n6/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..4b30720bcfa50b549dc5cd917882157c1d9aa60b --- /dev/null +++ b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/data/tabsyn_n6/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfbe32acad77c018301c459c8995e77626255566f0e6b962feddffd7b38c148d +size 128 diff --git a/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/data/tabsyn_n6/X_cat_train.npy b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/data/tabsyn_n6/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..4b30720bcfa50b549dc5cd917882157c1d9aa60b --- /dev/null +++ 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a/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/train_20260429_052136.log b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/train_20260429_052136.log new file mode 100644 index 0000000000000000000000000000000000000000..44cb7dbc876e1abdec0afe2b28b315fb4af706b2 --- /dev/null +++ b/timecost/n6/tabsyn/tabsyn-n6-20260429_052136/train_20260429_052136.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:972507424226fc6185aeeec4344d8d8b482886c49f06bdbd33f8ddd493887ad8 +size 2347813 diff --git a/timecost/n6/tvae/tvae-n6-20260429_031938/_tvae_generate.py b/timecost/n6/tvae/tvae-n6-20260429_031938/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..13d46b511588db3010384ffa8edc99b6969f0641 --- /dev/null +++ b/timecost/n6/tvae/tvae-n6-20260429_031938/_tvae_generate.py @@ -0,0 +1,23 @@ +import os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.tvae import TVAE +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +model = TVAE.load("/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/models_300epochs/tvae_300epochs.pt") +total = 6400 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/tvae-n6-6400-20260429_032109.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/tvae-n6-6400-20260429_032109.csv") diff --git a/timecost/n6/tvae/tvae-n6-20260429_031938/_tvae_train.py b/timecost/n6/tvae/tvae-n6-20260429_031938/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a0ba5846da17d7ab5065a35f8b6e78c234c97ed1 --- /dev/null +++ b/timecost/n6/tvae/tvae-n6-20260429_031938/_tvae_train.py @@ -0,0 +1,30 @@ +import json, os, sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env +apply_parallel_cap_from_env() +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +# Keep transform stage parallelism bounded for stability on shared host. +os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8") +os.environ.setdefault("OPENBLAS_NUM_THREADS", "8") +os.environ.setdefault("MKL_NUM_THREADS", "8") +_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip() +if _nj: + print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj) +else: + print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)") +print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", ""))) + +csv_path = "/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/staged/public/train.csv" +meta_path = "/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/tvae_metadata.json" +save_path = "/work/output-Benchmark-trainonly-v1/n6/tvae/tvae-n6-20260429_031938/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/timecost/n6/tvae/tvae-n6-20260429_031938/gen_20260429_032109.log 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