diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/_arf_generate.py b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..af250fd064a1c81ec3f4ba8a3dd21ba77321051e --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/_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-c2-20260501_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/syntheticSuccess/c2/arf/arf-c2-20260501_224900/_arf_train.py b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ba95402ff696a85b83e57232405ebd09988cad63 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/arf/arf-c2-20260501_224900/arf-c2-1382-20260501_224905.csv b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/arf-c2-1382-20260501_224905.csv new file mode 100644 index 0000000000000000000000000000000000000000..92d11d02e2729a5d2236986482c133cee53d1736 --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/arf-c2-1382-20260501_224905.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb41bf0eaecc5bd5099060e9e3737d315e593f3597a3ce141644d30fd9472169 +size 41457 diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/arf_model.pkl b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..597fde11184e22babdb14ec8c3309b7f720c6a56 --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:849dba257de251c9427efa37fad4fe089f7ba1d92d998337cae0bc292fd552fb +size 5105326 diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/gen_20260501_224905.log b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/gen_20260501_224905.log new file mode 100644 index 0000000000000000000000000000000000000000..30e1dc736f9f5d469f2937b066c38fa0bac056fa --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/gen_20260501_224905.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:998a24aca2924768417c547edcca17a87b04171f99df28db8d709ad0f2ec513e +size 2615 diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/input_snapshot.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5fa654607f22f68d2aaa5e57809017787b5eb02b --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/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/syntheticSuccess/c2/arf/arf-c2-20260501_224900/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/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/syntheticSuccess/c2/arf/arf-c2-20260501_224900/public_gate/public_gate_report.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/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/syntheticSuccess/c2/arf/arf-c2-20260501_224900/public_gate/staged_input_manifest.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..47d687447aae492ceebb90a272d81aa90055e0fd --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/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-c2-20260501_224900/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/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/syntheticSuccess/c2/arf/arf-c2-20260501_224900/runtime_result.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..5a5a615855f4dd5632bad149d2515b7f8164b2d4 --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "arf", + "run_id": "arf-c2-20260501_224900", + "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-c2-20260501_224900/arf-c2-1382-20260501_224905.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-01T22:49:00", + "ended_at": "2026-05-01T22:49:05", + "duration_sec": 5.405 + }, + "generate": { + "started_at": "2026-05-01T22:49:05", + "ended_at": "2026-05-01T22:49:07", + "duration_sec": 1.903 + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/arf/adapter_report.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..b3ca58757a938bec47d00f56d03266136112173a --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/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-c2-20260501_224900/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/arf/adapter_transforms_applied.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/arf/model_input_manifest.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..674e6fb3539947b2d44632c75c5822cd88e07e38 --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/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-c2-20260501_224900/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260501_224900/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/staged_features.json b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..61783861dd5de501592f76acb0469cf3f3b2622a --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/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/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/test.csv b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/train.csv b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd +size 41565 diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/val.csv b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..af8caf7ee42524188534a3daf32996dc491952d9 --- /dev/null +++ b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01 +size 5176 diff --git a/syntheticSuccess/c2/arf/arf-c2-20260501_224900/train_20260501_224900.log b/syntheticSuccess/c2/arf/arf-c2-20260501_224900/train_20260501_224900.log new file mode 100644 index 0000000000000000000000000000000000000000..439c7ac1ae7936dddaa8e517d1704e9d231dc065 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_generate.py b/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..bc85868eb04b1d284cad15328a73bed2db188403 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_train.py b/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b5233d0b40be8a68f36a1b0f24295a4fce51cf81 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet-c2-1382-20260501_224928.csv b/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet-c2-1382-20260501_224928.csv new file mode 100644 index 0000000000000000000000000000000000000000..fc5d17a270181a71ee29473a8c43adb23eedeabf --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_coltypes.json b/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..dc49694e781f314f19b59a568715784a7cabec24 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl b/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..a41a2cb32a11c469c794a576140511b67c8f7bdb --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/const_cols.json b/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/syntheticSuccess/c2/bayesnet/bayesnet-c2-20260501_224919/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git 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@@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:260745c93bbaaa167bd761bd653a194f7b25b477b3929a24e343741a3c4fa280 +size 38824 diff --git a/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/_fd_gen.py b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..d2b42922010bc7ec07265645dfb0e1d6cf86fca4 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/_fd_meta_host.json b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..be467ac019d08a03e8ba5c0d436f59170db78786 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/_fd_train.py b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c5db79a6adac10e5443c38df7e27a35dfe629363 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/forest-c2-1382-20260501_180507.csv new file mode 100644 index 0000000000000000000000000000000000000000..f27ce02de22935a63bf9c44b36c1ec95abf22b11 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..91b82d488e3ca149c88205a6b539417bae30b551 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/gen_20260501_180507.log b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/gen_20260501_180507.log new file mode 100644 index 0000000000000000000000000000000000000000..468dd563bf18965e5a15365cfbb1fbb8ecf3ca12 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/input_snapshot.json b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..24ba74f272fb2c967af52a4ad4947955f45646ea --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/models_fd/model.joblib b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..91b82d488e3ca149c88205a6b539417bae30b551 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/public_gate_report.json b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4cdcb2a4e28e40d59b17a83e773f622b7636a5 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/staged_input_manifest.json b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..40f47ecbbad7297b9fcd90dfedc1405053e4ced0 --- /dev/null +++ b/syntheticSuccess/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", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/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/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/runtime_result.json b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..9f9aca5fb1e17470b7901b0431f423d151468e76 --- /dev/null +++ b/syntheticSuccess/c2/forestdiffusion/forest-c2-20260501_180312/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "c2", + "model": "forestdiffusion", + "run_id": "forest-c2-20260501_180312", + "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-c2-20260501_180312/forest-c2-1382-20260501_180507.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/forestdiffusion/forest-c2-20260501_180312/models_fd/model.joblib" + }, + "timings": { + "train": { + "started_at": "2026-05-01T18:03:12", + "ended_at": "2026-05-01T18:05:07", + "duration_sec": 115.491 + }, + "generate": { + "started_at": "2026-05-01T18:05:07", + "ended_at": "2026-05-01T18:05:11", + "duration_sec": 3.318 + } + } +} \ No newline at end of file diff --git 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a/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/public/test.csv b/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/public/train.csv b/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..da4f4d55134eebc0e51157d0d467536371db1484 --- /dev/null +++ b/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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diff --git a/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/realtabformer/model_input_manifest.json b/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/realtabformer/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a2a0e091cda360e5f8336060c830cb8ff1da6e3b --- /dev/null +++ b/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/staged/realtabformer/model_input_manifest.json @@ -0,0 +1,151 @@ +{ + "dataset_id": "c2", + "model": "realtabformer", + "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", + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/realtabformer/rtf-c2-20260501_033610/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/realtabformer/rtf-c2-20260501_033610/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/realtabformer/rtf-c2-20260501_033610/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/realtabformer/rtf-c2-20260501_033610/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/realtabformer/rtf-c2-20260501_033610/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/realtabformer/rtf-c2-20260501_033610/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/train_20260501_033610.log b/syntheticSuccess/c2/realtabformer/rtf-c2-20260501_033610/train_20260501_033610.log new file mode 100644 index 0000000000000000000000000000000000000000..0f11a183ee02dcf365160c18b7f456f1aff30d4a --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_gen.py b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..766ebe32a4394cc53ea12a89ad3424187d78632c --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_train.py b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..75ca1b3f716377e162c52a64be03a64f2aa3dc14 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/gen_20260501_053427.log b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/gen_20260501_053427.log new file mode 100644 index 0000000000000000000000000000000000000000..2b04e56f59be7b12b34f7fc644d24c37604f5c64 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/input_snapshot.json b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..89c9d5855c86fb753a25070ff5c19d45d1e2fb9e --- /dev/null +++ b/syntheticSuccess/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": 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a/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/models_tabbyflow/trained.pt b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/models_tabbyflow/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..f063a878c470d9d40f79db79881596e1e497dffd --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..689b1d00d96508e44ba40e58dc4560e19afc2b3b --- /dev/null +++ b/syntheticSuccess/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": { + 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+ "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/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/staged_input_manifest.json b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a9b81ba9787b915e756fab34b82d4ddc1f8d0105 --- /dev/null +++ b/syntheticSuccess/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", + 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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/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/test.csv b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..392a0d7189a34d8f13ecf20a99d64b03230d517c --- /dev/null +++ b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73 +size 5273 diff --git a/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/train.csv b/syntheticSuccess/c2/tabbyflow/tabbyflow-c2-20260501_052813/staged/public/train.csv new file mode 100644 index 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https://git-lfs.github.com/spec/v1 +oid sha256:b0f057a74a0bffb161d24ac00249eb8e9e1d04cb5519e026f539daf6092430aa +size 284038 diff --git a/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_gen.py b/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..6ad02cde0ebb7085c857c9467656a0c3dc2b36e6 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_train.py b/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c639c32804feee4ba0d67da24759d1db2a17f4f6 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/gen_20260501_054253.log b/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/gen_20260501_054253.log new file mode 100644 index 0000000000000000000000000000000000000000..aca2c90c10233d71f8aef41d6b51dd4950215416 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/input_snapshot.json b/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..6d83542090e9720c485d72abe1bb75a800175917 --- /dev/null +++ b/syntheticSuccess/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": 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b/syntheticSuccess/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" + } + } + }, + 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a/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/train_20260501_053602.log b/syntheticSuccess/c2/tabdiff/tabdiff-c2-20260501_053602/train_20260501_053602.log new file mode 100644 index 0000000000000000000000000000000000000000..6de7164078944facc6e0eb3de930d92356d8d9f9 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_sample.py b/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..ecd5da7c2197ba6052bf73dd776c2236f87c2821 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_train.py b/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2c799ff7949ee261da70b3c0f15a1c4cfd25b669 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/X_cat_test.npy b/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/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/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/X_cat_train.npy b/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..6cf4fc75ff61837a47172e845d2942ba68f4ccd1 --- /dev/null +++ b/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/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/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/X_num_test.npy b/syntheticSuccess/c2/tabsyn/tabsyn-c2-20260501_054336/data/tabsyn_c2/X_num_test.npy new file mode 100644 index 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