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sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/SynthData0523/main/n3/tabdiff/tabdiff-n3-20260501_175353/train_20260501_175353.log b/SynthData0523/main/n3/tabdiff/tabdiff-n3-20260501_175353/train_20260501_175353.log new file mode 100644 index 0000000000000000000000000000000000000000..df10c37193a38e053d07c80622b7227d4213517f --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/_tabpfgen_generate.py b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c8ade1fa83cb3e83789abaa5dd376381b18ae5df --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/gen_20260501_005212.log b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/gen_20260501_005212.log new file mode 100644 index 0000000000000000000000000000000000000000..349d4af44544d58c87db4e1cf03ceb3b413eacc5 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/input_snapshot.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..2df53dbad689923eb0692c0029f1d32d625f8ce4 --- /dev/null +++ b/SynthData0523/main/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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"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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of file diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/public_gate_report.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..93b97319f739e39079d6c6549fd2020f8a6dac25 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/staged_input_manifest.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4d16af30bb8d718cd63e253f3b6b3ef0e8bad2cc --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/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-n3-20260501_005212/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/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", + 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"unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/runtime_result.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..b40d2e985607c070d321c3d4f640c5fa91d9e825 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "tabpfgen", + "run_id": "tabpfgen-n3-20260501_005212", + "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-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212" + }, + "timings": { + "train": { + "started_at": "2026-05-01T00:52:12", + "ended_at": "2026-05-01T00:52:12", + "duration_sec": 0.009 + }, + "generate": { + "started_at": "2026-05-01T00:52:12", + "ended_at": "2026-05-01T00:52:33", + "duration_sec": 21.267 + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/staged_features.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/test.csv b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/train.csv b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/val.csv b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/adapter_report.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..91c08fe1df342bb7d3302bc3d0f88fe494353964 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/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-n3-20260501_005212/staged/tabpfgen/model_input_manifest.json" +} \ No newline at end of file diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/adapter_transforms_applied.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/model_input_manifest.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0c0fc3fb109d265584a282c065ada476cee9c33e --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/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-n3-20260501_005212/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv new file mode 100644 index 0000000000000000000000000000000000000000..fb4bbc9859833a4f4492f0ece0954f39dbfddfa1 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:816d55b52419fb95a39661f1430dccc6b48867e8b9d6077c7a3a4428994edaf2 +size 446015 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen_meta.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..e2268fbaa45e5a02a338e7118394aa6950090894 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260501_005212/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/train_20260501_005212.log b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/train_20260501_005212.log new file mode 100644 index 0000000000000000000000000000000000000000..251655c92a85a09a0e32432e7fa14272b152157d --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260501_005212/train_20260501_005212.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a6a67813e3aecb37bb89b938492ceed24a2682e7ece7d6deb751cdc8de26e414 +size 595 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/_tabpfgen_generate.py b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..15d5540aed0e6302fa10912d2702d2233b6b2676 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/gen_20260505_030704.log b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/gen_20260505_030704.log new file mode 100644 index 0000000000000000000000000000000000000000..8de6e15214b0fee5ba9080b620c29fc6c112539b --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/gen_20260505_030704.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e441f54fe359096ba022b627e97722e2ae3740ffb72e9d3af6ff61fcb1851fc +size 12153 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/input_snapshot.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..79f8760e380f8422a0fda79fbeca112b468a6664 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/input_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b79a5fd7a2461408834df88eba8c103a1bb8680b2c1d0bbfd5b13aeb4cb9021a +size 1347 diff --git 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0000000000000000000000000000000000000000..6f3aaa01a627a955d2022165805d2786c0da78ba --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/adapter_transforms_applied.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 +size 2 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/model_input_manifest.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..16ca3e5eb7bb5bf14021d241390bca9de0620555 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/staged/tabpfgen/model_input_manifest.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9403a7f3c46f8cf19427579d24184383c77298a2a6f4c8b819d73afafb3e86ec +size 6714 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen-n3-3918-20260505_030704.csv new file mode 100644 index 0000000000000000000000000000000000000000..467648433795ba2872a7cf0946bc14085989490e --- /dev/null +++ b/SynthData0523/main/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:ac8c688aaba89b40c814343c37ef776800c31e9cc9431430b36fbcb8399e9689 +size 445891 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen_meta.json b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..2b896d239f91993c40e942d0f4c87b8850a6b064 --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/tabpfgen_meta.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4ee1ec53562ce6b03193d2e8eae2f63483a46444763c56f920f582a6ba71a4e +size 447 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/train_20260505_030704.log b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030703/train_20260505_030704.log new file mode 100644 index 0000000000000000000000000000000000000000..bbb19ccc48df6b982cbd4a06021a975ee8a4c85e --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030933/_tabpfgen_generate.py b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030933/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c8a5ff1ec27cd1a49e89fa91a0cbe66f97e4cc23 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030933/gen_20260505_030933.log b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_030933/gen_20260505_030933.log new file mode 100644 index 0000000000000000000000000000000000000000..b78a1c20d0db8a062638cd3ff71a3eb8222211a5 --- /dev/null +++ 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0000000000000000000000000000000000000000..bc61015a67e2f46e55ceafdb46051850c15de356 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031200/_tabpfgen_generate.py b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031200/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d07fb2a60c4a359dc9ba07b26c2e82bd3ddccb4 --- /dev/null +++ b/SynthData0523/main/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 {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabpfgen/tabpfgen-n3-20260505_031200/tabpfgen-n3-3918-20260505_031200.csv") diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031200/gen_20260505_031200.log b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031200/gen_20260505_031200.log new file mode 100644 index 0000000000000000000000000000000000000000..f8e63782055f608eb82d76676aaf364cfe783c1a --- /dev/null +++ b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031200/gen_20260505_031200.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:125243ca9824a93e9ac5a1d39499d2b6edc7020db63e1fc970c343623e98b6f1 +size 6595 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031200/input_snapshot.json 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sha256:18994d1a15bc7eb4fdaa75fa42d160430c7255623cfe8cb04b79006e4883b4b1 +size 595 diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031328/_tabpfgen_generate.py b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031328/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e2c2d0ecd70717fd7cce358679394d94fc519401 --- /dev/null +++ b/SynthData0523/main/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 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0000000000000000000000000000000000000000..97bf83a2a689f12cf5fc7191f6b56cdf6d98eadf --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031455/gen_20260505_031455.log b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031455/gen_20260505_031455.log new file mode 100644 index 0000000000000000000000000000000000000000..a72b4a73b597b85444a96e3631082ff5b337ab8b 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diff --git a/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031455/train_20260505_031455.log b/SynthData0523/main/n3/tabpfgen/tabpfgen-n3-20260505_031455/train_20260505_031455.log new file mode 100644 index 0000000000000000000000000000000000000000..55b138a16e1bac82b359117c064947760741e214 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_sample.py b/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..749894d8a508cae7402d8a4bb55efc31398e5e78 --- /dev/null +++ b/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/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/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_train.py b/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ed08fcb958c5edfd8a5b0c44967e0ef6c676d14e --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/data/tabsyn_n3/X_cat_test.npy b/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/data/tabsyn_n3/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..24e2b0be40b07b6e79f803a82e6e38723e462320 --- 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--git a/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/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", + 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index 0000000000000000000000000000000000000000..127847b9bcea895c6dfe08c3d56f231c5e71ff42 --- /dev/null +++ b/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/tabsyn-n3-3918-20260501_004440.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7d7a6688a47707a19005606e602ddbd32dc9a2890f2a3beb086c80f3a70e5ba0 +size 337885 diff --git a/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/train_20260501_002047.log b/SynthData0523/main/n3/tabsyn/tabsyn-n3-20260501_002047/train_20260501_002047.log new file mode 100644 index 0000000000000000000000000000000000000000..3cb0016df552216453e11ecaa99f4b9591a1d2e1 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/_tvae_generate.py b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..52c562c2500b3e0dc70ac69e40dcd0b563d31d93 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/_tvae_train.py b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9ffb91d2f7992ec86bc3302a687cd09505dfa5fa --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/gen_20260501_060403.log b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/gen_20260501_060403.log new file mode 100644 index 0000000000000000000000000000000000000000..5f22c71123057e3a567cc4b56bec9e252719dfc7 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/gen_20260501_060403.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b805205fe0670464d99d13c058266b9491632b06bd7170e1e7cad7c460d9a47 +size 404 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/input_snapshot.json b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/input_snapshot.json new file mode 100644 index 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a/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/adapter_report.json b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..181a5a10dbf4bab40a71cab59e7ea9306d081231 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/adapter_report.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c208d6ef3e2c4e222494be91683f449002dccdb4cfe29c57b36807d891b2a32c +size 312 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/adapter_transforms_applied.json b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..6f3aaa01a627a955d2022165805d2786c0da78ba --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 +size 2 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/model_input_manifest.json b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ea6631fd99f46dfbcec128cf55f751cfb15344ed --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/staged/tvae/model_input_manifest.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f3082ad3c219f7a6451e1ad3027d053f7a4c153fbbba6102c81147f542c2cb6 +size 6662 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/tvae-n3-3918-20260501_060403.csv b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/tvae-n3-3918-20260501_060403.csv new file mode 100644 index 0000000000000000000000000000000000000000..91aaffcaa6b65fdd740aac4db157768c585b4112 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/tvae-n3-3918-20260501_060403.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:68d4738c0d73ccb5e6e8ff0595a6f0b4b19a83b6696fd24ff768b0090bd0d49c +size 820086 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/tvae_metadata.json b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7767b4ed10004391de75050c3351cda8f57271c2 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260501_060320/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed0fccab4b7468f519203802e4215d32d4b6dde84f2294dabfe9040e3af45d41 +size 851 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/_tvae_generate.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c7b9f5dbe7772cae78842d3e657dde8a6274ea8e --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/_tvae_train.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7c7be901c03a6afbdafae5c9fa0fdb9cbe3a66b9 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/gen_20260504_172901.log b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/gen_20260504_172901.log new file mode 100644 index 0000000000000000000000000000000000000000..9f4c8df978304685fee287c97778601fa7ccd326 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/input_snapshot.json b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..6819967a6456c6177b39eacad5831ad24c8e9615 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/input_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2a29ef4cbdff3c5c3f19d4f9de3c9c7f38fdfdc2701f2dc5155c231901f0cc8 +size 1343 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/models_300epochs/train_20260504_172819.log b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/models_300epochs/train_20260504_172819.log new file mode 100644 index 0000000000000000000000000000000000000000..c9fda6dd36749071d1d84382f79794a3e6b5a34e --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..57bfd44f30cf1bf03a0a4d546a64684f68cc62cf --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a4d80176f56c6a53507fe524338ee9d9268f8217d8ce0b7379a6682d2a76be3f +size 640812 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..de2eb975cd4598d89d1f197e208efa0729d167f5 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0d6ea2b78489a6f1d2a4546e12404214ae439c9439a9922bea713ec72b4b12a +size 5698 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/public_gate_report.json b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..d75621d194f263189fa5b229b3636437bcf1cd1b --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/public_gate_report.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0ef01a386dd32e531a00a2fe40df2aeed2cbeace8b0beab6956a3accd027a93 +size 914 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/staged_input_manifest.json b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0a9e66a6f6087068bd47594dafcab5827c7f9fe8 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/public_gate/staged_input_manifest.json @@ -0,0 +1,3 @@ +version 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b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/public/train.csv b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/public/val.csv b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/adapter_report.json b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..d250343fae94413880340f443e46842e79836dfb --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/adapter_report.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:aefb490f6b28fd23973db44dcb17f369e6d83456f54fde7411cc024093044b6a +size 312 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/adapter_transforms_applied.json b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..6f3aaa01a627a955d2022165805d2786c0da78ba --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/adapter_transforms_applied.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 +size 2 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/model_input_manifest.json b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ce3c00a2ca7cd8d74e2dc0d27eab1e5b8688ef89 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/staged/tvae/model_input_manifest.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f81d0734535c906a7e8a47a6efa839dee680092a45776ddc584970204969310 +size 6662 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/tvae-n3-3918-20260504_172901.csv new file mode 100644 index 0000000000000000000000000000000000000000..ce98bd29f56845d8d881c8f81d574afb9ea1db39 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7767b4ed10004391de75050c3351cda8f57271c2 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_172819/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed0fccab4b7468f519203802e4215d32d4b6dde84f2294dabfe9040e3af45d41 +size 851 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_175727/_tvae_generate.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175727/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..4f19e5323617cc73049297e90dab025fa598fb8d --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_175727/_tvae_train.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175727/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5dd280a8b9572c97a00b926438610b5dcbd8f601 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_175727/gen_20260504_175758.log b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175727/gen_20260504_175758.log new file mode 100644 index 0000000000000000000000000000000000000000..be2199d29a34a5d38935b76a6d7a38e67909605f --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_175727/input_snapshot.json 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0000000000000000000000000000000000000000..7767b4ed10004391de75050c3351cda8f57271c2 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175727/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed0fccab4b7468f519203802e4215d32d4b6dde84f2294dabfe9040e3af45d41 +size 851 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_175816/_tvae_generate.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175816/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..24ca6e385d540d624017847f47ba243f2999bd56 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_175816/_tvae_train.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175816/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6a0e13d23bea670f006bdee5b27f5ed16ac0aace --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_175816/gen_20260504_175903.log b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175816/gen_20260504_175903.log new file mode 100644 index 0000000000000000000000000000000000000000..c9e9969cc7e63477a8419ee427d1b2f909f6c489 --- /dev/null +++ b/SynthData0523/main/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 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b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175816/tvae_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7767b4ed10004391de75050c3351cda8f57271c2 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175816/tvae_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed0fccab4b7468f519203802e4215d32d4b6dde84f2294dabfe9040e3af45d41 +size 851 diff --git a/SynthData0523/main/n3/tvae/tvae-n3-20260504_175920/_tvae_generate.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175920/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b7c5471b4f309613a61d50a47fc13bb5ba9a7bf0 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_175920/_tvae_train.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175920/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..67d99ebd85e50e12aca0f56191b29ca86c4abf8f --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log new file mode 100644 index 0000000000000000000000000000000000000000..6c444db1593eca9af73a52c23d9523948af9cfb9 --- /dev/null +++ b/SynthData0523/main/n3/tvae/tvae-n3-20260504_175920/gen_20260504_180001.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+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/SynthData0523/main/n3/tvae/tvae-n3-20260504_180018/_tvae_train.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_180018/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bd01c88cdd31ec29e1fb2cdeff82715de5186d16 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_180018/gen_20260504_180152.log b/SynthData0523/main/n3/tvae/tvae-n3-20260504_180018/gen_20260504_180152.log new file mode 100644 index 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b/SynthData0523/main/n3/tvae/tvae-n3-20260504_180209/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e0bb2d7e7d2aefffd85bee311158c71d512b1134 --- /dev/null +++ b/SynthData0523/main/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/SynthData0523/main/n3/tvae/tvae-n3-20260504_180209/_tvae_train.py b/SynthData0523/main/n3/tvae/tvae-n3-20260504_180209/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..99aa02f81dd2ad71f400e0409301e5bc93d9ceaf --- /dev/null +++ b/SynthData0523/main/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 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