diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/_arf_generate.py b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e96b4b1cd9495971fc9fe271d54266725c3ad34a --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(3918) +c_csv = "/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'n3' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv") diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/_arf_train.py b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..da3bae6bf5ab0806c16069e5f00dd55deafac2fe --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf_model.pkl") diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv new file mode 100644 index 0000000000000000000000000000000000000000..c13e43168f0bc389492c40106d5226be37884904 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:034d26d9e71f204197db1f262436a3474fe972cc90043a2777d5f7960608352c +size 876973 diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/arf_model.pkl b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b0e07c8ccbbf5dd3712bbb1bbcb052d5062467f6 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8c20fa2967362ef7efcb0a9251ff27d2a011b28bbc6d70d442806a722bfce04b +size 21167791 diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/gen_20260501_225052.log b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/gen_20260501_225052.log new file mode 100644 index 0000000000000000000000000000000000000000..50ae69d00e0b2f1aad567e7b76a60cef754af119 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/gen_20260501_225052.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:144b44334b50030bc8239b616693a40a439cd3c6109ba8a941f7e2e45e0fabba +size 719 diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/input_snapshot.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8f61e094a5a82208890ae5982a105d557698988d --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n3", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-train.csv", + "exists": true, + "size": 211519, + "sha256": "2160a06db2fea02adb0ebb79d8d853bd2544580be301e71f6e8deeb1489a9009" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-val.csv", + "exists": true, + "size": 26499, + "sha256": "d5ccfee07dfa9e7bdee5bb87642b43828e09f74c6cc0266a3d19d9feaa83106e" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n3/n3-test.csv", + "exists": true, + "size": 26676, + "sha256": "6f83aacb0d1747b95e287352a7fff5d9d65a1c127b773852346c68485c4ede47" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_profile.json", + "exists": true, + "size": 5235, + "sha256": "23d2e5904a2ca90cbf8d6e3c75887681721e9b90cc705c31a680c11513d0c46f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n3/n3-dataset_contract_v1.json", + "exists": true, + "size": 6281, + "sha256": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/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, + 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}, + { + "name": "total sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 244, + "unique_ratio": 0.062277, + "example_values": [ + "144", + "99", + "114", + "132", + "160" + ] + } + }, + { + "name": "density", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 839, + "unique_ratio": 0.21414, + "example_values": [ + "0.99304", + "0.9947", + "0.99155", + "0.98975", + "0.99705" + ] + } + }, + { + "name": "pH", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 100, + "unique_ratio": 0.025523, + "example_values": [ + "3.13", + "3.18", + "3.37", + "3.21", + "3.16" + ] + } + }, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/public_gate/public_gate_report.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..93b97319f739e39079d6c6549fd2020f8a6dac25 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/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/syntheticSuccess/n3/arf/arf-n3-20260501_225023/public_gate/staged_input_manifest.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4c6d80dbfb94bf367f92a7b82a91d4ff848f2e7f --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/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/arf/arf-n3-20260501_225023/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/public_gate/public_gate_report.json", + "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, + 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+ "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": 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}, + { + "name": "sulphates", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 78, + "unique_ratio": 0.019908, + "example_values": [ + "0.4", + "0.53", + "0.66", + "0.47", + "0.5" + ] + } + }, + { + "name": "alcohol", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 97, + "unique_ratio": 0.024758, + "example_values": [ + "9.6", + "9.7", + "11.7", + "13.4", + "8.8" + ] + } + }, + { + "name": "quality", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 7, + "unique_ratio": 0.001787, + "example_values": [ + "5", + "6", + "7", + "8", + "3" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/runtime_result.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..565af4f654a17e002bd8377d1111832bf9d9705c --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "arf", + "run_id": "arf-n3-20260501_225023", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf-n3-3918-20260501_225052.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/arf_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-01T22:50:23", + "ended_at": "2026-05-01T22:50:52", + "duration_sec": 28.875 + }, + "generate": { + "started_at": "2026-05-01T22:50:52", + "ended_at": "2026-05-01T22:50:54", + "duration_sec": 2.591 + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_report.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..0378e903f33fcfaa4f0b82a011e2cf31d64f52b6 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_transforms_applied.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/model_input_manifest.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b46735d55419e5f139da6469b04c0161024a8c4a --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/arf/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "arf", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "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/arf/arf-n3-20260501_225023/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/arf/arf-n3-20260501_225023/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/test.csv b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/train.csv b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/val.csv b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/syntheticSuccess/n3/arf/arf-n3-20260501_225023/train_20260501_225023.log b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/train_20260501_225023.log new file mode 100644 index 0000000000000000000000000000000000000000..f68464a938a0665daedb3ba539f3024b0452a832 --- /dev/null +++ b/syntheticSuccess/n3/arf/arf-n3-20260501_225023/train_20260501_225023.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2178ce762ea2f8d576ec23371a0d6569661f9d5ac97736a0c9b02e75c175edf +size 613 diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_generate.py b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..76ecd4acf4cb2ce14551a757d0ac42cc16db4d61 --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(3918) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv") diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_train.py b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6f4775148165f0c3356a0cc2ea96c6b087285084 --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl") diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv new file mode 100644 index 0000000000000000000000000000000000000000..5122af8070a39856aef00fd519e4673134151cf4 --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7eaef9af040116c7fea3c8e5e566ea772624f1e1548c99fa7b800a0deeac3f44 +size 884307 diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_coltypes.json b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..59893d873ed1f60d3bbb17d18abfda70729fe810 --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_coltypes.json @@ -0,0 +1,53 @@ +{ + "columns": [ + { + "name": "fixed acidity", + "type": "continuous" + }, + { + "name": "volatile acidity", + "type": "continuous" + }, + { + "name": "citric acid", + "type": "continuous" + }, + { + "name": "residual sugar", + "type": "continuous" + }, + { + "name": "chlorides", + "type": "continuous" + }, + { + "name": "free sulfur dioxide", + "type": "continuous" + }, + { + "name": "total sulfur dioxide", + "type": "continuous" + }, + { + "name": "density", + "type": "continuous" + }, + { + "name": "pH", + "type": "continuous" + }, + { + "name": "sulphates", + "type": "continuous" + }, + { + "name": "alcohol", + "type": "continuous" + }, + { + "name": "quality", + "type": "continuous" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..6ea0fbeea7caaa2028344865d76bd34aad1eae86 --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6796efb78000c399e81e0fc93d03b1d91f3c5d4aec0d0f61ad13d70100e0c1f5 +size 18294 diff --git 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+++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + 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"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/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/staged_input_manifest.json 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b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/runtime_result.json @@ -0,0 +1,27 @@ +{ + "dataset_id": "n3", + "model": "bayesnet", + "run_id": "bayesnet-n3-20260501_225103", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet-n3-3918-20260501_225112.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/bayesnet_model.pkl" + }, + "timings": { + "train": { + "started_at": "2026-05-01T22:51:04", + "ended_at": "2026-05-01T22:51:12", + "duration_sec": 7.985 + }, + "generate": { + "started_at": "2026-05-01T22:51:12", + "ended_at": "2026-05-01T22:51:17", + "duration_sec": 5.51 + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/adapter_report.json b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..9a235f852d28cbcee972af7cd8cdbc3ee6e85c7f --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/adapter_transforms_applied.json b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/model_input_manifest.json b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6eeff7a2dab3e1965b35b252e5b6ce786a468979 --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,266 @@ +{ + "dataset_id": "n3", + "model": "bayesnet", + "target_column": "quality", + "task_type": "classification", + "column_schema": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + "0.31", + "0.25", + "0.3" + ] + } + }, + { + "name": "residual sugar", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 296, + "unique_ratio": 0.075549, + "example_values": [ + "1", + "4.3", + "2.8", + "13", + "12.9" + ] + } + }, + { + "name": "chlorides", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 147, + "unique_ratio": 0.037519, + "example_values": [ + "0.042", + "0.036", + "0.038", + "0.047", + "0.033" + ] + } + }, + { + "name": "free sulfur dioxide", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/bayesnet/bayesnet-n3-20260501_225103/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/staged_features.json b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..08fe4d766d091d2f2ddba724a16b9509cbe4c390 --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "fixed acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "volatile acidity", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "citric acid", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "residual sugar", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "chlorides", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "free sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "total sulfur dioxide", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "density", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pH", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "sulphates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "alcohol", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "quality", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/test.csv b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..87ca26726fe82ccbaa397919305b043c17eb374e --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7813c8cca73ba4bfc6559215257b341c21e459980ad17ae6ddc809306bc9a0b9 +size 229945 diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/val.csv b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6acf84c76c47093b513c46d5503972a2dff275c0 --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf5979d9675567d9d6642640c9b84fc6c49e492604144723c30f50516c05cee6 +size 28791 diff --git a/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/train_20260501_225104.log b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/train_20260501_225104.log new file mode 100644 index 0000000000000000000000000000000000000000..b5aff868cf2aec0b81c0fc8bfef5f229545ee88e --- /dev/null +++ b/syntheticSuccess/n3/bayesnet/bayesnet-n3-20260501_225103/train_20260501_225104.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:62ccdc6bb8d143b5e6ea6deccbac97f29a291610113c088a7479c26916b67c4c +size 3740 diff --git a/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_X_host.npy b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..bb3be252292265fb98716334d40ccfaf8d75d244 --- /dev/null +++ b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b15fb02a2af34c0c93fdfe59170a6f8c933a7a529162825116447fdff3efa1ff +size 188192 diff --git a/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_gen.py b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..3faf692aec8e754cc83be1780f972f41046bdc13 --- /dev/null +++ b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(3918)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/forest-n3-3918-20260501_180740.csv', index=False) +print("saved", len(df)) diff --git a/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_meta_host.json b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..c65de394e081438752dd19dc6672d70748924abc --- /dev/null +++ b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol", "quality"], "cat_indexes": []} \ No newline at end of file diff --git a/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_train.py b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1c0813a1369f8a258815c4800dec650556ea556b --- /dev/null +++ b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib') diff --git a/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/forest-n3-3918-20260501_180740.csv b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/forest-n3-3918-20260501_180740.csv new file mode 100644 index 0000000000000000000000000000000000000000..ace52e09254eecbde1a45736816e358a97ddde24 --- /dev/null +++ b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/forest-n3-3918-20260501_180740.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0dc1c9e6161a7a3513fcbbfae9f79784fe424e8cdcbb3ea644bf6ae70f528826 +size 885780 diff --git a/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..dab33bf8088b3dc6fa4a9faafe7fa22fa997e3db --- /dev/null +++ b/syntheticSuccess/n3/forestdiffusion/forest-n3-20260501_180619/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"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/syntheticSuccess/n3/realtabformer/rtf-n3-20260501_010320/staged/public/test.csv b/syntheticSuccess/n3/realtabformer/rtf-n3-20260501_010320/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..db5cd50d3746410ea470625fa2ba2c8e9775cc0d --- /dev/null +++ b/syntheticSuccess/n3/realtabformer/rtf-n3-20260501_010320/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19e107152a94432e2a37ff045c00cfe35e1be30208b1553080d7dd0ede256d3b +size 28944 diff --git a/syntheticSuccess/n3/realtabformer/rtf-n3-20260501_010320/staged/public/train.csv b/syntheticSuccess/n3/realtabformer/rtf-n3-20260501_010320/staged/public/train.csv new file mode 100644 index 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/n3/realtabformer/rtf-n3-20260501_010320/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/n3/realtabformer/rtf-n3-20260501_010320/train_20260501_010320.log b/syntheticSuccess/n3/realtabformer/rtf-n3-20260501_010320/train_20260501_010320.log new file mode 100644 index 0000000000000000000000000000000000000000..47d08f616d63b3731a73d0fd5cf86d85f5b9ef21 --- /dev/null +++ b/syntheticSuccess/n3/realtabformer/rtf-n3-20260501_010320/train_20260501_010320.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:524c61eb693e3775beed7a4576dd3e79ea4ac03a163f7af2ee383ee6b691acbe +size 472847 diff --git a/syntheticSuccess/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_gen.py b/syntheticSuccess/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..cc773dcad632b3cd990c6e78e582634fc419b797 --- /dev/null +++ b/syntheticSuccess/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260501_005234/tabular_bundle/pipeline_n3" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_n3/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260501_005234/tabbyflow-n3-3918-20260501_005942.csv") diff --git a/syntheticSuccess/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_train.py b/syntheticSuccess/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..11c52de385c5406a71ac62f40888a03828ce3be8 --- /dev/null +++ b/syntheticSuccess/n3/tabbyflow/tabbyflow-n3-20260501_005234/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabbyflow/tabbyflow-n3-20260501_005234/tabular_bundle/pipeline_n3" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git 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b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 3918 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/config_sample_20260501_002035_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/tabddpm-n3-3918-20260501_002035.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/tabddpm-n3-3918-20260501_002035.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_train.py b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..16db5d0522d0347df0ebb928d10a5cd3e3da9af9 --- /dev/null +++ b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..c774736da1e2d6dd06bb2981c8d5d527a7059c43 --- /dev/null +++ b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/config_sample_20260501_002035_r0.toml b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/config_sample_20260501_002035_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..00d8c1b8acc5120a9558843978768b9f459c23e0 --- /dev/null +++ b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/config_sample_20260501_002035_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/n3/tabddpm/tabddpm-n3-20260501_002012/data" +model_type = "mlp" +num_numerical_features = 11 +device = "cuda:0" + +[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/X_num_train.npy b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..94beb8413ef55592710518365965f4cb503523ee --- /dev/null +++ b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:093d8f405d764277d5a007953f25ab6466f0857f71f0d8f3c437463fb5bf240a +size 172520 diff --git a/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/info.json b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "num_classes": 7 +} \ No newline at end of file diff --git a/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/y_train.npy b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git 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+[model_params] +d_in = 11 +num_classes = 7 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 3918 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/output/info.json b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..45d216717b2847b5a467946053f10b0f41c3148b --- /dev/null +++ b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/output/info.json @@ -0,0 +1,39 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 11, + 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sha256:d6215ab7dae82ddeae2fc63f308c3bc59d26da30cd7e61dd046ab97b6619fef9 +size 774771 diff --git a/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/train_20260501_002012.log b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/train_20260501_002012.log new file mode 100644 index 0000000000000000000000000000000000000000..9be1c25892c24ae25207e9ee3383feac085086c7 --- /dev/null +++ b/syntheticSuccess/n3/tabddpm/tabddpm-n3-20260501_002012/train_20260501_002012.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5ba05af1754eef93e2c519198aa9c11b3ded64badaf882b13abde890338e8da +size 1067 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_gen.py b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..76f2f6d01190680e15e6db23b7eb4d5923a7b8c4 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n3/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(3918)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_175353/tabdiff-n3-3918-20260501_175710.csv") diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_train.py b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..33f62823f0bbcc1bf529c511776922b9648c407f --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n3" +src = r"/work/output-Benchmark-trainonly-v1/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/gen_20260501_175710.log b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/gen_20260501_175710.log new file mode 100644 index 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--git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/models_tabdiff/trained.pt b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/models_tabdiff/trained.pt new file mode 100644 index 0000000000000000000000000000000000000000..bc319a55a9c7152a1137f968b6fa1c735125ad11 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/models_tabdiff/trained.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:306660b8aad4549267c68390b017e15ddb9bc06a02c80515eb72a97ae31a81eb +size 74 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + 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sha256:093d8f405d764277d5a007953f25ab6466f0857f71f0d8f3c437463fb5bf240a +size 172520 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/info.json b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/info.json new file mode 100644 index 0000000000000000000000000000000000000000..8b5f7b3a1a0c4ba2d967287826b9f5c5fd7619de --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/info.json @@ -0,0 +1,139 @@ +{ + "name": "pipeline_n3", + "task_type": "multiclass", + "n_num_features": 11, + "n_cat_features": 0, + "train_size": 3918, + "test_num": 3918, + "val_num": 3918, + "train_num": 3918, + "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).", + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10 + ], + "cat_col_idx": [], + "target_col_idx": [ + 11 + ], + "column_names": [ + "fixed acidity", + "volatile acidity", + "citric acid", + "residual sugar", + "chlorides", + "free sulfur dioxide", + "total sulfur dioxide", + "density", + "pH", + "sulphates", + "alcohol", + "quality" + ], + "int_col_idx": [], + "int_columns": [], + "int_col_idx_wrt_num": [], + "metadata": { + "columns": { + "0": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "1": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "2": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "3": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "4": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "5": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "6": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "7": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "8": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "9": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "10": { + "sdtype": "numerical", + "computer_representation": "Float" + }, + "11": { + "sdtype": "categorical" + } + } + }, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6, + "7": 7, + "8": 8, + "9": 9, + "10": 10, + "11": 11 + }, + "inverse_idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6, + "7": 7, + "8": 8, + "9": 9, + "10": 10, + "11": 11 + }, + "idx_name_mapping": { + "0": "fixed acidity", + "1": "volatile acidity", + "2": "citric acid", + "3": "residual sugar", + "4": "chlorides", + "5": "free sulfur dioxide", + "6": "total sulfur dioxide", + "7": "density", + "8": "pH", + "9": "sulphates", + "10": "alcohol", + "11": "quality" + }, + "n_classes": 7 +} \ No newline at end of file diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/real.csv b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..b4297372e8b9b076d6c03efa023cbf23edee4f56 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c80ba530c12c66a78530db5d98ead5f78fcdad2fe5eccccbc28d9649ceaa6023 +size 229825 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/test.csv b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..b4297372e8b9b076d6c03efa023cbf23edee4f56 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c80ba530c12c66a78530db5d98ead5f78fcdad2fe5eccccbc28d9649ceaa6023 +size 229825 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/val.csv b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..b4297372e8b9b076d6c03efa023cbf23edee4f56 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c80ba530c12c66a78530db5d98ead5f78fcdad2fe5eccccbc28d9649ceaa6023 +size 229825 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_test.npy b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_train.npy b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_val.npy b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..1f737a58cd26592130c3604aaa90044a451eb576 --- /dev/null +++ b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/tabular_bundle/pipeline_n3/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:85ed37c52f0ea54dfd3a476dcfafff02d61e92783748e5ee9aadfad20578bcb1 +size 31472 diff --git a/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/train_20260501_175353.log b/syntheticSuccess/n3/tabdiff/tabdiff-n3-20260501_175353/train_20260501_175353.log new file mode 100644 index 0000000000000000000000000000000000000000..df10c37193a38e053d07c80622b7227d4213517f --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/_tabpfgen_generate.py b/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c8ade1fa83cb3e83789abaa5dd376381b18ae5df --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/gen_20260501_005212.log b/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/gen_20260501_005212.log new file mode 100644 index 0000000000000000000000000000000000000000..349d4af44544d58c87db4e1cf03ceb3b413eacc5 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/input_snapshot.json b/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..2df53dbad689923eb0692c0029f1d32d625f8ce4 --- /dev/null +++ b/syntheticSuccess/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": "c0cc56f5fe28e1b65927255c6e3c2b0e9479862c2ec6f2fea1664da36c9a3a09" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c41fe304503de23190a02e6cbfafa35b3bf134d3 --- /dev/null +++ b/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,259 @@ +{ + "dataset_id": "n3", + "target_column": "quality", + "task_type": "classification", + "columns": [ + { + "name": "fixed acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.016845, + "example_values": [ + "7.1", + "8.5", + "6.4", + "6.8", + "7.6" + ] + } + }, + { + "name": "volatile acidity", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 121, + "unique_ratio": 0.030883, + "example_values": [ + "0.31", + "0.21", + "0.24", + "0.43", + "0.27" + ] + } + }, + { + "name": "citric acid", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 85, + "unique_ratio": 0.021695, + "example_values": [ + "0.17", + "0.41", + 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"/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/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv b/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/tabpfgen-n3-3918-20260501_005212.csv new file mode 100644 index 0000000000000000000000000000000000000000..14c8479f09cc24745763f4c19d101dbbdfc461c0 --- /dev/null +++ b/syntheticSuccess/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 sha256:5c4bf5785236913316d81cc4153e113790caf45aef95726aa24d80d86a2781dc +size 446015 diff --git 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b/syntheticSuccess/n3/tabpfgen/tabpfgen-n3-20260501_005212/train_20260501_005212.log new file mode 100644 index 0000000000000000000000000000000000000000..251655c92a85a09a0e32432e7fa14272b152157d --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_sample.py b/syntheticSuccess/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..749894d8a508cae7402d8a4bb55efc31398e5e78 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_train.py b/syntheticSuccess/n3/tabsyn/tabsyn-n3-20260501_002047/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ed08fcb958c5edfd8a5b0c44967e0ef6c676d14e --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/n3/tabsyn/tabsyn-n3-20260501_002047/data/tabsyn_n3/X_cat_test.npy b/syntheticSuccess/n3/tabsyn/tabsyn-n3-20260501_002047/data/tabsyn_n3/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..24e2b0be40b07b6e79f803a82e6e38723e462320 --- /dev/null +++ b/syntheticSuccess/n3/tabsyn/tabsyn-n3-20260501_002047/data/tabsyn_n3/X_cat_test.npy @@ -0,0 +1,3 @@ +version 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