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merge=lfs -text +SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/public_gate/public_gate_report.json filter=lfs diff=lfs merge=lfs -text diff --git a/SynthData0523/main/m6/arf/arf-m6-20260423_090001/_arf_generate.py b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ec3053aa0ac837b776cd9299cb779211106dab7a --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/_arf_generate.py @@ -0,0 +1,79 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时,从训练 CSV 有放回抽样,保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError;n=1 在部分版本会触发 + # AttributeError(不要用 n=1)。失败返回 None,由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(9864) +c_csv = "/work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/staged/public/train.csv" +with open("/work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/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] + +syn.to_csv("/work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv") diff --git a/SynthData0523/main/m6/arf/arf-m6-20260423_090001/_arf_train.py b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f100bab1c0ff1e1137cc6244afed32b6fc09877c --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/_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-SpecializedModels/m6/arf/arf-m6-20260423_090001/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-SpecializedModels/m6/arf/arf-m6-20260423_090001/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/arf_model.pkl") diff --git a/SynthData0523/main/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv new file mode 100644 index 0000000000000000000000000000000000000000..96f564ecc87b12b6155cea89554c29466229f9bf --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e3cf2b44c228cd7f9281ccc6ca4ffb48cd5d6311119b17abbf388bfe9fbfd7b +size 914103 diff --git a/SynthData0523/main/m6/arf/arf-m6-20260423_090001/arf_model.pkl b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..0229d5cb564e96e875d18feb7cf7f4fa3f5aa55e --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23a355180dedf2df8c3d946f6ae7ce2db4a456c5f9368775c04fe2b49f3ade86 +size 75782504 diff --git a/SynthData0523/main/m6/arf/arf-m6-20260423_090001/gen_20260423_090902.log b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/gen_20260423_090902.log new file mode 100644 index 0000000000000000000000000000000000000000..8469d1141c2e2e24f381b4042db38c449b476318 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/gen_20260423_090902.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1a31e597e4d393b695201b1094795bb16d93bb4090beb8e30f4554b95af2e55f +size 4662 diff --git a/SynthData0523/main/m6/arf/arf-m6-20260423_090001/input_snapshot.json b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..85e550b81639d6cbdbe17d9d406679425dac9c54 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "arf", + "inputs": { + "train_csv": { + "path": 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"status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "VisitorType", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m6/arf/arf-m6-20260423_090001/public_gate/staged_input_manifest.json b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7573cbf01a515e69f0600eccc759d5f93e904b11 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/public_gate/staged_input_manifest.json @@ -0,0 +1,382 @@ +{ + "dataset_id": "m6", + 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file mode 100644 index 0000000000000000000000000000000000000000..60be57a3609a30d56d27e37e49a3aedfa6294db5 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/SynthData0523/main/m6/arf/arf-m6-20260423_090001/train_20260423_090001.log b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/train_20260423_090001.log new file mode 100644 index 0000000000000000000000000000000000000000..90ad27bd3d2a77007776a6b3c6a06437b5485c66 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260423_090001/train_20260423_090001.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae31a387defef87ef3180aeb132fe49e9b15dae0caf185f16c2017376feef02b +size 348 diff --git a/SynthData0523/main/m6/arf/arf-m6-20260429_032047/_arf_generate.py b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..6cb528453a62781087fcdee2e0ef2bfe9fc937a8 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时,从训练 CSV 有放回抽样,保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError;n=1 在部分版本会触发 + # AttributeError(不要用 n=1)。失败返回 None,由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(9864) +c_csv = "/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm6' +if _ds_id == "c19": + # 仅 c19:object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv") diff --git a/SynthData0523/main/m6/arf/arf-m6-20260429_032047/_arf_train.py b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4c94a2c5482897564db4c8d2062ea1537941cfad --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零:处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/staged/public/train.csv") +df = _sanitize_for_arf(df) +print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols") + +model = arf.arf(x=df) +if hasattr(model, "fit"): + model.fit() +elif hasattr(model, "forde"): + model.forde() +else: + raise RuntimeError("arfpy API: no fit() / forde()") + +with open("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf_model.pkl") diff --git a/SynthData0523/main/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv new file mode 100644 index 0000000000000000000000000000000000000000..96f564ecc87b12b6155cea89554c29466229f9bf --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e3cf2b44c228cd7f9281ccc6ca4ffb48cd5d6311119b17abbf388bfe9fbfd7b +size 914103 diff --git a/SynthData0523/main/m6/arf/arf-m6-20260429_032047/arf_model.pkl b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..1e6a10f077a99b9ce2a537daaa5677ec473dd5d3 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/arf_model.pkl @@ -0,0 +1,3 @@ +version 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a/SynthData0523/main/m6/arf/arf-m6-20260429_032047/staged/arf/adapter_transforms_applied.json b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..6f3aaa01a627a955d2022165805d2786c0da78ba --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/staged/arf/adapter_transforms_applied.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 +size 2 diff --git a/SynthData0523/main/m6/arf/arf-m6-20260429_032047/staged/arf/model_input_manifest.json b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..07ff8baa27f6ef40e73aa8936fd8dd0bf382434c --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/staged/arf/model_input_manifest.json @@ -0,0 +1,3 @@ +version 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0000000000000000000000000000000000000000..60be57a3609a30d56d27e37e49a3aedfa6294db5 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/SynthData0523/main/m6/arf/arf-m6-20260429_032047/train_20260429_032047.log b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/train_20260429_032047.log new file mode 100644 index 0000000000000000000000000000000000000000..3b773cb689f7da622f857def3fc046a5d355ddd4 --- /dev/null +++ b/SynthData0523/main/m6/arf/arf-m6-20260429_032047/train_20260429_032047.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:298d13ce543b6bb0c550ea0a1193e8cc1a62b93915e781e52ee34396c14bc66a +size 733 diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_generate.py b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..af42cdf43d827d48050a3d1fac9d5d4c66c17164 --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_generate.py @@ -0,0 +1,43 @@ +import subprocess, sys, os + +pip_libs = "/pip_libs" +sys.path.insert(0, pip_libs) +os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "") + +def _ensure_deps(): + try: + import synthcity + except ModuleNotFoundError: + print("[BayesNet] synthcity not found - installing to cache...") + subprocess.run( + [sys.executable, "-m", "pip", "install", + "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"], + check=True + ) + import shutil, glob + for pat in ["torch", "torch-*", "torchvision", "torchvision-*", + "torchvision.libs", "torchgen", "nvidia*", "triton*"]: + for p in glob.glob(os.path.join(pip_libs, pat)): + if os.path.isdir(p): shutil.rmtree(p) + else: os.remove(p) + if pip_libs not in sys.path: + sys.path.insert(0, pip_libs) + +_ensure_deps() + +import pickle, json as _json +with open("/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=9864).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +if os.path.exists(const_path): + with open(const_path) as _f: + const_cols = _json.load(_f) + for col, val in const_cols.items(): + syn[col] = val + print(f"[BayesNet] Restored constant column '{col}' = {val}") + +syn.to_csv("/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-9864-20260330_065702.csv", index=False) +print(f"[BayesNet] Generated 9864 rows -> /work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-9864-20260330_065702.csv") diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_train.py b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a3968f50262b0a0a29c3f70f2219c6e1e4258302 --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_train.py @@ -0,0 +1,62 @@ +import subprocess, sys, os + +pip_libs = "/pip_libs" +sys.path.insert(0, pip_libs) +os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "") + +def _ensure_deps(): + try: + import synthcity + except ModuleNotFoundError: + print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...") + # Install synthcity with numpy<2 to avoid conflicts + subprocess.run( + [sys.executable, "-m", "pip", "install", + "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"], + check=True + ) + # Remove torch/torchvision from pip_libs to avoid shadowing system versions + import shutil, glob + for pat in ["torch", "torch-*", "torchvision", "torchvision-*", + "torchvision.libs", "torchgen", "nvidia*", "triton*"]: + for p in glob.glob(os.path.join(pip_libs, pat)): + if os.path.isdir(p): shutil.rmtree(p) + else: os.remove(p) + if pip_libs not in sys.path: + sys.path.insert(0, pip_libs) + +_ensure_deps() + +from synthcity.plugins import Plugins +import pickle +import pandas as pd +from synthcity.plugins.core.dataloader import GenericDataLoader + +df = pd.read_csv("/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/staged/public/train.csv") +df = df.dropna(axis=1, how="all") + +# Drop zero-variance columns (only 1 unique value) to avoid +# synthcity encoder KeyError during generation +import json as _json +const_cols = {} +for col in list(df.columns): + nuniq = df[col].nunique() + if nuniq <= 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}' (value={const_cols[col]})") + +# Save constant columns info so generate can restore them +const_path = "/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w") as _f: + _json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +print(f"[BayesNet] Training on {len(df)} rows, {len(df.columns)} cols") + +loader = GenericDataLoader(df) +plugin = Plugins().get("bayesian_network") +plugin.fit(loader) + +with open("/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl") diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-1000-20260321_080006.csv b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-1000-20260321_080006.csv new file mode 100644 index 0000000000000000000000000000000000000000..97ff84eca711ea3231e3e819226ac35d60909382 --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-1000-20260321_080006.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75f830b8cddfbd8bef88a57ffa1d3486104182eb1e5f934f7548d951bdcb5123 +size 147356 diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-9864-20260330_065702.csv b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-9864-20260330_065702.csv new file mode 100644 index 0000000000000000000000000000000000000000..9be722404873048831c1804dcd0df26e9f699f36 --- /dev/null +++ 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}, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "VisitorType", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/public_gate/staged_input_manifest.json b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..79326dd11a2a2c01e86b3aa475488d0c8217afb0 --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/public_gate/staged_input_manifest.json @@ -0,0 +1,382 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": 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0000000000000000000000000000000000000000..60be57a3609a30d56d27e37e49a3aedfa6294db5 --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/train_20260321_075851.log b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/train_20260321_075851.log new file mode 100644 index 0000000000000000000000000000000000000000..44ee9beb9cae4b2a1bcf7810bf55edec6078c6d6 --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/train_20260321_075851.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:41cb69abc1cc25c35ea640a5108139b16f128e572a7b41f6fed0940f118ceb4f +size 19383 diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_generate.py b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5bc8001cc4b4804555ffa3cfb61d1a3c696e0e62 --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(9864) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv") diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_train.py b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0af87da8451508afc336041db8cf5a90f796f660 --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/_bayesnet_train.py @@ -0,0 +1,133 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 1: + const_cols[col] = df[col].iloc[0] if len(df) > 0 else None + df = df.drop(columns=[col]) + print(f"[BayesNet] Dropped zero-variance column '{col}'") + +const_path = "/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(cols_in_df={_n_plan}, rows={_n_samples})" +) + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet_model.pkl") diff --git a/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv new file mode 100644 index 0000000000000000000000000000000000000000..e919aa34a17ca01eb8056bd9b8a8fb0ebf58fd8e --- /dev/null +++ b/SynthData0523/main/m6/bayesnet/bayesnet-m6-20260429_032623/bayesnet-m6-9864-20260429_032644.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75842ec919c837e2003f211a4049d29a222a5fc4e6f77d003ebb8c6035f6d9cd +size 1931119 diff --git 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116376 diff --git a/SynthData0523/main/m6/ctgan/ctgan-m6-20260328_052614/staged/public/train.csv b/SynthData0523/main/m6/ctgan/ctgan-m6-20260328_052614/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..ae3a266ed7bebf02bc27c50b9dea6e8c5fe71c10 --- /dev/null +++ b/SynthData0523/main/m6/ctgan/ctgan-m6-20260328_052614/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e1f0338c367408dc21d5a4ec9cdc5d3fe8188916db6085f3fd326304a55551e1 +size 924849 diff --git a/SynthData0523/main/m6/ctgan/ctgan-m6-20260328_052614/staged/public/val.csv b/SynthData0523/main/m6/ctgan/ctgan-m6-20260328_052614/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..60be57a3609a30d56d27e37e49a3aedfa6294db5 --- /dev/null +++ b/SynthData0523/main/m6/ctgan/ctgan-m6-20260328_052614/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/_ctgan_generate.py b/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..cd962c7d70b43c09c205e11c9a5f99d56451dd83 --- /dev/null +++ b/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/models_300epochs/ctgan_300epochs.pt") +total = 9864 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv") \ No newline at end of file diff --git a/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv b/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv new file mode 100644 index 0000000000000000000000000000000000000000..746830e20f227f83e5b4e0eeca337ba2f400e7f5 --- /dev/null +++ b/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/ctgan-m6-9864-20260429_032657.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:50249165a917990154fa88d1ddbe5bc4332ea03d46079b6b3b0aae9f231bbbae +size 1594920 diff --git a/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/ctgan_metadata.json b/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7696095eabe616528a205efec37277acc32e6453 --- /dev/null +++ b/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/ctgan_metadata.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:69efb4af5739c13af5759fdaa03ac779463c639192af619d867b40847f5e5655 +size 1288 diff --git a/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/gen_20260429_032657.log b/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/gen_20260429_032657.log new file mode 100644 index 0000000000000000000000000000000000000000..2a441a2bce3ea18d8654bbf19f5bd32b15d2b1aa --- /dev/null +++ b/SynthData0523/main/m6/ctgan/ctgan-m6-20260429_032145/gen_20260429_032657.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+version https://git-lfs.github.com/spec/v1 +oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4 +size 116198 diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..f9898e1189639328928f0b5de8c60707b257fa19 --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce27b51e592355e14fee8f6d47e08e3f99736fcde567190e0e180a1656361059 +size 710336 diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_gen.py b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..b88ba963d6d245c135ef011507e934db80fca36c --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib') +# generate:batch_size 为样本数 +arr = m.generate(batch_size=int(9864)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv', index=False) +print("saved", len(df)) diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..251a69826a87cb622f08d510751935457d78fdf8 --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31499a842126905384d1f622fa8907f31d1b1584ac9bbf184a88a9888f7643c6 +size 338 diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_train.py b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..cfba2859ac03543dd3c16c9f2b85ae440f6bf708 --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_054459/forestdiffusion_model.joblib') diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/forest-m6-9864-20260430_054654.csv new file mode 100644 index 0000000000000000000000000000000000000000..e32025bbc8f34848774a886a4bd31ca36978b7b2 --- /dev/null +++ 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diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/models_fd/model.joblib b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/models_fd/model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..654c882509b6183f8c5548fa3699ec14a6e4acee --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/models_fd/model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72b48bad6de7a535f72fcdeddfceebe8a0c6146586d37ab93dff734bb9f971df +size 243056863 diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_054459/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3a8fd19da5a7dd743f2010f4c6260432f89b10cc --- /dev/null +++ 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b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..f9898e1189639328928f0b5de8c60707b257fa19 --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce27b51e592355e14fee8f6d47e08e3f99736fcde567190e0e180a1656361059 +size 710336 diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_gen.py b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..b9a2a962dfa0a7c943ad1491170d5771d0ed3e2a --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/forestdiffusion_model.joblib') +# generate:batch_size 为样本数 +arr = m.generate(batch_size=int(9864)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/forest-m6-9864-20260430_060226.csv', index=False) +print("saved", len(df)) diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_meta_host.json b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..251a69826a87cb622f08d510751935457d78fdf8 --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_meta_host.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31499a842126905384d1f622fa8907f31d1b1584ac9bbf184a88a9888f7643c6 +size 338 diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_train.py b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5ae23b74b949b85045f1bea228331d51b6c65386 --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m6/forestdiffusion/forest-m6-20260430_060004/forestdiffusion_model.joblib') diff --git a/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/forest-m6-9864-20260430_060226.csv b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/forest-m6-9864-20260430_060226.csv new file mode 100644 index 0000000000000000000000000000000000000000..c15f647f361e783613b88edf18bef713b5c4b0c5 --- /dev/null +++ b/SynthData0523/main/m6/forestdiffusion/forest-m6-20260430_060004/forest-m6-9864-20260430_060226.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5edd8d5e6d83268443f94f3866892e11239c4be4bf1225ee6f14497b5ee56352 +size 1828706 diff --git 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--git a/SynthData0523/main/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/model_input_manifest.json b/SynthData0523/main/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..dad5473aacc3486201990ba762592da04e063280 --- /dev/null +++ b/SynthData0523/main/m6/realtabformer/rtf-m6-20260429_070344/staged/realtabformer/model_input_manifest.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6fe109ea814bd4812917e027aac83d8e5ba609b25abb7410202720a3ead0ef66 +size 9429 diff --git a/SynthData0523/main/m6/realtabformer/rtf-m6-20260429_070344/train_20260429_070345.log b/SynthData0523/main/m6/realtabformer/rtf-m6-20260429_070344/train_20260429_070345.log new file mode 100644 index 0000000000000000000000000000000000000000..fcc6b039b4e502119d478e2ee1928f92b4ee0140 --- /dev/null +++ b/SynthData0523/main/m6/realtabformer/rtf-m6-20260429_070344/train_20260429_070345.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7f0a24e9dd084e3b7415a7336b230837fd0b7d5b1df0aaf178e247c26cc463e7 +size 1315977 diff --git a/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_gen.py b/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..2e66829e5f7859200f843199de97279266604d05 --- /dev/null +++ b/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m6" +src = r"/work/output-Benchmark-trainonly-v1/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_m6/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(9864)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabbyflow-m6-9864-20260429_042639.csv") diff --git a/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_train.py b/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b476dece61a4f64634ae360b705233d34d361b1a --- /dev/null +++ b/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_m6" +src = r"/work/output-Benchmark-trainonly-v1/m6/tabbyflow/tabbyflow-m6-20260429_041029/tabular_bundle/pipeline_m6" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/gen_20260429_042639.log b/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/gen_20260429_042639.log new file mode 100644 index 0000000000000000000000000000000000000000..ebb4436cc214f8438425a01d32b03fe02a6e70e6 --- /dev/null +++ b/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/gen_20260429_042639.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:538973c4de222ddfe910ca4c660e3d86f483297f671bfdc8908796020b4aee66 +size 3072 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b/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/train_20260429_041030.log new file mode 100644 index 0000000000000000000000000000000000000000..1972f6cbeb6cddb044d759b3301547d4ed494aae --- /dev/null +++ b/SynthData0523/main/m6/tabbyflow/tabbyflow-m6-20260429_041029/train_20260429_041030.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d73a8a46feb3e57fcbf854bafd3ec87b8b2e46095356480983ec9e601cda85f6 +size 497663 diff --git a/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_sample_r0.py b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..4989c94cb87185dd827fcbdbcb4332d02db7ba8b --- /dev/null +++ b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 9864 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/config_sample_20260429_052144_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV(npy 在 TabDDPM 的 parent_dir,即 npy_dir) +info_path = "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/tabddpm-m6-9864-20260429_052144.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/tabddpm-m6-9864-20260429_052144.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_train.py b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c43239108b866cd1e3bd5bb14d3986e4bd9e935d --- /dev/null +++ b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..6ee624e6a06407c6b0aeaa316d28650e1daa0561 --- /dev/null +++ b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/config.toml @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3f7f2fac847789e8c70a3770d0d989badc63f3a04839d1081e09507f8c5d77e1 +size 770 diff --git a/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/config_sample_20260429_052144_r0.toml b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/config_sample_20260429_052144_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..0b236d6a306196eba882b0393547d5321097333f --- /dev/null +++ b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/config_sample_20260429_052144_r0.toml @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:488ca1e03138ddfb89c77af4aaa3a3a191f119ab5a52371cb864cb190c734717 +size 770 diff --git a/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/data/X_cat_train.npy b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..622304720bf969c46f4c82b28d35220f4b91c4b5 --- /dev/null +++ b/SynthData0523/main/m6/tabddpm/tabddpm-m6-20260429_052038/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version 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0000000000000000000000000000000000000000..1d84f1aa900f80f3bf11a8051db5759d352a303f --- /dev/null +++ b/SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m6" +src = r"/work/output-Benchmark-trainonly-v1/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_m6/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(9864)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/m6/tabdiff/tabdiff-m6-20260429_042700/tabdiff-m6-9864-20260429_043402.csv") diff --git a/SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_train.py b/SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a68c6a882e2ddef4634feb29817cd820ef424006 --- /dev/null +++ b/SynthData0523/main/m6/tabdiff/tabdiff-m6-20260429_042700/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_m6" +src = r"/work/output-Benchmark-trainonly-v1/m6/tabdiff/tabdiff-m6-20260429_042700/tabular_bundle/pipeline_m6" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git 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