diff --git a/syntheticSuccess/m6/arf/arf-m6-20260423_090001/_arf_generate.py b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ec3053aa0ac837b776cd9299cb779211106dab7a --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/m6/arf/arf-m6-20260423_090001/_arf_train.py b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f100bab1c0ff1e1137cc6244afed32b6fc09877c --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv new file mode 100644 index 0000000000000000000000000000000000000000..a277909403d58ec16ee3d3127ec7a09d7532186e --- /dev/null +++ b/syntheticSuccess/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:e131b12db9695b205eb97e35ed4baf665c6de67084e3117089dd0d357fa53710 +size 922076 diff --git a/syntheticSuccess/m6/arf/arf-m6-20260423_090001/arf_model.pkl b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..0229d5cb564e96e875d18feb7cf7f4fa3f5aa55e --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/m6/arf/arf-m6-20260423_090001/gen_20260423_090902.log b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/gen_20260423_090902.log new file mode 100644 index 0000000000000000000000000000000000000000..8469d1141c2e2e24f381b4042db38c449b476318 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/m6/arf/arf-m6-20260423_090001/input_snapshot.json b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..85e550b81639d6cbdbe17d9d406679425dac9c54 --- /dev/null +++ b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + 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b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..99a260799c8356f445bdc0b33ccce8b459ba1938 --- /dev/null +++ b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m6", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "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/syntheticSuccess/m6/arf/arf-m6-20260423_090001/public_gate/staged_input_manifest.json b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7573cbf01a515e69f0600eccc759d5f93e904b11 --- /dev/null +++ b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/public_gate/staged_input_manifest.json @@ -0,0 +1,382 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/staged/public/val.csv", + "test_csv": 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m6/arf/arf-m6-20260423_090001/staged/public/staged_features.json b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..49b25bd1347f910a2b95ef0c137d1a64bf69e0cb --- /dev/null +++ b/syntheticSuccess/m6/arf/arf-m6-20260423_090001/staged/public/staged_features.json @@ -0,0 +1,92 @@ +[ + { + "feature_name": "Administrative", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Administrative_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Informational", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Informational_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ProductRelated", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ProductRelated_Duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "BounceRates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "ExitRates", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "PageValues", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "SpecialDay", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "OperatingSystems", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Browser", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Region", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "TrafficType", + "data_type": "continuous", + "is_target": false + }, + { + 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100644 index 0000000000000000000000000000000000000000..90ad27bd3d2a77007776a6b3c6a06437b5485c66 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_generate.py b/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..af42cdf43d827d48050a3d1fac9d5d4c66c17164 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_train.py b/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a3968f50262b0a0a29c3f70f2219c6e1e4258302 --- /dev/null +++ b/syntheticSuccess/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/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-1000-20260321_080006.csv b/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-1000-20260321_080006.csv new file mode 100644 index 0000000000000000000000000000000000000000..97ff84eca711ea3231e3e819226ac35d60909382 --- /dev/null +++ b/syntheticSuccess/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 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sha256:1dbd52bc8ffb8f1ec30729a43673da76d8f95c6f1291f113b445aa03bae0ec51 +size 6552 diff --git a/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/gen_20260330_065702.log b/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/gen_20260330_065702.log new file mode 100644 index 0000000000000000000000000000000000000000..854a6a834a6768f2a05d610866f05bf8dc3b1284 --- /dev/null +++ b/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/gen_20260330_065702.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce02ced95c4350e9b0e2dd3d0d65759f930275dac78115571b534c21059c3829 +size 11263 diff --git a/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/input_snapshot.json b/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..99f410ec3031ca9da543eec56186120ddd23b682 --- /dev/null +++ b/syntheticSuccess/m6/bayesnet/bayesnet-m6-20260321_075851/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json", + "exists": true, + "size": 7622, + "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98" + }, + 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b/syntheticSuccess/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/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/_tabpfgen_generate.py b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..0374b598a6c34c805496e343becc569367aff821 --- /dev/null +++ b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/_tabpfgen_generate.py @@ -0,0 +1,68 @@ +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/train.csv") +target_col = "VisitorType" + +feature_cols = [c for c in df.columns if c != target_col] + +# --- Label-encode categorical / object columns --- +cat_encodings = {} # col -> list of unique values (index = code) +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)") + +# Encode target if categorical +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 + +# Handle NaN +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 + +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating 9864 rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=9864) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +# --- Inverse label-encoding for categorical columns --- +for col, cats in cat_encodings.items(): + # Round to nearest integer index, clamp to valid range + 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] + +syn_df = syn_df[list(df.columns)] +syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/tabpfgen-m6-9864-20260422_070321.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/tabpfgen-m6-9864-20260422_070321.csv") diff --git a/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/gen_20260422_070321.log b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/gen_20260422_070321.log new file mode 100644 index 0000000000000000000000000000000000000000..b0000ec5bfa2cf3aaf46fd21e9c293e0770476f3 --- /dev/null +++ b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/gen_20260422_070321.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3bfbf2de3d5f6fe8bccae8484ad3fabd4df4baba9f0a3b189425b5b3a8a9eec +size 588 diff --git a/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/input_snapshot.json b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..8508a20b6499aa7c3d1c2d992165b962275eb5f8 --- /dev/null +++ b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m6", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv", + "exists": true, + "size": 856785, + "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv", + "exists": true, + "size": 107758, + "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv", + "exists": true, + "size": 107996, + "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de" + }, + "profile_json": { + "path": 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"parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000203, + "example_values": [ + "FALSE", + "TRUE" + ] + } + }, + { + "name": "Revenue", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000203, + "example_values": [ + "FALSE", + "TRUE" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/public_gate_report.json b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..99a260799c8356f445bdc0b33ccce8b459ba1938 --- /dev/null +++ b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m6", + "status": "pass", + "checks": [ + { + "check_id": "PG001_csv_parse_ok", + "status": "pass" + }, + { + "check_id": "PG002_split_header_consistent", + "status": "pass" + }, + { + "check_id": "PG003_profile_header_match", + "status": "pass" + }, + { + "check_id": "PG004_missing_token_normalized", + "status": "pass" + }, + { + "check_id": "PG005_semantic_type_validated", + "status": "pass" + }, + { + "check_id": "PG006_target_defined_and_valid", + "status": "pass" + } + ], + "target_column": "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/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/staged_input_manifest.json b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..804722f09f940a3afbd5ffc9ce1b19d192954074 --- /dev/null +++ b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/public_gate/staged_input_manifest.json @@ -0,0 +1,382 @@ +{ + "dataset_id": "m6", + "target_column": "VisitorType", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/test.csv", + "features_json": 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"feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000203, + "example_values": [ + "FALSE", + "TRUE" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m6/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/tabpfgen-m6-9864-20260422_070321.csv b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/tabpfgen-m6-9864-20260422_070321.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1c0e3c2c8bec6776c75a1fd49d0cd21341ee49f --- /dev/null +++ b/syntheticSuccess/m6/tabpfgen/m6-migrated-20260422_183752/tabpfgen-m6-9864-20260422_070321.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:616d9d37e723dd1ef37679b55f47966b72503c7b0066cc463d1ffaca33e78bd3 +size 1969742 diff --git a/syntheticSuccess/m6/tvae/tvae-m6-20260320_071000/_tvae_generate.py b/syntheticSuccess/m6/tvae/tvae-m6-20260320_071000/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..7e8b58d9af49fab0fdf159550d723e307a5209fd --- /dev/null +++ b/syntheticSuccess/m6/tvae/tvae-m6-20260320_071000/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/models_300epochs/tvae_300epochs.pt") +samples = model.sample(9864) +samples.to_csv("/work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/tvae-m6-9864-20260330_065647.csv", index=False) +print(f"[TVAE] Generated 9864 rows -> /work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/tvae-m6-9864-20260330_065647.csv") diff --git a/syntheticSuccess/m6/tvae/tvae-m6-20260320_071000/_tvae_train.py b/syntheticSuccess/m6/tvae/tvae-m6-20260320_071000/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2fb285af8196f6b9457d75a6f00a7ee86e1da067 --- /dev/null +++ b/syntheticSuccess/m6/tvae/tvae-m6-20260320_071000/_tvae_train.py @@ -0,0 +1,16 @@ +import json, sys +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +csv_path = "/work/DatasetNew/m6/m6-train.csv" +meta_path = "/work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m6/tvae/tvae-m6-20260320_071000/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/syntheticSuccess/m6/tvae/tvae-m6-20260320_071000/gen_20260320_071402.log b/syntheticSuccess/m6/tvae/tvae-m6-20260320_071000/gen_20260320_071402.log new file mode 100644 index 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