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+syn.to_csv("/work/output-SpecializedModels/m1/arf/arf-m1-20260321_061030/arf-m1-1200-20260330_065531.csv", index=False) +print(f"[ARF] Generated 1200 rows -> /work/output-SpecializedModels/m1/arf/arf-m1-20260321_061030/arf-m1-1200-20260330_065531.csv") diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/_arf_train.py b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..40b2bbffa3067434c975026739d9003c7706d1f8 --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/m1/arf/arf-m1-20260321_061030/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +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/m1/arf/arf-m1-20260321_061030/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/m1/arf/arf-m1-20260321_061030/arf_model.pkl") diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf-m1-1000-20260321_061113.csv b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf-m1-1000-20260321_061113.csv new file mode 100644 index 0000000000000000000000000000000000000000..14cf9e3688ec443f98db6a8a5290124214deaa11 --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf-m1-1000-20260321_061113.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14f41f6aa9226c635c54d5f430ac435ed5a56aabb9530fd6cf7d3b1251f7623e +size 403308 diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf-m1-1200-20260330_065531.csv b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf-m1-1200-20260330_065531.csv new file mode 100644 index 0000000000000000000000000000000000000000..d59b1627906cd9af4b1071d186da0be31e2ebdf2 --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf-m1-1200-20260330_065531.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5e0629ccefff9497bf806f956e362763f61e17b1eb2409362d126c19a66bfd0 +size 404632 diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf_model.pkl b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..ec27fc4968811e865601c072c409d127ed7929f3 --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ca6e2cc43b319957e5557d5fa86199ed735df4edbc15f6e8e11e1eacf56b4b82 +size 17780166 diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/gen_20260321_061113.log b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/gen_20260321_061113.log new file mode 100644 index 0000000000000000000000000000000000000000..7ec7a55ea95077f62c4d16824aea13f5930f31c6 --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/gen_20260321_061113.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a140c5aa515341f7fb3779b80f9241caac02f2988662df92e9ec975bcc404a56 +size 5178 diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/gen_20260330_065531.log b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/gen_20260330_065531.log new file mode 100644 index 0000000000000000000000000000000000000000..63982e9f7ff42c3a75c5719e71b1c8b531feb870 --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/gen_20260330_065531.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7c783bc7dda5614addc74ea1ef7e79367cdae633d3a3d136348784398485ee35 +size 5178 diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/input_snapshot.json b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a28da5f59cb6830fb6a5216d814a2a38ada05e1d --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m1", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m1/m1-train.csv", + "exists": true, + "size": 247736, + "sha256": "28658fdcbade81b9228e4ee5f9e62cadcf890698f730afc2be402c32a71e151b" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m1/m1-val.csv", + "exists": true, + "size": 31474, + "sha256": "456422d2c2f69adfe81c81e2e6be1bf6fee895a582b8b63462ff234f90872927" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m1/m1-test.csv", + "exists": true, + "size": 31470, + "sha256": 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"unique_ratio": 0.19, + "example_values": [ + "95.0", + "78.2", + "93.7", + "79.9", + "63.4" + ] + } + }, + { + "name": "Meetings_Per_Week", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 19, + "unique_ratio": 0.015833, + "example_values": [ + "11", + "10", + "7", + "8", + "6" + ] + } + }, + { + "name": "Commute_Time_Minutes", + "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": 0.070833, + "example_values": [ + "0", + "18", + "39", + "25", + "44" + ] + } + }, + { + "name": "Job_Satisfaction", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + 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b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/staged/public/staged_features.json @@ -0,0 +1,152 @@ +[ + { + "feature_name": "Employee_ID", + "data_type": "ID", + "is_target": false + }, + { + "feature_name": "Age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Years_Experience", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "WFH_Days_Per_Week", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Gender", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Education_Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Marital_Status", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Has_Children", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Location_Type", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Department", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Job_Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Company_Size", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Industry", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Home_Office_Quality", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Internet_Speed_Category", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Work_Hours_Per_Week", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Manager_Support_Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Team_Collaboration_Frequency", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Productivity_Score", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Task_Completion_Rate", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Quality_Score", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Innovation_Score", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Efficiency_Rating", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Meetings_Per_Week", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Commute_Time_Minutes", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Job_Satisfaction", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Stress_Level", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Work_Life_Balance", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Survey_Date", + "data_type": "timestamp", + "is_target": false + }, + { + "feature_name": "Response_Quality", + "data_type": "categorical", + "is_target": true + } +] \ No 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sha256:1e9c852dcaca7e39ee66137ba8b9d16cff5ac3db8773ada638088281140b801f +size 246535 diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/staged/public/val.csv b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6c2de2dac86a09bf5299ab48292aa44bf6ad4099 --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74e1ec553c236519ce940f48b25c8f9b87aca28e5746f70de8e292fcae7b3dfb +size 31323 diff --git a/SynthData0523/main/m1/arf/arf-m1-20260321_061030/train_20260321_061031.log b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/train_20260321_061031.log new file mode 100644 index 0000000000000000000000000000000000000000..3e38b7e704f8cab0e466c053d888e8f8f7e02095 --- /dev/null +++ b/SynthData0523/main/m1/arf/arf-m1-20260321_061030/train_20260321_061031.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:43e979186eaca28049eb60034cadd1d6bf922cde915575b8550844d5baf07ffa +size 232 diff --git a/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/_bayesnet_generate.py b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..f80d7200abfe5373e26cb0f3fb77282e2e5f3f25 --- /dev/null +++ b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/_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/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=1200).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/m1/bayesnet/bayesnet-m1-20260321_061005/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/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet-m1-1200-20260330_065535.csv", index=False) +print(f"[BayesNet] Generated 1200 rows -> /work/output-SpecializedModels/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet-m1-1200-20260330_065535.csv") diff --git a/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/_bayesnet_train.py b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..4f52e171d31c8c947d67ddfa4aeabc3811cbda71 --- /dev/null +++ b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/_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/m1/bayesnet/bayesnet-m1-20260321_061005/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/m1/bayesnet/bayesnet-m1-20260321_061005/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/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet_model.pkl") diff --git a/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet-m1-1000-20260321_061056.csv b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet-m1-1000-20260321_061056.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb0afde44d1eaac67355bbf858d7650139e3b980 --- /dev/null +++ b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet-m1-1000-20260321_061056.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93b72f1057e13089d2faecc674dab9586cd974c6868a85463d48710bbc3df836 +size 268585 diff --git a/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet-m1-1200-20260330_065535.csv b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/bayesnet-m1-1200-20260330_065535.csv new file mode 100644 index 0000000000000000000000000000000000000000..ab1960ebe36726db24bbee0af440795456aa068a 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0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/gen_20260321_061056.log b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/gen_20260321_061056.log new file mode 100644 index 0000000000000000000000000000000000000000..9c88bc38270c6f631f8c9405c0d3b79cf9c6cf41 --- /dev/null +++ b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/gen_20260321_061056.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e091a4611921d7fd1bce8646734d6dcd4df2c80ec912bed8af3685f1e78b9e50 +size 232 diff --git a/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/gen_20260330_065535.log b/SynthData0523/main/m1/bayesnet/bayesnet-m1-20260321_061005/gen_20260330_065535.log new file mode 100644 index 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"unique_ratio": 0.0025, + "example_values": [ + "High", + "Medium", + "Low" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/ctgan/ctgan-m1-20260322_064456/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/ctgan/ctgan-m1-20260322_064456/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/ctgan/ctgan-m1-20260322_064456/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/ctgan/ctgan-m1-20260322_064456/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/ctgan/ctgan-m1-20260322_064456/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/ctgan/ctgan-m1-20260322_064456/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/staged_features.json b/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..7c1d797ff4d56c1da8121d03f4688e8a9b9c13bc --- /dev/null +++ b/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/staged_features.json @@ -0,0 +1,152 @@ +[ + { + "feature_name": "Employee_ID", + "data_type": "ID", + "is_target": false + }, + { + "feature_name": "Age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Years_Experience", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "WFH_Days_Per_Week", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Gender", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Education_Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Marital_Status", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Has_Children", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Location_Type", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Department", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Job_Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Company_Size", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Industry", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Home_Office_Quality", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Internet_Speed_Category", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Work_Hours_Per_Week", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Manager_Support_Level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Team_Collaboration_Frequency", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Productivity_Score", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Task_Completion_Rate", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Quality_Score", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Innovation_Score", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Efficiency_Rating", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Meetings_Per_Week", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Commute_Time_Minutes", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Job_Satisfaction", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Stress_Level", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Work_Life_Balance", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Survey_Date", + "data_type": "timestamp", + "is_target": false + }, + { + "feature_name": "Response_Quality", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/test.csv b/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..271d074b582c0df7d0f41d0c95be6dce4649470a --- /dev/null +++ b/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b10fb23dd851cc8e9512cc2f6c6cf8a7d55a9e129e0ea3fcfdf74a1d9c4ae4e +size 31319 diff --git a/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/train.csv b/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..05187648c8c9f481592f6db0a9997f7e91ebcac0 --- /dev/null +++ b/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1e9c852dcaca7e39ee66137ba8b9d16cff5ac3db8773ada638088281140b801f +size 246535 diff --git a/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/val.csv b/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..6c2de2dac86a09bf5299ab48292aa44bf6ad4099 --- /dev/null +++ b/SynthData0523/main/m1/ctgan/ctgan-m1-20260322_064456/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74e1ec553c236519ce940f48b25c8f9b87aca28e5746f70de8e292fcae7b3dfb +size 31323 diff --git a/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_X_host.npy b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_X_host.npy new file mode 100644 index 0000000000000000000000000000000000000000..2426b9ed6477248f7b4b26b438dcbd3e30e9c7b0 --- /dev/null +++ b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9730d2f620dc4bac5c4001ab4f324a90fc9873f49cbc1ee16b4bddc091208464 +size 123008 diff --git a/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_gen.py b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..09b634aac9c1a914139fbd57c3dc5f388261c586 --- /dev/null +++ b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m1/forestdiffusion/forest-m1-20260511_124552/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(1200)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m1/forestdiffusion/forest-m1-20260511_124552/forest-m1-1200-20260511_124615.csv', index=False) +print("saved", len(df)) diff --git a/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_meta_host.json b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..43e7918919f4b6058093a2d9909dfbf911beb1eb --- /dev/null +++ b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_meta_host.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f2b1923479475aa345ae9e8118f163ab1e51b7e0287e1b395355399b6c0de0e +size 657 diff --git a/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_train.py b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..bb1e5eb8c9a111772d5e20a568cf1d7a2b1c488a --- /dev/null +++ b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m1/forestdiffusion/forest-m1-20260511_124552/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m1/forestdiffusion/forest-m1-20260511_124552/_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=2 " + f"n_estimators=1 duplicate_K=1 n_jobs=1 " + f"max_depth=2 xgb_verbosity=0 xgb_nthread=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=2, n_estimators=1, duplicate_K=1, n_jobs=1, + model="xgboost", max_depth=2, tree_method="hist", cat_indexes=cat_indexes, + verbosity=0, nthread=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/m1/forestdiffusion/forest-m1-20260511_124552/forestdiffusion_model.joblib') diff --git a/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/forest-m1-1200-20260511_124615.csv b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/forest-m1-1200-20260511_124615.csv new file mode 100644 index 0000000000000000000000000000000000000000..fbe26a4eeadfb5005c8e870768aa76d0ce8cf1d3 --- /dev/null +++ b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/forest-m1-1200-20260511_124615.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48e4cedae4174bfe875442188a94d865974b400d171aafbfe5309fd6bee790d5 +size 343629 diff --git a/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/forestdiffusion_model.joblib b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/forestdiffusion_model.joblib new file mode 100644 index 0000000000000000000000000000000000000000..1ee9bb48a33373f2946c07ff259aeb3b5edeef43 --- /dev/null +++ b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/forestdiffusion_model.joblib @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:692d634c671690bfc6c37e08c33163e2704590a5aabc02f1ea73894560d9168e +size 12194148 diff --git a/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/gen_20260511_124615.log b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/gen_20260511_124615.log new file mode 100644 index 0000000000000000000000000000000000000000..697459fb9033b699f3b08cc25945b53d56d322b5 --- /dev/null +++ b/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/gen_20260511_124615.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3976f2bc3873f399157e04568eb1fec084ffb28af73120f7b09d142bf6121f23 +size 294 diff --git a/SynthData0523/main/m1/forestdiffusion/forest-m1-20260511_124552/input_snapshot.json 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b/SynthData0523/main/m1/realtabformer/rtf-m1-20260321_074318/train_20260321_074318.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12df33b2e47e63d10ed30727921e71904b9cb14f4ad9eb00c317e2d9b430dbc4 +size 276996 diff --git a/SynthData0523/main/m1/tabbyflow/tabbyflow-m1-20260420_092230/_tabbyflow_gen.py b/SynthData0523/main/m1/tabbyflow/tabbyflow-m1-20260420_092230/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..ec858be0348a973aa1fb598da6fc72d4191ca0e6 --- /dev/null +++ b/SynthData0523/main/m1/tabbyflow/tabbyflow-m1-20260420_092230/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/m1/tabbyflow/tabbyflow-m1-20260420_092230/tabular_bundle/pipeline_ds" +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_ds/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(1200)), +]) +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-SpecializedModels/m1/tabbyflow/tabbyflow-m1-20260420_092230/tabbyflow-m1-1200-20260420_092929.csv") diff --git a/SynthData0523/main/m1/tabbyflow/tabbyflow-m1-20260420_092230/_tabbyflow_train.py b/SynthData0523/main/m1/tabbyflow/tabbyflow-m1-20260420_092230/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..fa82de04d7854e0ab6152ed8d96fc44db9b509df --- /dev/null +++ b/SynthData0523/main/m1/tabbyflow/tabbyflow-m1-20260420_092230/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/m1/tabbyflow/tabbyflow-m1-20260420_092230/tabular_bundle/pipeline_ds" +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/m1/tabbyflow/tabbyflow-m1-20260420_092230/input_snapshot.json b/SynthData0523/main/m1/tabbyflow/tabbyflow-m1-20260420_092230/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..81cdacd8b4ad26646b4e9398edc60c819ac0479f --- /dev/null +++ b/SynthData0523/main/m1/tabbyflow/tabbyflow-m1-20260420_092230/input_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6b1b71f8454999d3432c5e0940240ade9e0314d30dc8c1ebfab0f458430fbf00 +size 1350 diff --git 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sha256:1aa396f1317b1e24c195c9f00a0a48d5511283735e1689bf7e307cbb9f3385f0 +size 288242 diff --git a/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/_tabddpm_sample.py b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..573d66729b408ecbc64f0e901a38809d768a42c4 --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/_tabddpm_sample.py @@ -0,0 +1,67 @@ +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 1200 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/config_sample_20260424_040321.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV +work_dir = "/work/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725" +info_path = os.path.join(work_dir, "data", "info.json") +with open(info_path) as f: + info = json.load(f) + +output_dir = os.path.join(work_dir, "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-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/tabddpm-m1-1200-20260424_040321.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/tabddpm-m1-1200-20260424_040321.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/_tabddpm_train.py b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..951255b2a77e1d0bf9ed8228088a1e1b185f13a1 --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/_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-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/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/m1/tabddpm/tabddpm-m1-20260424_033725/config.toml b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..a98368458f411dfcad74f40b15683150fff120d3 --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 14 +device = "cuda:0" + +[model_params] +d_in = 29 +num_classes = 3 +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/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/config_sample_20260424_040321.toml b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/config_sample_20260424_040321.toml new file mode 100644 index 0000000000000000000000000000000000000000..96007bfb7143053ef4f4b3e913308f67640742ef --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/config_sample_20260424_040321.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 14 +device = "cuda:0" + +[model_params] +d_in = 29 +num_classes = 3 +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 = 1200 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/X_cat_train.npy b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..a26d260c23a77933aa771e0ad36782cd34200b6a --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78f6fd9e5aaa0a3614393afa4d2da2e9eddd7691e7d98c31198027faa6bd64a3 +size 144128 diff --git a/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/X_num_train.npy b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..7048d4fdc713de4fd5d099696cee6432ace49447 --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:94fc96e7a04cf92dedd43ff8259c690bb7735758a094b6a16bca650ed5d1fb5e +size 67328 diff --git a/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/info.json b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..43b6095080cf225a054e27f9b0cd9ffba29992db --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/data/info.json @@ -0,0 +1,76 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 14, + "n_cat_features": 15, + "train_size": 1200, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 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+ "2024-05-02", + "2024-03-09", + "2024-01-28" + ] + } + }, + { + "name": "Response_Quality", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.0025, + "example_values": [ + "High", + "Medium", + "Low" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m1/tabddpm/tabddpm-m1-20260424_033725/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/tabddpm-m1-1200-20260424_040321.csv b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/tabddpm-m1-1200-20260424_040321.csv new file mode 100644 index 0000000000000000000000000000000000000000..cc87abefb0a0e6a88ecf00b6493b61d658db64ca --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/tabddpm-m1-1200-20260424_040321.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f97069608ad8ef67a89f17f7523e9577d332ba973cc318c9df7ee87240bbd28 +size 254990 diff --git a/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/train_20260424_033725.log b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/train_20260424_033725.log new file mode 100644 index 0000000000000000000000000000000000000000..82f36618332ad988405422fc2500944df35d392a --- /dev/null +++ b/SynthData0523/main/m1/tabddpm/tabddpm-m1-20260424_033725/train_20260424_033725.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73babf4e6aff6372cb0be7ed3efbdf4cee5548651dd6f6a19ba7980e39927c7a +size 901 diff --git a/SynthData0523/main/m1/tabdiff/tabdiff-m1-20260420_091055/_tabdiff_gen.py b/SynthData0523/main/m1/tabdiff/tabdiff-m1-20260420_091055/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..dff629bf8c3a8956d77d7551120478b70f230e70 --- /dev/null +++ b/SynthData0523/main/m1/tabdiff/tabdiff-m1-20260420_091055/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/m1/tabdiff/tabdiff-m1-20260420_091055/tabular_bundle/pipeline_ds" +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_ds/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(1200)), +]) +# 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-SpecializedModels/m1/tabdiff/tabdiff-m1-20260420_091055/tabdiff-m1-1200-20260420_092135.csv") diff --git a/SynthData0523/main/m1/tabdiff/tabdiff-m1-20260420_091055/_tabdiff_train.py b/SynthData0523/main/m1/tabdiff/tabdiff-m1-20260420_091055/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..78cbacf4a4f46cbf6a3ce1a24fa3cbec7eacfc4e --- /dev/null +++ b/SynthData0523/main/m1/tabdiff/tabdiff-m1-20260420_091055/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/m1/tabdiff/tabdiff-m1-20260420_091055/tabular_bundle/pipeline_ds" +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/SynthData0523/main/m1/tabdiff/tabdiff-m1-20260420_091055/input_snapshot.json b/SynthData0523/main/m1/tabdiff/tabdiff-m1-20260420_091055/input_snapshot.json new file mode 100644 index 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0000000000000000000000000000000000000000..700c9b1ec72b04d85c32001a9dabc0e9eb01ddd9 --- /dev/null +++ b/SynthData0523/main/m1/tabpfgen/m1-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/m1/staged/public/train.csv") +target_col = "Response_Quality" + +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 1200 rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=1200) + +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/m1/tabpfgen-m1-1200-20260422_070320.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/tabpfgen-m1-1200-20260422_070320.csv") diff --git a/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/gen_20260422_070320.log b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/gen_20260422_070320.log new file mode 100644 index 0000000000000000000000000000000000000000..068b63a23f822f58128d0dac1c0ee3f59f8ed1d6 --- /dev/null +++ b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/gen_20260422_070320.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e99ab525b406ea721ce482d937c07bf1e9b83e039c56f6285b848207b9222afb +size 1408 diff --git a/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/input_snapshot.json b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..4161f907f2436b8cfd67e912a0194dc16ebd4b8c --- /dev/null +++ b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m1", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m1/m1-train.csv", + "exists": true, + "size": 247736, + "sha256": "28658fdcbade81b9228e4ee5f9e62cadcf890698f730afc2be402c32a71e151b" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m1/m1-val.csv", + "exists": true, + "size": 31474, + "sha256": "456422d2c2f69adfe81c81e2e6be1bf6fee895a582b8b63462ff234f90872927" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m1/m1-test.csv", + "exists": true, + "size": 31470, + "sha256": "e0c692b62a23156b1c7d1895a979efb671de3e8a79399169602adeafb5733764" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m1/m1-dataset_profile.json", + "exists": true, + "size": 12335, + "sha256": "bec761b39442c197addda4f50857b05419b1209fd5da1163d5a5ec10f0a79c62" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m1/m1-dataset_contract_v1.json", + "exists": true, + "size": 14869, + "sha256": "5dd17025fe3446132776c80de35ceea1682db199b4e0c374bbb9b622c76a6180" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b26ced9a74a6265a050830c197f7d22c57a5f01f --- /dev/null +++ b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,625 @@ +{ + "dataset_id": "m1", + "target_column": "Response_Quality", + "task_type": "classification", + "columns": [ + { + "name": "Employee_ID", + "role": "id", + "semantic_type": "id", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1200, + "unique_ratio": 1.0, + "example_values": [ + "EMP1304", + "EMP0398", + "EMP0387", + "EMP0550", + "EMP0598" + ] + } + }, + { + "name": "Age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 39, + "unique_ratio": 0.0325, + "example_values": [ + "28", + "47", + "38", + "29", + "26" + ] + } + }, + { + "name": "Years_Experience", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 29, + "unique_ratio": 0.024167, + "example_values": [ + "3", + "7", + "11", + "4", + "2" + ] + } + }, + { + "name": "WFH_Days_Per_Week", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.005, + "example_values": [ + "5", + "4", + "2", + "3", + "1" + ] + } + }, + { + "name": "Gender", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.0025, + "example_values": [ + "Female", + "Male", + "Non-binary" + ] + } + }, + { + "name": "Education_Level", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6, + "unique_ratio": 0.005, + "example_values": [ + "Bachelor Degree", + "Master Degree", + "PhD", + "Associate Degree", + "Professional Degree" + ] + } + }, + { + "name": "Marital_Status", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + 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}, + { + "name": "Survey_Date", + "role": "feature", + "semantic_type": "datetime", + "nullable": false, + "missing_tokens": [], + "parse_format": "%Y-%m-%d", + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 122, + "unique_ratio": 0.101667, + "example_values": [ + "2024-02-04", + "2024-02-05", + "2024-05-02", + "2024-03-09", + "2024-01-28" + ] + } + }, + { + "name": "Response_Quality", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.0025, + "example_values": [ + "High", + "Medium", + "Low" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/public_gate/public_gate_report.json b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/public_gate/public_gate_report.json new file mode 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"/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m1/m1-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/public_gate/staged_input_manifest.json b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..42d5dc1cc07775b4e59b3702168feeec5742bfc8 --- /dev/null +++ b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/public_gate/staged_input_manifest.json @@ -0,0 +1,630 @@ +{ + "dataset_id": "m1", + "target_column": "Response_Quality", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/staged/public/val.csv", + "test_csv": 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"Medium", + "Low" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/m1/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/tabpfgen-m1-1200-20260422_070320.csv b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/tabpfgen-m1-1200-20260422_070320.csv new file mode 100644 index 0000000000000000000000000000000000000000..c36c1bdaeb70fd763c31f60947055fceeb037637 --- /dev/null +++ b/SynthData0523/main/m1/tabpfgen/m1-migrated-20260422_183752/tabpfgen-m1-1200-20260422_070320.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d811a62ecbf9fe9b44956c79f060ec6917fd4351032ad2f62b2cc791bb144503 +size 309165 diff --git a/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/_tabsyn_sample.py b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..82fd742e06f24194e49553c41be73751b5c1f73a --- /dev/null +++ b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m1/tabsyn/tabsyn-m1-20260421_023648" +dataname = "tabsyn_m1" +output_csv = "/work/output-SpecializedModels/m1/tabsyn/tabsyn-m1-20260421_023648/tabsyn-m1-1200-20260421_033053.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 1200 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/_tabsyn_train.py b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6c3c3e222ad9827b77f8afc8d228113a4b82908e --- /dev/null +++ b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m1/tabsyn/tabsyn-m1-20260421_023648" +dataname = "tabsyn_m1" +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") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/data/tabsyn_m1/X_cat_test.npy b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/data/tabsyn_m1/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..f860a2b589f42632a8afd01bb14dc96637e482a0 --- /dev/null +++ b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/data/tabsyn_m1/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e4d672241c126ee19f0e2fa1b9f4785103fe957c9f1a92c90fffdea8d954cbdf +size 16928 diff --git a/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/data/tabsyn_m1/X_cat_train.npy b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/data/tabsyn_m1/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..4bf71e47c441dd7cb8c2503e086d32f5635e63fa --- /dev/null +++ b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/data/tabsyn_m1/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a037fda4c2146726e538c36092fac61b93e0bb6171ee6d0f04491fce4761e7df +size 151328 diff --git a/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/data/tabsyn_m1/X_num_test.npy b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/data/tabsyn_m1/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e4057c396d46913e965141dfc58778b7c1f5325a --- /dev/null +++ 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index 0000000000000000000000000000000000000000..28b45d18baf095cae90ec63793b645f9072f5a31 --- /dev/null +++ b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/tabsyn-m1-1200-20260421_033053.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dad38074db666d113789baeb8ac3cfdf6912d19b6bfcef495b96b09df49721b5 +size 281478 diff --git a/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/train_20260421_023649.log b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/train_20260421_023649.log new file mode 100644 index 0000000000000000000000000000000000000000..0f18bb7bd1805c79acd4bfebabc900b23234180a --- /dev/null +++ b/SynthData0523/main/m1/tabsyn/tabsyn-m1-20260421_023648/train_20260421_023649.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8552a1ea3b269b312c80c88ac5d2ccb8896addd2da3e62e8a409161a94ed329d +size 2235969 diff --git a/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/_tvae_generate.py b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..3b3bf0b1101f9af1df28f5e58ab0243d2e822676 --- /dev/null +++ b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m1/tvae/tvae-m1-20260322_205529/models_300epochs/tvae_300epochs.pt") +samples = model.sample(1200) +samples.to_csv("/work/output-SpecializedModels/m1/tvae/tvae-m1-20260322_205529/tvae-m1-1200-20260330_065521.csv", index=False) +print(f"[TVAE] Generated 1200 rows -> /work/output-SpecializedModels/m1/tvae/tvae-m1-20260322_205529/tvae-m1-1200-20260330_065521.csv") diff --git a/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/_tvae_train.py b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b15cb664bb106533474f494e3d00c21481ab06f2 --- /dev/null +++ b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/_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/output-SpecializedModels/m1/tvae/tvae-m1-20260322_205529/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/m1/tvae/tvae-m1-20260322_205529/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m1/tvae/tvae-m1-20260322_205529/models_300epochs/tvae_300epochs.pt" +epochs = 300 + +data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None) +print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}") +model = TVAE(epochs=epochs, batch_size=500) +model.fit(data, discrete_columns) +model.save(save_path) +print(f"[TVAE] Model saved -> {save_path}") diff --git a/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/gen_20260322_205639.log b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/gen_20260322_205639.log new file mode 100644 index 0000000000000000000000000000000000000000..fd10ec31d9f705c982c73ab5a2a567178a5836e1 --- /dev/null +++ b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/gen_20260322_205639.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:04f47186aaea3908486fbb2039beb7f31820545a10170f4769cb6c5657f9ca8f +size 126 diff --git a/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/gen_20260330_065521.log b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/gen_20260330_065521.log new file mode 100644 index 0000000000000000000000000000000000000000..a0525dedc831e7ce1593b0ac65c97e1fbaadffc3 --- /dev/null +++ b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/gen_20260330_065521.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51b1d5d8cf8056ffea99345159280c3bba466ab7cd70931792963aefdd0e6f15 +size 126 diff --git 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"sha256": "e0c692b62a23156b1c7d1895a979efb671de3e8a79399169602adeafb5733764" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m1/m1-dataset_profile.json", + "exists": true, + "size": 12335, + "sha256": "bec761b39442c197addda4f50857b05419b1209fd5da1163d5a5ec10f0a79c62" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m1/m1-dataset_contract_v1.json", + "exists": true, + "size": 14869, + "sha256": "5dd17025fe3446132776c80de35ceea1682db199b4e0c374bbb9b622c76a6180" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/models_300epochs/train_20260322_205529.log b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/models_300epochs/train_20260322_205529.log new file mode 100644 index 0000000000000000000000000000000000000000..253c241ceaa03b6efdea50849d5b4675676247c0 --- /dev/null +++ b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/models_300epochs/train_20260322_205529.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2274eb5dfac966feb7f0302808e18f3f8c7e39876e710dfcdd20df5c18d61ab9 +size 170 diff --git a/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/models_300epochs/tvae_300epochs.pt b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..ecca7a6f50fc03112f5602c2dfaa0fef54404ec3 --- /dev/null +++ b/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9dcf88f8364ff88fe729888bd731da7f60a5cfe9839801b6f73cc65bcfb77258 +size 1896172 diff --git a/SynthData0523/main/m1/tvae/tvae-m1-20260322_205529/public_gate/normalized_schema_snapshot.json 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