diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/_arf_generate.py b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..929b79c4c39bddd49116abbe8852e696db4623ab --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/c5/arf/arf-c5-20260321_064412/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=6732) +syn.to_csv("/work/output-SpecializedModels/c5/arf/arf-c5-20260321_064412/arf-c5-6732-20260330_065300.csv", index=False) +print(f"[ARF] Generated 6732 rows -> /work/output-SpecializedModels/c5/arf/arf-c5-20260321_064412/arf-c5-6732-20260330_065300.csv") diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/_arf_train.py b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..26bb415c6f29db92f469f6116adaecbfe3d59d9e --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/c5/arf/arf-c5-20260321_064412/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/c5/arf/arf-c5-20260321_064412/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/c5/arf/arf-c5-20260321_064412/arf_model.pkl") diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/arf-c5-1000-20260321_064510.csv 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a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/arf_model.pkl b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..0c318269181e5aeab3e4bd339b8a1fad1680ed19 --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1541af70911cc0d026c74ca77b997057c181dfeac3bc410b8d66d0ee103dbb6d +size 11403267 diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/gen_20260321_064510.log b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/gen_20260321_064510.log new file mode 100644 index 0000000000000000000000000000000000000000..e8f942d460b213b438772f7be5001b3a7ac993c4 --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/gen_20260321_064510.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:452a5f6557057dba9274930c08564f44fabe28bf940f017c580698bcd16b994c +size 7390 diff --git 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+ "LEAVES" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/public_gate/public_gate_report.json b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..492da238f9513051457e6857be56cde4d58edb61 --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c5", + "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": "class", + 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+ "feature_name": "spore-print-color", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "population", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "habitat", + "data_type": "categorical", + "is_target": false + } +] \ No newline at end of file diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/test.csv b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..84b54bc5cb4314ab419a4473eec1ca4d0f24eabd --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:22374ee05e54a92c07546639c8485d89e02a7c7b38db99ef9ac5dfb259bd032d +size 125218 diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/train.csv b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..16709efa9ec0bac522d9ff1fe7980ce71cea2661 --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a23d9f1558e4268759d44a5a58662ddff4e0b757a65c49e7019e9db25203034 +size 997613 diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/val.csv b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..15ae7b37b5fa7515648c9830acf63d02f537b63f --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa96f3f053a3ae913c7cd723e390bf19f86afc22192eea09094e136ce2a6eb6c +size 124824 diff --git a/syntheticSuccess/c5/arf/arf-c5-20260321_064412/train_20260321_064412.log b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/train_20260321_064412.log new file mode 100644 index 0000000000000000000000000000000000000000..a9854e41431f343d8e1f3cebde3b6c0f01ae19e3 --- /dev/null +++ b/syntheticSuccess/c5/arf/arf-c5-20260321_064412/train_20260321_064412.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9541151515445648cd503d0dc4b46e4c093b8333c976004f6531574575c89037 +size 232 diff --git a/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/_bayesnet_generate.py b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..768368beb8fcb4dfb2f1933d9d069ff0889db125 --- /dev/null +++ b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/_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/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=6732).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/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/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-6732-20260330_065301.csv", index=False) +print(f"[BayesNet] Generated 6732 rows -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-6732-20260330_065301.csv") diff --git a/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/_bayesnet_train.py b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f709156ec45f0d9d8d11e8fa56c22621fe03441c --- /dev/null +++ b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/_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/c5/bayesnet/bayesnet-c5-20260321_061655/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/c5/bayesnet/bayesnet-c5-20260321_061655/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/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl") diff --git a/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-1000-20260321_061740.csv b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-1000-20260321_061740.csv new file mode 100644 index 0000000000000000000000000000000000000000..1be003949e3bd93066836aa4a49c1e3bc7e9beea --- /dev/null +++ b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-1000-20260321_061740.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fa600141a10f64adf4bbb8e012ca936d8e8e21c25f0aec26b77af8648ed9d3bd +size 148865 diff --git a/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-6732-20260330_065301.csv b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-6732-20260330_065301.csv new file mode 100644 index 0000000000000000000000000000000000000000..3c865f70f73bdf046cc1cfceed32f723a7ab999a --- /dev/null +++ b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-6732-20260330_065301.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0375ab7e5dbddcaca3c05a768f6d25c9c5ffae65611f52d94bae7275912b2e0c +size 998418 diff --git a/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e153a29921d938639e99c1470ab3fb73392b7dad --- /dev/null +++ b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 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-0,0 +1,36 @@ +{ + "dataset_id": "c5", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-train.csv", + "exists": true, + "size": 1004346, + "sha256": "47ca8fcb0dce8411cee7c20652d6bf10a48a4c284cef58267e775b807c625180" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-val.csv", + "exists": true, + "size": 125666, + "sha256": "599dbe0d059984263e88e20a64ae75c0f9795a6ee662c7ae3d13fe4db35753c2" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-test.csv", + "exists": true, + "size": 126062, + "sha256": "f4eab85438337cfa2fd60a783388c9bb9f2a67a8c077d09f929fe34ee2895c28" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c5/c5-dataset_profile.json", + "exists": true, + "size": 8949, + "sha256": "74d201cb6f2a25d865c87f0421b9f9c5969d2edfd8fbae898594ff626b33e393" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c5/c5-dataset_contract_v1.json", + "exists": true, + "size": 11183, + "sha256": "df30b6c2fb9044e5c99b3257357a58462928289a836a9cf591e2f754f49bf729" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b0cfe6a66eb449bbb23102ff9d047c851993d280 --- /dev/null +++ b/syntheticSuccess/c5/bayesnet/bayesnet-c5-20260321_061655/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,467 @@ +{ + "dataset_id": "c5", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + 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"categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 10, + "unique_ratio": 0.001485, + "example_values": [ + "YELLOW", + "GRAY", + "BUFF", + "WHITE", + "BROWN" + ] + } + }, + { + "name": "bruises?", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.000297, + "example_values": [ + "BRUISES", + "NO" + ] + } + }, + { + "name": "odor", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [ + "NONE" + ], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.453209, + "unique_count": 8, + "unique_ratio": 0.002173, + "example_values": [ + "ALMOND", + "FOUL", + "FISHY", + "SPICY", + "ANISE" + ] + } + }, + { + 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260328_094434/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c5/realtabformer/rtf-c5-20260328_094434/train_20260328_094434.log b/syntheticSuccess/c5/realtabformer/rtf-c5-20260328_094434/train_20260328_094434.log new file mode 100644 index 0000000000000000000000000000000000000000..91335582c67bb3434d95ac70025f99e94d7556c9 --- /dev/null +++ b/syntheticSuccess/c5/realtabformer/rtf-c5-20260328_094434/train_20260328_094434.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee88e260121894a7cc137c2dae1d2e0dc8e7158e6cbf234d452a34b3d30dfb75 +size 1507409 diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/_tabddpm_sample.py b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..b642bcf83b0940964bbb733cfa0f02147d76cd40 --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/_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 6732 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/config_sample_20260422_211947.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/c5/tabddpm/tabddpm-c5-20260422_211246" +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/c5/tabddpm/tabddpm-c5-20260422_211246/tabddpm-c5-6732-20260422_211947.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/tabddpm-c5-6732-20260422_211947.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/_tabddpm_train.py b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a4e7e15a3ea99f8f52ab93851ab0de67d124383c --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/_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/c5/tabddpm/tabddpm-c5-20260422_211246/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/config.toml b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..3d70231d28d96455b43382221741ef72df31f97e --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/output" +real_data_path = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 22 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/config_sample_20260422_211947.toml b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/config_sample_20260422_211947.toml new file mode 100644 index 0000000000000000000000000000000000000000..c15c8ba08e7cc3d787e4b89c7198613729a6dce3 --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/config_sample_20260422_211947.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/output" +real_data_path = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 22 +num_classes = 2 +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 = 6732 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/X_cat_train.npy b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..049bbe2d600e0dc19d8800739a7985fadff45073 --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d2e5a8320a843e2485682b858182bafd262c91a7389b4627e7c2a43d15ca001a +size 1184960 diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/info.json b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..3773aabc908a4d92d99358303a71623225fa32ee --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/info.json @@ -0,0 +1,61 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 0, + "n_cat_features": 22, + "train_size": 6732, + "num_col_idx": [], + "cat_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21 + ], + "target_col_idx": [ + 22 + ], + "column_names": [ + "cap-shape", + "cap-surface", + "cap-color", + "bruises?", + "odor", + "gill-attachment", + "gill-spacing", + "gill-size", + "gill-color", + "stalk-shape", + "stalk-root", + "stalk-surface-above-ring", + "stalk-surface-below-ring", + "stalk-color-above-ring", + "stalk-color-below-ring", + "veil-type", + "veil-color", + "ring-number", + "ring-type", + "spore-print-color", + "population", + "habitat", + "class" + ], + "num_classes": 2 +} \ No newline at end of file diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/y_train.npy b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..c2149b5e34b6925862a4f8e723001f41490c4acc --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1b36003f6d55aaa9706a2d1248efd2c4736d8f8d8a8fdac7857df93028d682ec +size 53984 diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/gen_20260422_211947.log b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/gen_20260422_211947.log new file mode 100644 index 0000000000000000000000000000000000000000..f474d2e8016bc4a391dfca30a12c6a6f52e8aa45 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100644 index 0000000000000000000000000000000000000000..d704b8ae80565af0478919823312e292503c5682 --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/tabddpm-c5-6732-20260422_211947.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5b67760e94a26b6c00a491716bb2780d3ec88b32aa56e59289c2f111c5dab6b +size 620615 diff --git a/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/train_20260422_211247.log b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/train_20260422_211247.log new file mode 100644 index 0000000000000000000000000000000000000000..e9d006129b48b223ae8dffa15e87645b214c8b34 --- /dev/null +++ b/syntheticSuccess/c5/tabddpm/tabddpm-c5-20260422_211246/train_20260422_211247.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00a81fea0758acec86f7d70be97f369c00eb01c7291ae5cbc1c52605b4c3662d +size 855 diff --git a/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/_tabsyn_sample.py b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..84c9d3ad67fe511b121a4177e051b0936589f6c8 --- /dev/null +++ b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446" +dataname = "tabsyn_c5" +output_csv = "/work/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/tabsyn-c5-6732-20260421_004948.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 6732 rows") +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "sample", + "--method", "tabsyn", + "--gpu", "0", + "--save_path", output_csv], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print(f"[TabSyn] Saved -> {output_csv}") diff --git a/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/_tabsyn_train.py b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f996f1e1115da7053983abd25cbbe7b2f8ffc685 --- /dev/null +++ b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446" +dataname = "tabsyn_c5" +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/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/data/tabsyn_c5/X_cat_test.npy 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabsyn/tabsyn-c5-20260420_233446/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/synthetic/tabsyn_c5/real.csv b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/synthetic/tabsyn_c5/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..a1cfdbca25aaec1fbb41059a6efd4e993ed73737 --- /dev/null +++ b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/synthetic/tabsyn_c5/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be645b323a5e46b76d5b8b86978c4e3f826963c34a5e1825173a7c8a38c6632d +size 348766 diff --git a/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/synthetic/tabsyn_c5/test.csv b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/synthetic/tabsyn_c5/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..6ce0932d0eb1e5e13a30abb0a02434d7493d8578 --- /dev/null +++ b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/synthetic/tabsyn_c5/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:de784e42be81816df34db9430e38d02fe9fceb971dfd34d75451e27e75266d9e +size 39088 diff --git a/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/tabsyn-c5-6732-20260421_004948.csv b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/tabsyn-c5-6732-20260421_004948.csv new file mode 100644 index 0000000000000000000000000000000000000000..10cb795662107888134bcf364c8d35453314bdf1 --- /dev/null +++ b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/tabsyn-c5-6732-20260421_004948.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fd79801c7bcee57d816a90abe9516e9aaa97549e5e2690282433cb4f2a717e1b +size 364177 diff --git a/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/train_20260420_233447.log b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/train_20260420_233447.log new file mode 100644 index 0000000000000000000000000000000000000000..946f0e55c7bb576f4fbe71e12f4fafbace5f4544 --- /dev/null +++ b/syntheticSuccess/c5/tabsyn/tabsyn-c5-20260420_233446/train_20260420_233447.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29591a3952c3960ec21d63ae98f352d444e72708529a421764e04f8b428bbca3 +size 2626439 diff --git a/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/_tvae_generate.py b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..a9384f91188d380e09e805abed6d9255b22d9411 --- /dev/null +++ b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/models_300epochs/tvae_300epochs.pt") +samples = model.sample(6732) +samples.to_csv("/work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/tvae-c5-6732-20260330_065259.csv", index=False) +print(f"[TVAE] Generated 6732 rows -> /work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/tvae-c5-6732-20260330_065259.csv") diff --git a/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/_tvae_train.py b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3c7711921762aa591a611a2ffad5760d5a246fd2 --- /dev/null +++ b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/_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/c5/tvae/tvae-c5-20260321_070511/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/c5/tvae/tvae-c5-20260321_070511/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/c5/tvae/tvae-c5-20260321_070511/gen_20260321_070708.log b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/gen_20260321_070708.log new file mode 100644 index 0000000000000000000000000000000000000000..05ead0c5013e7516cb8114ef69ea3c01b126303a --- /dev/null +++ b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/gen_20260321_070708.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:556ef1b3b57ee4780c1bc51f835f52e37d2c4c3af10d81018d11dfce68709348 +size 126 diff --git 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b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..f9ee7ae98151ca1d1db2678ce838ce87458a4e7c --- /dev/null +++ b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9cb7df35aebee3e73e6931c65690e854704cb3b860cf5f6580ecd8fc3cc6a1bf +size 870892 diff --git a/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b0cfe6a66eb449bbb23102ff9d047c851993d280 --- /dev/null +++ b/syntheticSuccess/c5/tvae/tvae-c5-20260321_070511/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,467 @@ +{ + "dataset_id": "c5", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + 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