diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/_arf_generate.py b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..942c44cd45f99caa9ca04eca1fc255cc62ebf9d3 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=15326) +syn.to_csv("/work/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/arf-m9-15326-20260330_065743.csv", index=False) +print(f"[ARF] Generated 15326 rows -> /work/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/arf-m9-15326-20260330_065743.csv") diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/_arf_train.py b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1724b2c63ff85c63e77d3d18155a781d2f9ddd0f --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/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/m9/arf/arf-m9-20260325_033734/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/arf_model.pkl") diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf-m9-1000-20260325_034743.csv b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf-m9-1000-20260325_034743.csv new file mode 100644 index 0000000000000000000000000000000000000000..8af793091f1fdee70d3f31b5943d4d3078882e83 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf-m9-1000-20260325_034743.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79993d3ba4e1850c0106bf98bc3f491931a673f84262a2ced81f12e51ed6a1fd +size 147845 diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf-m9-15326-20260330_065743.csv b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf-m9-15326-20260330_065743.csv new file mode 100644 index 0000000000000000000000000000000000000000..df60bfb25ded652b88f371e3ef93cfd29ed7a7d9 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf-m9-15326-20260330_065743.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9964b96bda8f7d22c3ac84a7d9bb1f12a0d4c14de761cd533ba540e6565ddf46 +size 2256146 diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf_model.pkl b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..9bc17800b4f30ebb768e182f461d157af7c60213 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c038e8a81a33e3c389d0634ae0503c4de4273ac4e13e33c82c00593d919654d6 +size 116039658 diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/gen_20260325_034743.log b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/gen_20260325_034743.log new file mode 100644 index 0000000000000000000000000000000000000000..ebaf4c85ba283ab5140e3863880dad19da437eaf --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/gen_20260325_034743.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3778f8acb2055940c52c4357fee13102d8eb2ef48b3ba8d293747bddc3a2d6f0 +size 3598 diff --git 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"/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv", + "exists": true, + "size": 1582666, + "sha256": "a3f8afdc773b440d51abd0d3490ff6dd140f091235334aa58569b075b44a200a" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv", + "exists": true, + "size": 199049, + "sha256": "e502e558e93bf38ce6eb32cd839eebaf5a0e428bd8949a13d6f6bbc6c94b56b6" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv", + "exists": true, + "size": 198975, + "sha256": "ea619037db7eed6d1be2023c1b315852b421cf64e17dcb85bd3068d306a093a0" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_profile.json", + "exists": true, + "size": 6232, + "sha256": "4c610aaaca65a4d30b558d1400dd96c2bf90091fcf91257fbcb164d0072d5f20" + }, + "contract_json": { + "path": 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file diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/public_gate/public_gate_report.json b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..321349d7d7e5ae14c7384836ae0b049df0f868e2 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m9", + "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": "target", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv" + } +} \ No newline at end of file diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/public_gate/staged_input_manifest.json b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..054f43bc477e7aea21187481bb5f166757b9f4a6 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/public_gate/staged_input_manifest.json @@ -0,0 +1,296 @@ +{ + "dataset_id": "m9", + "target_column": "target", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/staged/public/train.csv", + "val_csv": 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"unique_ratio": 0.000257, + "example_values": [ + "Male", + "Female", + "Other" + ] + } + }, + { + "name": "relevent_experience", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.00013, + "example_values": [ + "No relevent experience", + "Has relevent experience" + ] + } + }, + { + "name": "enrolled_university", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.02088, + "unique_count": 3, + "unique_ratio": 0.0002, + "example_values": [ + "no_enrollment", + "Part time course", + "Full time course" + ] + } + }, + { + "name": "education_level", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, 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0000000000000000000000000000000000000000..17a055d99ecdc5d0aa1629acbe6a88a6225824a7 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/runtime_result.json @@ -0,0 +1,14 @@ +{ + "dataset_id": "m9", + "model": "arf", + "run_id": "arf-m9-20260325_033734", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "skipped", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/arf-m9-15326-20260330_065743.csv" + } +} \ No newline at end of file diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/arf/adapter_report.json b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..38c84f576196f72d592231df4202012998f8c64f --- /dev/null +++ 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"profile_stats": { + "missing_rate": 0.311823, + "unique_count": 8, + "unique_ratio": 0.000759, + "example_values": [ + "50-99", + "10000+", + "<10", + "1000-4999", + "100-500" + ] + } + }, + { + "name": "company_type", + "role": "feature", + "semantic_type": "text", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.322393, + "unique_count": 6, + "unique_ratio": 0.000578, + "example_values": [ + "Pvt Ltd", + "Early Stage Startup", + "Public Sector", + "Funded Startup", + "NGO" + ] + } + }, + { + "name": "last_new_job", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.022054, + "unique_count": 6, + "unique_ratio": 0.0004, + "example_values": [ + "never", + "1", + "2", + ">4", + "4" + ] + } + }, + { + "name": "training_hours", + "role": "feature", + 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/arf/arf-m9-20260325_033734/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/staged_features.json b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..7829b9601abc583fbbf277a2461ce66a2fe1c39c --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/staged_features.json @@ -0,0 +1,72 @@ +[ + { + "feature_name": "enrollee_id", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "city", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "city_development_index", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "gender", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "relevent_experience", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "enrolled_university", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "education_level", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "major_discipline", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "experience", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "company_size", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "company_type", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "last_new_job", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "training_hours", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "target", + "data_type": "continuous", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/test.csv b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..fbd77efbaa72ae2847a889f9f439ffa3eb7a20cb --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f61fd91d6b36fb78247e6a342fc5f089500396b09eda485421c74d5012138c07 +size 193961 diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/train.csv b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..380218e84fe0af224d4a7f8e4eb35a13f6f41b4a --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8f6feb29a6c69afd23345fe03b2f3bb5d7f0f08bf8c8be86132e85ea54cfd65 +size 1544257 diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/val.csv b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..0c6d7887a58f1106c1c000b75fd0b2e28c7ff5c0 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c73f6c76113338cf786877e911ac48e749a569a85ac2e266c7a6dea77ec9d28e +size 193818 diff --git a/syntheticSuccess/m9/arf/arf-m9-20260325_033734/train_20260325_033735.log b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/train_20260325_033735.log new file mode 100644 index 0000000000000000000000000000000000000000..f91b4ca96c48c2e5423920088b1b68fcc4e0b799 --- /dev/null +++ b/syntheticSuccess/m9/arf/arf-m9-20260325_033734/train_20260325_033735.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a8a1cc00491ced348d620400f36ca8952ad6e1810daaae01cefcd5a1733f9dd +size 350 diff --git a/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/_bayesnet_generate.py b/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b5762974bab107d97e25f5f2db5378ebfd12e89e --- /dev/null +++ b/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/_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/m9/bayesnet/bayesnet-m9-20260321_080130/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=15326).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/m9/bayesnet/bayesnet-m9-20260321_080130/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/m9/bayesnet/bayesnet-m9-20260321_080130/bayesnet-m9-15326-20260330_065816.csv", index=False) +print(f"[BayesNet] Generated 15326 rows -> /work/output-SpecializedModels/m9/bayesnet/bayesnet-m9-20260321_080130/bayesnet-m9-15326-20260330_065816.csv") diff --git a/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/_bayesnet_train.py b/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..5beb70405a6f429fb727311915dda2e24c080f06 --- /dev/null +++ b/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/_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/m9/bayesnet/bayesnet-m9-20260321_080130/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/m9/bayesnet/bayesnet-m9-20260321_080130/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/m9/bayesnet/bayesnet-m9-20260321_080130/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m9/bayesnet/bayesnet-m9-20260321_080130/bayesnet_model.pkl") diff --git a/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/bayesnet-m9-1000-20260321_080228.csv b/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/bayesnet-m9-1000-20260321_080228.csv new file mode 100644 index 0000000000000000000000000000000000000000..ef54e6c92726c6de1d2dbcd800c187413e16c980 --- /dev/null +++ b/syntheticSuccess/m9/bayesnet/bayesnet-m9-20260321_080130/bayesnet-m9-1000-20260321_080228.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/syntheticSuccess/m9/realtabformer/rtf-m9-20260328_045425/realtabformer_features.json new file mode 100644 index 0000000000000000000000000000000000000000..7829b9601abc583fbbf277a2461ce66a2fe1c39c --- /dev/null +++ b/syntheticSuccess/m9/realtabformer/rtf-m9-20260328_045425/realtabformer_features.json @@ -0,0 +1,72 @@ +[ + { + "feature_name": "enrollee_id", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "city", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "city_development_index", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "gender", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "relevent_experience", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "enrolled_university", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "education_level", + "data_type": "categorical", + "is_target": false + }, + { + 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0.000759, + "example_values": [ + "50-99", + "10000+", + "<10", + "1000-4999", + "100-500" + ] + } + }, + { + "name": "company_type", + "role": "feature", + "semantic_type": "text", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.322393, + "unique_count": 6, + "unique_ratio": 0.000578, + "example_values": [ + "Pvt Ltd", + "Early Stage Startup", + "Public Sector", + "Funded Startup", + "NGO" + ] + } + }, + { + "name": "last_new_job", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.022054, + "unique_count": 6, + "unique_ratio": 0.0004, + "example_values": [ + "never", + "1", + "2", + ">4", + "4" + ] + } + }, + { + "name": "training_hours", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 241, + "unique_ratio": 0.015725, + "example_values": [ + "25", + "11", + "84", + "29", + "46" + ] + } + }, + { + "name": "target", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.00013, + "example_values": [ + "0.0", + "1.0" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/realtabformer/rtf-m9-20260328_045425/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/realtabformer/rtf-m9-20260328_045425/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/realtabformer/rtf-m9-20260328_045425/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/realtabformer/rtf-m9-20260328_045425/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/realtabformer/rtf-m9-20260328_045425/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/realtabformer/rtf-m9-20260328_045425/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m9/realtabformer/rtf-m9-20260328_045425/train_20260328_045426.log b/syntheticSuccess/m9/realtabformer/rtf-m9-20260328_045425/train_20260328_045426.log new file mode 100644 index 0000000000000000000000000000000000000000..d9cec3bee88ea360709e92e78106fcdc8bc67446 --- /dev/null +++ b/syntheticSuccess/m9/realtabformer/rtf-m9-20260328_045425/train_20260328_045426.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:48fd2801e53d3d6c268b49304db69e7b82b0a7805908814dfcebf5e34ca52471 +size 3476984 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/_tabddpm_sample.py b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..2c6b11de744398ba569779b0b407f9f04e8d283e --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_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 15326 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/config_sample_20260424_034322.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/m9/tabddpm/tabddpm-m9-20260424_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/m9/tabddpm/tabddpm-m9-20260424_033725/tabddpm-m9-15326-20260424_034322.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/tabddpm-m9-15326-20260424_034322.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/_tabddpm_train.py b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..851c28296936ba5ed00c26606be6550e2b545731 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_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/m9/tabddpm/tabddpm-m9-20260424_033725/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/config.toml b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..a85023536ba9330457127b5a24b27db18a2fb0b7 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 13 +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/m9/tabddpm/tabddpm-m9-20260424_033725/config_sample_20260424_034322.toml b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/config_sample_20260424_034322.toml new file mode 100644 index 0000000000000000000000000000000000000000..4cb8c35e837f8b1a6868664de9ea225b5ec6eeff --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/config_sample_20260424_034322.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 13 +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 = 15326 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/X_cat_train.npy b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..21022a6d928e727077767838a0b6ba6aaf3eeefb --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:06eb3595be90c4e9190c420620234c355b668e5632d2d869a64b85a080cae497 +size 1226208 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/X_num_train.npy b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..347f6b1f531a2cfcd4cda704f89380dad6c6dc49 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f7943c2adffab551acd527e1d55f012d3a59b1dcfe2bfb91963fe98eade5a49d +size 184040 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/info.json b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cccc935b42ee7e09dfac922d3de61502ca7d1ba1 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/info.json @@ -0,0 +1,44 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 3, + "n_cat_features": 10, + "train_size": 15326, + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12 + ], + "target_col_idx": [ + 13 + ], + "column_names": [ + "enrollee_id", + "city_development_index", + "training_hours", + "city", + "gender", + "relevent_experience", + "enrolled_university", + "education_level", + "major_discipline", + "experience", + "company_size", + "company_type", + "last_new_job", + "target" + ], + "num_classes": 2 +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/y_train.npy b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..9136ce05d9c3aef0b5a47772b659c19e001dae37 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/data/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc8bd737e275f3b3bdb41f9f4c775e33682aeb8b38f89dace80249eadf5064c3 +size 122736 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/gen_20260424_034322.log b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/gen_20260424_034322.log new file mode 100644 index 0000000000000000000000000000000000000000..92d1ca8f512a4885ae6ac96c78fdbcfa0ff29420 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/gen_20260424_034322.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e62f8eaef7c047f60431eaa84b1a08d1784f2c511112c92bd41352ce889602b +size 336233 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/input_snapshot.json b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..e6bbf5155a016c213addd7edd51f3aef217fc725 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m9", + "model": "tabddpm", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv", + "exists": true, + "size": 1582666, + "sha256": "a3f8afdc773b440d51abd0d3490ff6dd140f091235334aa58569b075b44a200a" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv", + "exists": true, + "size": 199049, + "sha256": "e502e558e93bf38ce6eb32cd839eebaf5a0e428bd8949a13d6f6bbc6c94b56b6" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv", + "exists": true, + "size": 198975, + "sha256": "ea619037db7eed6d1be2023c1b315852b421cf64e17dcb85bd3068d306a093a0" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_profile.json", + "exists": true, + "size": 6232, + "sha256": "4c610aaaca65a4d30b558d1400dd96c2bf90091fcf91257fbcb164d0072d5f20" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_contract_v1.json", + "exists": true, + "size": 7292, + "sha256": "260b8491ad4298f588ea417cbe5685f50711c85e61ee38789fb8bf3f7269178c" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_cat_train.npy b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..296fc3e1df56c70930135bed13a8922909c306c5 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5fb91bf804eaf8a26406a75833684b94d34f5b1e53c3134faa2787d5b63688ea +size 307111 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_cat_unnorm.npy b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_cat_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..963515acb78359fc139750a1de3f60c312242cbf --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_cat_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:526cafa1dde221ac1ed61bc510833eafa24ca6830651d5692529b5e762e42fde +size 1226208 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_num_train.npy b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b0174a85957ab8590dcbf8cbdb8f2b02dbf3086b --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2398ba422bcd4b19b2e8d635d70d8721ecef70db851e6ca33628d9695c991c45 +size 367952 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_num_unnorm.npy b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_num_unnorm.npy new file mode 100644 index 0000000000000000000000000000000000000000..841f05a3a73e04f6567002657f5f919a70d7b369 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/X_num_unnorm.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:afcc6e7a015825d189211d6ad63f7786205dfb403d06a353e6c9b21e0d4d9283 +size 367952 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/config.toml b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..4cb8c35e837f8b1a6868664de9ea225b5ec6eeff --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/output" +real_data_path = "/work/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/data" +model_type = "mlp" +num_numerical_features = 3 +device = "cuda:0" + +[model_params] +d_in = 13 +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 = 15326 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/info.json b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/info.json new file mode 100644 index 0000000000000000000000000000000000000000..cccc935b42ee7e09dfac922d3de61502ca7d1ba1 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/info.json @@ -0,0 +1,44 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 3, + "n_cat_features": 10, + "train_size": 15326, + "num_col_idx": [ + 0, + 1, + 2 + ], + "cat_col_idx": [ + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12 + ], + "target_col_idx": [ + 13 + ], + "column_names": [ + "enrollee_id", + "city_development_index", + "training_hours", + "city", + "gender", + "relevent_experience", + "enrolled_university", + "education_level", + "major_discipline", + "experience", + "company_size", + "company_type", + "last_new_job", + "target" + ], + "num_classes": 2 +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/loss.csv b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/loss.csv new file mode 100644 index 0000000000000000000000000000000000000000..437aa824297face1109dfbd6b599e08d2fab3568 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/loss.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c7fab452b3f0042d77a396a502c5ae1de93f9ebf5820f5af193d5972b79ed934 +size 1516 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/model.pt b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..04f9a2f67c750df41fe1b6c85d3311428772c327 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0479422f3a7e0adc238c3f1811dcd2955e5f75e2f403d1d535b4ef8db53bbf65 +size 832342 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/model_ema.pt b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/model_ema.pt new file mode 100644 index 0000000000000000000000000000000000000000..893b039a83554f2eb533edaee6b5d438f3fc144e --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/model_ema.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9d75f4b6cefe156b33306e001a0edaf0321bb13e0a8bace170f2da513f63de4e +size 833186 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/y_train.npy b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e493eef5fe21c727cd709561d24aa5701da5b80e --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/output/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7fe8c6602c0d5fc6597185c3373189a63a88ae88a42c37c96e9fa042d56cb457 +size 122736 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..ddd5199aed66b1b6fb226e8f963ac35bd3c271c2 --- /dev/null +++ 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"unique_ratio": 0.00144, + "example_values": [ + "5", + "4", + "13", + "3", + ">20" + ] + } + }, + { + "name": "company_size", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.311823, + "unique_count": 8, + "unique_ratio": 0.000759, + "example_values": [ + "50-99", + "10000+", + "<10", + "1000-4999", + "100-500" + ] + } + }, + { + "name": "company_type", + "role": "feature", + "semantic_type": "text", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.322393, + "unique_count": 6, + "unique_ratio": 0.000578, + "example_values": [ + "Pvt Ltd", + "Early Stage Startup", + "Public Sector", + "Funded Startup", + "NGO" + ] + } + }, + { + "name": "last_new_job", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.022054, + "unique_count": 6, + "unique_ratio": 0.0004, + "example_values": [ + "never", + "1", + "2", + ">4", + "4" + ] + } + }, + { + "name": "training_hours", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 241, + "unique_ratio": 0.015725, + "example_values": [ + "25", + "11", + "84", + "29", + "46" + ] + } + }, + { + "name": "target", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.00013, + "example_values": [ + "0.0", + "1.0" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabddpm/tabddpm-m9-20260424_033725/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/tabddpm-m9-15326-20260424_034322.csv b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/tabddpm-m9-15326-20260424_034322.csv new file mode 100644 index 0000000000000000000000000000000000000000..462c4a1cb0d3697418ffc1bb19b514610d9dfc9b --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/tabddpm-m9-15326-20260424_034322.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4030204a5617fec523e8e7a5a2840b527a7102c09f821c0fdcb6d05cf4e1c4de +size 1370955 diff --git a/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/train_20260424_033725.log b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/train_20260424_033725.log new file mode 100644 index 0000000000000000000000000000000000000000..165fa88392f8b5b68f02e1e8e9ae5c654ddedb01 --- /dev/null +++ b/syntheticSuccess/m9/tabddpm/tabddpm-m9-20260424_033725/train_20260424_033725.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f0d3a7c57be54184c1b4bb0147a1bea4056fd0136a9f74bc6f5f6f6d73ee7dd +size 898 diff --git a/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/_tabpfgen_generate.py b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..7f14ac9ae542c6b6f66527c6b1b2f540e3c6555d --- /dev/null +++ b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/_tabpfgen_generate.py @@ -0,0 +1,87 @@ +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_191739/m9/staged/public/train.csv") +target_col = "target" + +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 +target_n = int(15326) + +# 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 {target_n} rows via generate_regression") +X_syn, y_syn = gen.generate_regression(X, y, n_samples=target_n) + +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] + +# Ensure output row count is strictly aligned with target_n. +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat([syn_df.reset_index(drop=True), extra.reset_index(drop=True)], ignore_index=True) + else: + # Defensive fallback: if generator returns empty, bootstrap from training rows. + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError(f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}") +syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/tabpfgen-m9-15326-20260422_191741.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/tabpfgen-m9-15326-20260422_191741.csv") diff --git a/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/gen_20260422_191741.log b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/gen_20260422_191741.log new file mode 100644 index 0000000000000000000000000000000000000000..796313214c94e59f7ffbb5264e34f64b6a0aea3f --- /dev/null +++ b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/gen_20260422_191741.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7687d662119a29c59f79c8a9cad8610a6bfd6d4210c5ce5662afbbaf3601618c +size 1087 diff --git a/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/input_snapshot.json b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..a7976e3a03b008f727800a00db937403d4b350d5 --- /dev/null +++ b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m9", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv", + "exists": true, + "size": 1582666, + "sha256": "a3f8afdc773b440d51abd0d3490ff6dd140f091235334aa58569b075b44a200a" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv", + "exists": true, + "size": 199049, + "sha256": "e502e558e93bf38ce6eb32cd839eebaf5a0e428bd8949a13d6f6bbc6c94b56b6" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv", + "exists": true, + "size": 198975, + "sha256": "ea619037db7eed6d1be2023c1b315852b421cf64e17dcb85bd3068d306a093a0" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_profile.json", + "exists": true, + "size": 6232, + "sha256": "4c610aaaca65a4d30b558d1400dd96c2bf90091fcf91257fbcb164d0072d5f20" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m9/m9-dataset_contract_v1.json", + "exists": true, + "size": 7292, + "sha256": "260b8491ad4298f588ea417cbe5685f50711c85e61ee38789fb8bf3f7269178c" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..ddd5199aed66b1b6fb226e8f963ac35bd3c271c2 --- /dev/null +++ b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,291 @@ +{ + "dataset_id": "m9", + "target_column": "target", + "task_type": "classification", + "columns": [ + { + "name": "enrollee_id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 15326, + "unique_ratio": 1.0, + "example_values": [ + "32408", + "5965", + "32011", + "32184", + "13892" + ] + } + }, + { + "name": "city", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 122, + "unique_ratio": 0.00796, + "example_values": [ + "city_103", + "city_13", + "city_136", + "city_114", + "city_21" + ] + } + }, + { + "name": "city_development_index", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 92, + "unique_ratio": 0.006003, + "example_values": [ + "0.92", + "0.8270000000000001", + "0.897", + "0.9259999999999999", + "0.624" + ] + } + }, + { + "name": "gender", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.238353, + "unique_count": 3, + "unique_ratio": 0.000257, + "example_values": [ + "Male", + "Female", + "Other" + ] + } + }, + { + "name": "relevent_experience", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.00013, + "example_values": [ + "No relevent experience", + "Has relevent experience" + ] + } + }, + { + "name": "enrolled_university", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.02088, + "unique_count": 3, + "unique_ratio": 0.0002, + "example_values": [ + "no_enrollment", + "Part time course", + "Full time course" + ] + } + }, + { + "name": "education_level", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.024403, + "unique_count": 5, + "unique_ratio": 0.000334, + "example_values": [ + "Primary School", + "Masters", + "Graduate", + "High School", + "Phd" + ] + } + }, + { + "name": "major_discipline", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.148636, + "unique_count": 6, + "unique_ratio": 0.00046, + "example_values": [ + "STEM", + "Humanities", + "Business Degree", + "No Major", + "Other" + ] + } + }, + { + "name": "experience", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.003393, + "unique_count": 22, + "unique_ratio": 0.00144, + "example_values": [ + "5", + "4", + "13", + "3", + ">20" + ] + } + }, + { + "name": "company_size", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.311823, + "unique_count": 8, + "unique_ratio": 0.000759, + "example_values": [ + "50-99", + "10000+", + "<10", + "1000-4999", + "100-500" + ] + } + }, + { + "name": "company_type", + "role": "feature", + "semantic_type": "text", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.322393, + "unique_count": 6, + "unique_ratio": 0.000578, + "example_values": [ + "Pvt Ltd", + "Early Stage Startup", + "Public Sector", + "Funded Startup", + "NGO" + ] + } + }, + { + "name": "last_new_job", + "role": "feature", + "semantic_type": "categorical", + "nullable": true, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.022054, + "unique_count": 6, + "unique_ratio": 0.0004, + "example_values": [ + "never", + "1", + "2", + ">4", + "4" + ] + } + }, + { + "name": "training_hours", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 241, + "unique_ratio": 0.015725, + "example_values": [ + "25", + "11", + "84", + "29", + "46" + ] + } + }, + { + "name": "target", + "role": "target", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 0.00013, + "example_values": [ + "0.0", + "1.0" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/public_gate_report.json b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..321349d7d7e5ae14c7384836ae0b049df0f868e2 --- /dev/null +++ b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m9", + "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" + } + ], + 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"/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/m9/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/tabpfgen-m9-15326-20260422_191741.csv b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/tabpfgen-m9-15326-20260422_191741.csv new file mode 100644 index 0000000000000000000000000000000000000000..291145501e193f73e54e35981f843e0efb4e5feb --- /dev/null +++ b/syntheticSuccess/m9/tabpfgen/m9-migrated-20260422_193053/tabpfgen-m9-15326-20260422_191741.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:54587bfc4eddd99dfee3559fff0e0519e5a944e87fec32798cde3da9eb051165 +size 2164400 diff --git a/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_sample.py b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..1b934b82f3d0e3d73617ea408a62f62e7dcdafc6 --- /dev/null +++ b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114" +dataname = "tabsyn_m9" +output_csv = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.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 15326 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/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_train.py b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b3492cd736de3e883f229f002cc3dffd80f65dd7 --- /dev/null +++ b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114" +dataname = "tabsyn_m9" +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/m9/tabsyn/tabsyn-m9-20260421_005114/data/tabsyn_m9/X_cat_test.npy b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/data/tabsyn_m9/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e1099fc4906ec8d8a016289cbbabf019fcb6e837 --- /dev/null +++ b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/data/tabsyn_m9/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tabsyn/tabsyn-m9-20260421_005114/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/real.csv b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..a6b7f45a44b7a8ff0f8dd73b1e672bb8dabd59c9 --- /dev/null +++ b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:63528ad6a7339d36f3513325bd781b5219fd2fc0bb3e4ab28d347ff05297048a +size 685623 diff --git a/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/test.csv b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..84a2628aaeb072f6060e1de20e0ee9b6d58bbd5b --- /dev/null +++ b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/synthetic/tabsyn_m9/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c725a1357bb8b8b0baf466ae991f0c451b0d8d671612dc535a52d4a8bc19b2fc +size 76144 diff --git a/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.csv b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.csv new file mode 100644 index 0000000000000000000000000000000000000000..c92a60adce7154e7c16e22b7db1d134598cbfe85 --- /dev/null +++ b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/tabsyn-m9-15326-20260421_021410.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2eae4e8229ae2309319dead2c9d320eba2fe85b8143102d3faaf4f3adc62d596 +size 812058 diff --git a/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/train_20260421_005116.log b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/train_20260421_005116.log new file mode 100644 index 0000000000000000000000000000000000000000..11029746a6f9859aa3e061cce3de4f0eeaa03107 --- /dev/null +++ b/syntheticSuccess/m9/tabsyn/tabsyn-m9-20260421_005114/train_20260421_005116.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d83d33572d69d4e36f7568f5e6a39be49f52ef7576aa0ec8b3feec0fb4e7fa9d +size 3244267 diff --git a/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/_tvae_generate.py b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..87e81d4367ea88ac666a1de88e32c3b7f13332e0 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/models_300epochs/tvae_300epochs.pt") +samples = model.sample(15326) +samples.to_csv("/work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/tvae-m9-15326-20260330_065724.csv", index=False) +print(f"[TVAE] Generated 15326 rows -> /work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/tvae-m9-15326-20260330_065724.csv") diff --git a/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/_tvae_train.py b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0ef6756bd89e32fa946968aeb0a50294db167968 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/_tvae_train.py @@ -0,0 +1,16 @@ +import json, sys +import pandas as pd +from ctgan.data import read_csv +from ctgan.synthesizers.tvae import TVAE + +csv_path = "/work/DatasetNew/m9/m9-train.csv" +meta_path = "/work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/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/m9/tvae/tvae-m9-20260320_071415/gen_20260320_071808.log b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/gen_20260320_071808.log new file mode 100644 index 0000000000000000000000000000000000000000..7b54d1059007fad0ae88a33c648031b2ddc38f66 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/gen_20260320_071808.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6bff7a7807381233e8da476d42a0f25486aae5e26dbb97aa41873686f7dac00a +size 126 diff --git a/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/gen_20260330_065724.log b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/gen_20260330_065724.log new file mode 100644 index 0000000000000000000000000000000000000000..e1061c7da73c2b9c5d41477d226bddd3cb83bc76 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/gen_20260330_065724.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3964c4f5b8fefcca855c03d7fe5435543af6c93710fdb3a2fe5ed09d18e7de32 +size 128 diff --git a/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/input_snapshot.json b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0cc7041b5a4c895e32c5e7a60ed2d796c7c51313 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m9", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv", + "exists": true, + "size": 1582666, + "sha256": "a3f8afdc773b440d51abd0d3490ff6dd140f091235334aa58569b075b44a200a" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv", + "exists": true, + "size": 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b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/models_300epochs/train_20260320_071415.log new file mode 100644 index 0000000000000000000000000000000000000000..399ba9e2685fd7300f10294e9b77d0b5670834f1 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/models_300epochs/train_20260320_071415.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:17373901e7c1fdf335610f6d812e9e9d0c630de2d1b9faa2915831481a2c5e97 +size 171 diff --git a/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/models_300epochs/tvae_300epochs.pt b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..b349531ff6760bd6a9df3e1b13e451395d1ac5d7 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a1a65107530e993ac3f1cbc0bdf472e7388a08713027f69cad5bb63b38e5f60 +size 879532 diff --git a/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..ddd5199aed66b1b6fb226e8f963ac35bd3c271c2 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,291 @@ +{ + "dataset_id": "m9", + "target_column": "target", + "task_type": "classification", + "columns": [ + { + "name": "enrollee_id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 15326, + "unique_ratio": 1.0, + "example_values": [ + "32408", + "5965", + "32011", + "32184", + "13892" + ] + } + }, + { + "name": "city", + "role": "feature", + "semantic_type": "categorical", + 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0000000000000000000000000000000000000000..321349d7d7e5ae14c7384836ae0b049df0f868e2 --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m9", + "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": "target", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m9/m9-test.csv" + } +} \ No newline at end of file diff --git a/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/public_gate/staged_input_manifest.json b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9b1e2e66d4c1f2508ac8af985f5fe0580354e78e --- /dev/null +++ b/syntheticSuccess/m9/tvae/tvae-m9-20260320_071415/public_gate/staged_input_manifest.json @@ -0,0 +1,296 @@ +{ + "dataset_id": "m9", + "target_column": "target", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m9/tvae/tvae-m9-20260320_071415/staged/public/val.csv", + "test_csv": 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