diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/_bayesnet_generate.py b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..6ad57aa6d2fe676d2815324487279784ed75aeb8 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/_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/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl", "rb") as f: + plugin = pickle.load(f) +syn = plugin.generate(count=10368).dataframe() + +# Restore zero-variance columns that were dropped during training +const_path = "/work/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/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/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv", index=False) +print(f"[BayesNet] Generated 10368 rows -> /work/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv") diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/_bayesnet_train.py b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0289b5f475a8703148a672f1cec2699a8a092707 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/_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/c7/bayesnet/bayesnet-c7-20260321_061816/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/c7/bayesnet/bayesnet-c7-20260321_061816/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/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl", "wb") as f: + pickle.dump(plugin, f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl") diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-1000-20260321_061903.csv b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-1000-20260321_061903.csv new file mode 100644 index 0000000000000000000000000000000000000000..0ad76fe0492ac805a816eea77ebd477f085fb237 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-1000-20260321_061903.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a082b4d0235d47db0678b4525f42d70a94759dbd763c798f07df7236a908e891 +size 81759 diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv new file mode 100644 index 0000000000000000000000000000000000000000..ccced25053d3ab70f20eac4e0288e55bd832df62 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6362c2f52025591f90cd98a82c2a4f59c83d60f6b043b1e40450dad3066099ae +size 847021 diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..3e84a00dffdf3d65058913e02479a7a71cb3b137 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f4d7e04ef9de203ea1e07b25a9caac563c8bf34884957dbac67a8ddd149ab7e2 +size 1180522 diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/const_cols.json b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/const_cols.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260321_061903.log b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260321_061903.log new file mode 100644 index 0000000000000000000000000000000000000000..6a7e6e084a3fcadc7c6995c888bd96adbbc80009 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260321_061903.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a8ad19b389b2fb228b4232d0425ff2a5262c916f39eb9f715d485cff20b23c8 +size 480 diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260330_065316.log b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260330_065316.log new file mode 100644 index 0000000000000000000000000000000000000000..0f0ca9836baccb06563f086e9e85824fa6217952 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260330_065316.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c8a9f2d9052018cdd13942a9a359eb52d98f22fdc71cb2df8060f7653b8337b5 +size 482 diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/input_snapshot.json b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..b61f053445c3620fe2db11628eda0f82572c1916 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "bayesnet", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": 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a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "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": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/public_gate_report.json b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "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", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv" + } +} \ No newline at end of file diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/staged_input_manifest.json b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..373ee5a46b013e97c3699886bfe2b78fb933cbda --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/staged_input_manifest.json @@ -0,0 +1,188 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "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": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + 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0000000000000000000000000000000000000000..b2221c783639c69f916dd17f64ee828be06b1dc5 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/runtime_result.json @@ -0,0 +1,14 @@ +{ + "dataset_id": "c7", + "model": "bayesnet", + "run_id": "bayesnet-c7-20260321_061816", + "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/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv" + } +} \ No newline at end of file diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/adapter_report.json b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..c43a27d82fecc34bf23835ef9b980e04ca120ffe --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/adapter_report.json @@ -0,0 +1,7 @@ +{ + "adapter_ready_status": "pass", + "adapter_fail_reason_code": null, + "adapter_fail_detail": null, + "adapter_transforms_applied": [], + "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/model_input_manifest.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/adapter_transforms_applied.json b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/model_input_manifest.json b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..813ded23b6c6f1a6e22e034b7e334f86b6062acf --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "bayesnet", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "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": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/staged_input_manifest.json", + "train_csv": 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new file mode 100644 index 0000000000000000000000000000000000000000..0e23df6bcfb7ecfd44dfefc3d0ca0bf6b6aebc60 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/staged_features.json @@ -0,0 +1,47 @@ +[ + { + "feature_name": "parents", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "has_nurs", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "form", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "children", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "finance", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "social", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "health", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "class", + "data_type": "categorical", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..cb9d7081ca745e9eed28a35168872d450c4a2a44 --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058 +size 106030 diff --git a/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c8e5e0c5031fc7e8e25213132c1231950ec29da --- /dev/null +++ b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv @@ -0,0 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b/syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/train_20260321_061816.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ac5d75bda1da96923ed90d790fc4743707eff8968b962898056f8d855f54505 +size 465 diff --git a/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/_tabddpm_sample.py b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..a5e6a37ca660f5f3e9fb4e51cdf4cb3b00e7c7d1 --- /dev/null +++ b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_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 10368 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/config_sample_20260422_211650.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/c7/tabddpm/tabddpm-c7-20260422_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/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/_tabddpm_train.py b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ca34e9c5a89803ca4601fa39e091ec5dbc675ff7 --- /dev/null +++ b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_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/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_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/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..fb3507c47c65e585ed553137ac897af3cae2fd41 --- /dev/null +++ b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/output" +real_data_path = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 8 +num_classes = 5 +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/c7/tabddpm/tabddpm-c7-20260422_211246/config_sample_20260422_211650.toml b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/config_sample_20260422_211650.toml new file mode 100644 index 0000000000000000000000000000000000000000..8291818e700d28d267bde633242bcdd5dd298a4b --- /dev/null +++ b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/config_sample_20260422_211650.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/output" +real_data_path = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/data" +model_type = "mlp" +num_numerical_features = 0 +device = "cuda:0" + +[model_params] +d_in = 8 +num_classes = 5 +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 = 10368 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/data/X_cat_train.npy b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..5088dd2f36e0ffeae013fe0c5cdc769d9d88d33b --- /dev/null +++ b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe170020f0e97be3d763c4207ff95efc4e981264660ef6397fcf5f6e62d31eec +size 663680 diff --git a/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/data/info.json b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/data/info.json new 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b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/staged/tabddpm/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7e392a26c0b40aa9b60261ccfc731641c529a0e2 --- /dev/null +++ b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/staged/tabddpm/model_input_manifest.json @@ -0,0 +1,190 @@ +{ + "dataset_id": "c7", + "model": "tabddpm", + "target_column": "class", + "task_type": "classification", + "column_schema": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "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": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv new file mode 100644 index 0000000000000000000000000000000000000000..4fcd705ed6cebe4635ec23ef3d88d70ade1311cd --- /dev/null +++ b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e47f61612fd6e90d460223dfc4141b04efea2493a9b73535ec40c7ee9c5939af +size 373315 diff --git a/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/train_20260422_211247.log b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/train_20260422_211247.log new file mode 100644 index 0000000000000000000000000000000000000000..1131680952d1e73a1b937c0ce5947165913d9aab --- /dev/null +++ b/syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/train_20260422_211247.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f028e706e08a19c4a934b82131e1effca68e7b9828a75b6b087059ef0d8381ed +size 807 diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/_tabsyn_sample.py b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..775768d8f0d8ea7c185e9bae06c95d8ce836e4e4 --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446" +dataname = "tabsyn_c7" +output_csv = "/work/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/tabsyn-c7-10368-20260421_004501.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 10368 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/c7/tabsyn/tabsyn-c7-20260420_233446/_tabsyn_train.py b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..2cb2fb84542ff650609d6bfa869ae61e30826af9 --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/_tabsyn_train.py @@ -0,0 +1,62 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446" +dataname = "tabsyn_c7" +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/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_cat_test.npy b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..10356328239808f8ebf9a94ee11860628bc2f5aa --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cd640bebcc215e08eccfdd043b2feb09235ed70b8137fa742fcdade2660724e9 +size 72704 diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_cat_train.npy b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..2c4b97ef4d196962b046db9b1ad3e3b02b707da1 --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d0d4c33c78b19bafc79c335032c730b93e22edcf5857302d91c15998364d493b +size 653312 diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_num_test.npy b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..3cf8aaa49467a936f5a6b472c97695e948dbf6fb --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:609442f82a0f440f7756d8240e3512054c95f71c0a4f3b2180049dcab97cc0e3 +size 5312 diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_num_train.npy b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..f60e2e9f02df650885a75042aeb6b1a5589ff385 --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d5f7721f8c478bb809250f67f050c1842ca61048c92968b6e3f4c9ca0c0bb7b9 +size 46784 diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/info.json b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/info.json new file mode 100644 index 0000000000000000000000000000000000000000..139ad68b7f95098bea50ae281686f83f91192494 --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/data/tabsyn_c7/info.json @@ -0,0 +1,105 @@ +{ + "name": "tabsyn_c7", + "task_type": "multiclass", + "n_num_features": 1, + "n_cat_features": 7, + "train_size": 11664, + "num_col_idx": [ + 0 + ], + "cat_col_idx": [ + 1, + 2, + 3, + 4, + 5, + 6, + 7 + ], + "target_col_idx": [ + 8 + ], + "column_names": [ + "parents", + "has_nurs", + "form", + "children", + "housing", + "finance", + "social", + "health", + "class" + ], + "train_num": 11664, + "test_num": 1296, + "header": 0, + "file_type": "csv", + "data_path": "data/tabsyn_c7/train.csv", + "test_path": null, + "idx_mapping": { + "0": 0, + "1": 1, + "2": 2, + "3": 3, + "4": 4, + "5": 5, + "6": 6, 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0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "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": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabsyn/tabsyn-c7-20260420_233446/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/synthetic/tabsyn_c7/real.csv b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/synthetic/tabsyn_c7/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..5d64b824eaca7ec3d9eb36cda6f600c8c0cedfa0 --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/synthetic/tabsyn_c7/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b982b7a55b4fc4e84901c92fd913136c195362e5f6933ec1a6bf0d0595fe1e76 +size 210019 diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/synthetic/tabsyn_c7/test.csv b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/synthetic/tabsyn_c7/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..d0592f495903848668ae667620ccd291a443353c --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/synthetic/tabsyn_c7/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef18313fe934718eeb4a24ef415da12acfedc7c21dae50bdf67f9c6e47ee8e66 +size 23395 diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/tabsyn-c7-10368-20260421_004501.csv b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/tabsyn-c7-10368-20260421_004501.csv new file mode 100644 index 0000000000000000000000000000000000000000..d1b90f4ca4b057fd4929d94803bb82c0d5b7920b --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/tabsyn-c7-10368-20260421_004501.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5b3f2a066c4027c9422e5056dfa943a9788abea8080aaa1259ca7c2cd2a60ca0 +size 233347 diff --git a/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/train_20260420_233446.log b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/train_20260420_233446.log new file mode 100644 index 0000000000000000000000000000000000000000..cf8cec13a9f44f736b8b0d1fbcd0b8b3b20462e0 --- /dev/null +++ b/syntheticSuccess/c7/tabsyn/tabsyn-c7-20260420_233446/train_20260420_233446.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d2458122d40859ae385d3e930ab90c49c0ba76234d7c4ffb447381faedc7c957 +size 2620627 diff --git a/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/_tvae_generate.py b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5a6c3a341cdaebe212697824c3111e2938b6a147 --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/c7/tvae/tvae-c7-20260321_070722/models_300epochs/tvae_300epochs.pt") +samples = model.sample(10368) +samples.to_csv("/work/output-SpecializedModels/c7/tvae/tvae-c7-20260321_070722/tvae-c7-10368-20260330_065302.csv", index=False) +print(f"[TVAE] Generated 10368 rows -> /work/output-SpecializedModels/c7/tvae/tvae-c7-20260321_070722/tvae-c7-10368-20260330_065302.csv") diff --git a/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/_tvae_train.py b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..dec654376d63bd8cec097231fd61edde67126929 --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/_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/c7/tvae/tvae-c7-20260321_070722/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/c7/tvae/tvae-c7-20260321_070722/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/c7/tvae/tvae-c7-20260321_070722/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/c7/tvae/tvae-c7-20260321_070722/gen_20260321_070932.log b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/gen_20260321_070932.log new file mode 100644 index 0000000000000000000000000000000000000000..6588c4a625d8be6b2461f5d3bb3d55fdccec0f53 --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/gen_20260321_070932.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a4c62a09c7b61c41351777af1a5bd1e4efe9301e2a2a68cbbd1e807ec298246 +size 126 diff --git a/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/gen_20260330_065302.log b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/gen_20260330_065302.log new file mode 100644 index 0000000000000000000000000000000000000000..e2cddc6bef6064fccc39c9aabb215bc09f17f9fe --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/gen_20260330_065302.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:175b1b68749c373f88efe97385d5f90bb16ab3cda3d22ab02ab44cd233008367 +size 128 diff --git a/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/input_snapshot.json b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..efb8007253c004156220915598545e76948d4591 --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c7", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv", + "exists": true, + "size": 857718, + "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv", + "exists": true, + "size": 107489, + "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv", + "exists": true, + "size": 107327, + "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json", + "exists": true, + "size": 4014, + "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json", + "exists": true, + "size": 4759, + "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/models_300epochs/train_20260321_070722.log b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/models_300epochs/train_20260321_070722.log new file mode 100644 index 0000000000000000000000000000000000000000..10593d8a63cd89ddba16ac1429bf0e7ec40f45b7 --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/models_300epochs/train_20260321_070722.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5d763a6af4b2c1ea6494815f877c23f7f4a9e780ad1b14a1a9a2adeedb21675 +size 170 diff --git a/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/models_300epochs/tvae_300epochs.pt b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c2430ac3faf721e0ae403f5cb5dd73510821badd --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9a29346cadb27080b0c38b6d143e1eddf8a9008feeffd2a0780565ae7b224cea +size 536748 diff --git a/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..3ea4859ec2aa245a19531b254f45d87237c23069 --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,183 @@ +{ + "dataset_id": "c7", + "target_column": "class", + "task_type": "classification", + "columns": [ + { + "name": "parents", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "usual", + "pretentious", + "great_pret" + ] + } + }, + { + "name": "has_nurs", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "very_crit", + "critical", + "improper", + "less_proper", + "proper" + ] + } + }, + { + "name": "form", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "complete", + "completed", + "incomplete", + "foster" + ] + } + }, + { + "name": "children", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 4, + "unique_ratio": 0.000386, + "example_values": [ + "1", + "3", + "2", + "more" + ] + } + }, + { + "name": "housing", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "less_conv", + "convenient", + "critical" + ] + } + }, + { + "name": "finance", + "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": 0.000193, + "example_values": [ + "convenient", + "inconv" + ] + } + }, + { + "name": "social", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "slightly_prob", + "nonprob", + "problematic" + ] + } + }, + { + "name": "health", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 0.000289, + "example_values": [ + "recommended", + "priority", + "not_recom" + ] + } + }, + { + "name": "class", + "role": "target", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 5, + "unique_ratio": 0.000482, + "example_values": [ + "priority", + "spec_prior", + "not_recom", + "very_recom", + "recommend" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/public_gate/public_gate_report.json b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..3328fe59cd490a424e50f931e9eedba9e81d1cec --- /dev/null +++ b/syntheticSuccess/c7/tvae/tvae-c7-20260321_070722/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "c7", + "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", + "task_type": "classification", + "input_splits": { + "train": 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