diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/_arf_generate.py b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..e534570697fc6e69b4dc4ba5b6fd160fbf2fb8f0 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/_arf_generate.py @@ -0,0 +1,6 @@ +import pickle +with open("/work/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=304887) +syn.to_csv("/work/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/arf-m11-304887-20260330_065931.csv", index=False) +print(f"[ARF] Generated 304887 rows -> /work/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/arf-m11-304887-20260330_065931.csv") diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/_arf_train.py b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..555dc91364b99ff54417e26b5ce69722a4bc1839 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/_arf_train.py @@ -0,0 +1,19 @@ +import pickle +import pandas as pd +from arfpy import arf + +df = pd.read_csv("/work/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/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/m11/arf/arf-m11-20260325_034835/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/arf_model.pkl") diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf-m11-1000-20260325_044045.csv b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf-m11-1000-20260325_044045.csv new file mode 100644 index 0000000000000000000000000000000000000000..7d71f32de6661ae8b6b67cf3278cb832dbb037ad --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf-m11-1000-20260325_044045.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c99faf4ead6dd75b90907c96868bf8600c7ed9018f11ea9f52691e2e9fb7de06 +size 136611 diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf-m11-304887-20260330_065931.csv b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf-m11-304887-20260330_065931.csv new file mode 100644 index 0000000000000000000000000000000000000000..ecab7921c07d9163136ed3d64dac48b51309d5fa --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf-m11-304887-20260330_065931.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8903e64c0daaa9af5cbd9f3a6a813f4a6e2c820ec422163fcdad9eb090038701 +size 41524330 diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf_model.pkl b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..b4d0b346195b7dc81450651f1bb82818ad3a0aa2 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47ef9337ee7351af0f33c5f83833724a01074a576f48d309a62de14dc03b46ee +size 775571434 diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/gen_20260325_044045.log b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/gen_20260325_044045.log new file mode 100644 index 0000000000000000000000000000000000000000..6d00b6c6b183ff257552129fce453415dc336894 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/gen_20260325_044045.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3dbdf308effdc71f88c05efa4fd9f08819d653ed7802b275c3bb274ee7519d53 +size 1389 diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/gen_20260330_065931.log b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/gen_20260330_065931.log new file mode 100644 index 0000000000000000000000000000000000000000..820e5ae28617618cf8b39f60b4ffbe96d5751cc4 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/gen_20260330_065931.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:87cf5a7240dfd4334376f5c86b0cf004f019c7e65f20944088c6721bd3900137 +size 1393 diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/input_snapshot.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..41ce1f85101891f548d117f67a49596f0fc72bd6 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m11", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-train.csv", + "exists": true, + "size": 17450561, + "sha256": "01325179667586bada6e9b69ae7d1fe3f59df8ff4ddc3f41822af5e99f6b6e32" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-val.csv", + "exists": true, + "size": 2181590, + "sha256": "1066848f54a0895112eb7fa66933159bc43ebd8ec01b60d70aa6b1a9b87d1db1" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-test.csv", + "exists": true, + "size": 2181600, + "sha256": "bf23f0509554f8ee3a54a40d5fb242b0191a63d0d36c2a0f4c066ee89939b168" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m11/m11-dataset_profile.json", + "exists": true, + "size": 5042, + "sha256": "16ed6d26c47d5128e66a3420dfa9ed6baf8bf95b281df3cf6ab41f304f623433" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m11/m11-dataset_contract_v1.json", + "exists": true, + "size": 5995, + "sha256": "fd026c5f7619286326c84b02dceae6378a6ef481b60818d54b9b7ac1a77c79f8" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/normalized_schema_snapshot.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..bf8c63411cc620aa163585498d096c3a102abb61 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,242 @@ +{ + "dataset_id": "m11", + "target_column": "Previously_Insured", + "task_type": "classification", + "columns": [ + { + "name": "id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.065598, + "example_values": [ + "117073", + "228488", + "340589", + "168907", + "342998" + ] + } + }, + { + "name": "Gender", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "Male", + "Female" + ] + } + }, + { + "name": "Age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.000216, + "example_values": [ + "52", + "49", + "25", + "43", + "35" + ] + } + }, + { + "name": "Driving_License", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "1", + "0" + ] + } + }, + { + "name": "Region_Code", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 53, + "unique_ratio": 0.000174, + "example_values": [ + "0.0", + "28.0", + "15.0", + "8.0", + "24.0" + ] + } + }, + { + "name": "Previously_Insured", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Vehicle_Age", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 1e-05, + "example_values": [ + "1-2 Year", + "< 1 Year", + "> 2 Years" + ] + } + }, + { + "name": "Vehicle_Damage", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "Yes", + "No" + ] + } + }, + { + "name": "Annual_Premium", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.065598, + "example_values": [ + "2630.0", + "72227.0", + "26215.0", + "51539.0", + "30355.0" + ] + } + }, + { + "name": "Policy_Sales_Channel", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 152, + "unique_ratio": 0.000499, + "example_values": [ + "26.0", + "120.0", + "152.0", + "7.0", + "160.0" + ] + } + }, + { + "name": "Vintage", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 290, + "unique_ratio": 0.000951, + "example_values": [ + "27", + "71", + "42", + "52", + "138" + ] + } + }, + { + "name": "Response", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "0", + "1" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/public_gate_report.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..080193a6ba27c1babdca4764364b9b7b8a618b37 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m11", + "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": "Previously_Insured", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-test.csv" + } +} \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/staged_input_manifest.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..945efba2ac3c0e6ee4873c059bb4d009933e9710 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/public_gate/staged_input_manifest.json @@ -0,0 +1,247 @@ +{ + "dataset_id": "m11", + "target_column": "Previously_Insured", + "task_type": "classification", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/public_gate/public_gate_report.json", + "column_schema": [ + { + "name": "id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.065598, + "example_values": [ + "117073", + "228488", + "340589", + "168907", + "342998" + ] + } + }, + { + "name": "Gender", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "Male", + "Female" + ] + } + }, + { + "name": "Age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.000216, + "example_values": [ + "52", + "49", + "25", + "43", + "35" + ] + } + }, + { + "name": "Driving_License", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "1", + "0" + ] + } + }, + { + "name": "Region_Code", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 53, + "unique_ratio": 0.000174, + "example_values": [ + "0.0", + "28.0", + "15.0", + "8.0", + "24.0" + ] + } + }, + { + "name": "Previously_Insured", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Vehicle_Age", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 1e-05, + "example_values": [ + "1-2 Year", + "< 1 Year", + "> 2 Years" + ] + } + }, + { + "name": "Vehicle_Damage", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "Yes", + "No" + ] + } + }, + { + "name": "Annual_Premium", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.065598, + "example_values": [ + "2630.0", + "72227.0", + "26215.0", + "51539.0", + "30355.0" + ] + } + }, + { + "name": "Policy_Sales_Channel", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 152, + "unique_ratio": 0.000499, + "example_values": [ + "26.0", + "120.0", + "152.0", + "7.0", + "160.0" + ] + } + }, + { + "name": "Vintage", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 290, + "unique_ratio": 0.000951, + "example_values": [ + "27", + "71", + "42", + "52", + "138" + ] + } + }, + { + "name": "Response", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "0", + "1" + ] + } + } + ] +} \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/runtime_result.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/runtime_result.json new file mode 100644 index 0000000000000000000000000000000000000000..15a85e921b150f3996e59f6f54b8d4b2102bf4e7 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/runtime_result.json @@ -0,0 +1,14 @@ +{ + "dataset_id": "m11", + "model": "arf", + "run_id": "arf-m11-20260325_034835", + "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/m11/arf/arf-m11-20260325_034835/arf-m11-304887-20260330_065931.csv" + } +} \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/adapter_report.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..b81318d34fe2f83f3c6c18ebe5e641fbfd5f510c --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/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/m11/arf/arf-m11-20260325_034835/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/adapter_transforms_applied.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/model_input_manifest.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..98fe4c48a5758f29cc3a70ef72a2fcfbb5e379e4 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/arf/model_input_manifest.json @@ -0,0 +1,249 @@ +{ + "dataset_id": "m11", + "model": "arf", + "target_column": "Previously_Insured", + "task_type": "classification", + "column_schema": [ + { + "name": "id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.065598, + "example_values": [ + "117073", + "228488", + "340589", + "168907", + "342998" + ] + } + }, + { + "name": "Gender", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "Male", + "Female" + ] + } + }, + { + "name": "Age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.000216, + "example_values": [ + "52", + "49", + "25", + "43", + "35" + ] + } + }, + { + "name": "Driving_License", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "1", + "0" + ] + } + }, + { + "name": "Region_Code", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 53, + "unique_ratio": 0.000174, + "example_values": [ + "0.0", + "28.0", + "15.0", + "8.0", + "24.0" + ] + } + }, + { + "name": "Previously_Insured", + "role": "target", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "Vehicle_Age", + "role": "feature", + "semantic_type": "text", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "keep_raw", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 3, + "unique_ratio": 1e-05, + "example_values": [ + "1-2 Year", + "< 1 Year", + "> 2 Years" + ] + } + }, + { + "name": "Vehicle_Damage", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "Yes", + "No" + ] + } + }, + { + "name": "Annual_Premium", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.065598, + "example_values": [ + "2630.0", + "72227.0", + "26215.0", + "51539.0", + "30355.0" + ] + } + }, + { + "name": "Policy_Sales_Channel", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 152, + "unique_ratio": 0.000499, + "example_values": [ + "26.0", + "120.0", + "152.0", + "7.0", + "160.0" + ] + } + }, + { + "name": "Vintage", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 290, + "unique_ratio": 0.000951, + "example_values": [ + "27", + "71", + "42", + "52", + "138" + ] + } + }, + { + "name": "Response", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "0", + "1" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/arf/arf-m11-20260325_034835/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/staged_features.json b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..17718d348f1c487e5e40f83b26d73659d71a2ab7 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/staged_features.json @@ -0,0 +1,62 @@ +[ + { + "feature_name": "id", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Gender", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Driving_License", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Region_Code", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Previously_Insured", + "data_type": "binary", + "is_target": true + }, + { + "feature_name": "Vehicle_Age", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "Vehicle_Damage", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Annual_Premium", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Policy_Sales_Channel", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Vintage", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Response", + "data_type": "binary", + "is_target": false + } +] \ No newline at end of file diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/test.csv b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..60f06794190d5ae30c52339de8d35314cbbcd55b --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45a031a23f36e6dec485a2c50a0314a1e7d92b0f084fe2fe226b068b2781783d +size 2143487 diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/train.csv b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..0b2402fec31a5a265e98680aef6ea40a037f14b5 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:929bf5af6a31a08475878fb8f509e60d9b9aa0a48344ec40053e242f33eb15aa +size 17145673 diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/val.csv b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..e576899dddbc20c2142f10c7293c6465a6255543 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd630014d7f4fb9141831d29ca94cf2849de0e2a950913df63df846778180600 +size 2143479 diff --git a/syntheticSuccess/m11/arf/arf-m11-20260325_034835/train_20260325_034841.log b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/train_20260325_034841.log new file mode 100644 index 0000000000000000000000000000000000000000..a0fa5cf42f2d127538f25beed41f6cf887460e69 --- /dev/null +++ b/syntheticSuccess/m11/arf/arf-m11-20260325_034835/train_20260325_034841.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:167d1edaba123a812ecdf298828a5e8b4f2660c286880f5bb881a5edf0fe43e4 +size 662 diff --git a/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/_bayesnet_generate.py b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..18cb8b7733c593d6c4114422e671cd7a0bb76f8c --- /dev/null +++ b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/_bayesnet_generate.py @@ -0,0 +1,104 @@ + +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.sampling import BayesianModelSampling + +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-SpecializedModels/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_model.pkl", "rb") as f: + bundle = pickle.load(f) + +network = bundle["network"] +inverse = bundle["inverse"] +cols = bundle["column_order"] +integer_columns = set(bundle.get("integer_columns") or []) +full_order = bundle.get("full_column_order") or cols +const_cols = bundle.get("const_cols") or {} + +num_rows = int(304887) +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=num_rows, show_progress=False) +raw = raw.reset_index(drop=True) +if len(raw) > num_rows: + raw = raw.iloc[:num_rows] +_tries = 0 +while len(raw) < num_rows and _tries < 64: + _tries += 1 + nextra = min(10000, num_rows - len(raw)) + more = sampler.forward_sample(size=max(nextra, 1), show_progress=False) + more = more.reset_index(drop=True) + if len(more) == 0: + break + raw = pd.concat([raw, more], ignore_index=True) + if len(raw) > num_rows: + raw = raw.iloc[:num_rows] + +out = pd.DataFrame(index=raw.index) +rng = np.random.default_rng() + +for c in cols: + if c in inverse["categorical"]: + levels = inverse["categorical"][c] + idx = raw[c].astype(int).to_numpy() + idx = np.clip(idx, 0, max(0, len(levels) - 1)) + out[c] = [levels[i] for i in idx] + else: + edges = np.asarray(inverse["continuous"][c], dtype=float) + if edges.size < 2: + out[c] = 0.0 + else: + nbin = edges.size - 1 + res = [] + for k in raw[c].astype(int).to_numpy(): + k = int(k) + if k < 0: + k = 0 + if k >= nbin: + k = nbin - 1 + lo, hi = float(edges[k]), float(edges[k + 1]) + if hi < lo: + lo, hi = hi, lo + v = rng.uniform(lo, hi) + if c in integer_columns: + v = int(round(v)) + res.append(v) + out[c] = res + +final = pd.DataFrame(index=out.index) +for c in full_order: + if c in const_cols: + final[c] = const_cols[c] + elif c in out.columns: + final[c] = out[c] + +dtypes = bundle.get("original_dtypes") or {} +for c, dts in dtypes.items(): + if c not in final.columns: + continue + try: + if "int" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64") + elif "float" in dts: + final[c] = pd.to_numeric(final[c], errors="coerce") + except Exception: + pass + +if len(final) != num_rows: + final = final.iloc[:num_rows].copy() +final.to_csv("/work/output-SpecializedModels/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet-m11-304887-20260422_060305.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet-m11-304887-20260422_060305.csv") diff --git a/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/_bayesnet_train.py b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ea4b44172e70b28ce0de179adb8bbd1d24383b05 --- /dev/null +++ b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/_bayesnet_train.py @@ -0,0 +1,118 @@ + +import json +import pickle +import subprocess +import sys +import warnings + +import numpy as np +import pandas as pd +from pgmpy.estimators import TreeSearch +from pgmpy.models import DiscreteBayesianNetwork +warnings.filterwarnings("ignore", category=FutureWarning) + +def _ensure_cloudpickle(): + try: + import cloudpickle # noqa: F401 + except ModuleNotFoundError: + subprocess.check_call( + [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"], + ) + +_ensure_cloudpickle() + +with open("/work/output-SpecializedModels/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_coltypes.json", "r", encoding="utf-8") as _f: + colmeta = json.load(_f) +integer_columns = set(colmeta.get("integer_columns") or []) + +df = pd.read_csv("/work/output-SpecializedModels/m11/bayesnet/bayesnet-m11-20260422_060152/staged/public/train.csv") +df = df.dropna(axis=1, how="all") +full_column_order = list(df.columns) + +const_cols = {} +for col in list(df.columns): + if df[col].nunique(dropna=True) <= 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}'") + +const_path = "/work/output-SpecializedModels/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json") +with open(const_path, "w", encoding="utf-8") as _f: + json.dump({k: str(v) for k, v in const_cols.items()}, _f) + +inverse = {"categorical": {}, "continuous": {}} +enc = pd.DataFrame(index=df.index) +_n_samples = len(df) +_n_plan = sum( + 1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns +) +max_bins = 10 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 +if _n_plan > 55: + max_bins = 4 +print(f"[BayesNet] max_bins={max_bins} (cols_in_df={_n_plan}, rows={_n_samples})") + +for entry in colmeta["columns"]: + name = entry["name"] + if name not in df.columns: + continue + kind = entry["type"] + s = df[name] + if kind == "categorical": + uniques = sorted(s.dropna().unique(), key=lambda x: str(x)) + mapping = {str(v): i for i, v in enumerate(uniques)} + inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))] + enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int) + else: + s_num = pd.to_numeric(s, errors="coerce") + nu = int(s_num.nunique(dropna=True)) + q = min(max_bins, max(2, nu)) + if nu < 2: + enc[name] = np.zeros(len(s_num), dtype=int) + lo, hi = float(s_num.min()), float(s_num.max()) + inverse["continuous"][name] = [lo, hi] + else: + try: + _, bins = pd.qcut( + s_num, q=q, retbins=True, duplicates="drop" + ) + except Exception: + med = float(s_num.median()) + s2 = s_num.fillna(med) + _, bins = pd.qcut( + s2, q=min(q, 3), retbins=True, duplicates="drop" + ) + bins = np.asarray(bins, dtype=float) + lab = pd.cut( + s_num, bins=bins, labels=False, include_lowest=True + ) + enc[name] = lab.fillna(0).astype(int) + inverse["continuous"][name] = bins.tolist() + +print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)") + +enc_struct = enc +if len(enc) > 25000: + enc_struct = enc.sample(n=25000, random_state=0, replace=False) + print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})") +dag = TreeSearch(enc_struct).estimate(show_progress=False) +for col in enc.columns: + if col not in dag.nodes(): + dag.add_node(col) + print(f"[BayesNet] Added isolated node to DAG: {col}") +network = DiscreteBayesianNetwork(dag) +network.fit(enc) + +bundle = { + "network": network, + "inverse": inverse, + "column_order": list(enc.columns), + "full_column_order": full_column_order, + "integer_columns": list(integer_columns), + "original_dtypes": {c: str(df[c].dtype) for c in enc.columns}, + "const_cols": const_cols, +} +with open("/work/output-SpecializedModels/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_model.pkl") diff --git a/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet-m11-304887-20260422_060305.csv b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet-m11-304887-20260422_060305.csv new file mode 100644 index 0000000000000000000000000000000000000000..e21bea048fe32922be6f39f253dc87229293c258 --- /dev/null +++ b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet-m11-304887-20260422_060305.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:253872373992dc01f3fb45f0de4307ad32973d43616d2eefe286f6304c8168bd +size 41210166 diff --git a/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_coltypes.json b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..34c8bfc7e6b7efad46b81120b9ab9699bd6835a3 --- /dev/null +++ b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_coltypes.json @@ -0,0 +1,53 @@ +{ + "columns": [ + { + "name": "id", + "type": "continuous" + }, + { + "name": "Gender", + "type": "categorical" + }, + { + "name": "Age", + "type": "continuous" + }, + { + "name": "Driving_License", + "type": "categorical" + }, + { + "name": "Region_Code", + "type": "continuous" + }, + { + "name": "Previously_Insured", + "type": "categorical" + }, + { + "name": "Vehicle_Age", + "type": "categorical" + }, + { + "name": "Vehicle_Damage", + "type": "categorical" + }, + { + "name": "Annual_Premium", + "type": "continuous" + }, + { + "name": "Policy_Sales_Channel", + "type": "continuous" + }, + { + "name": "Vintage", + "type": "continuous" + }, + { + "name": "Response", + "type": "categorical" + } + ], + "integer_columns": [] +} \ No newline at end of file diff --git a/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_model.pkl b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..c1f3bcc1936d3b33b4bef1418b7e65960384515c --- /dev/null +++ b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/bayesnet_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2a5324aef0d1f9b598c2d75393569bf2d13655a5c878cf5055a7487d6cbd135 +size 7097 diff --git a/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/const_cols.json b/syntheticSuccess/m11/bayesnet/bayesnet-m11-20260422_060152/const_cols.json new file mode 100644 index 0000000000000000000000000000000000000000..9e26dfeeb6e641a33dae4961196235bdb965b21b --- /dev/null +++ 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"Vehicle_Damage", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "Annual_Premium", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Policy_Sales_Channel", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Vintage", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "Response", + "data_type": "binary", + "is_target": false + } +] \ No newline at end of file diff --git a/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/public/test.csv b/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..60f06794190d5ae30c52339de8d35314cbbcd55b --- /dev/null +++ b/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45a031a23f36e6dec485a2c50a0314a1e7d92b0f084fe2fe226b068b2781783d 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a/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/realtabformer/adapter_transforms_applied.json b/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/realtabformer/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/realtabformer/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/realtabformer/model_input_manifest.json b/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/realtabformer/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..fc1f71104c8ab46ecfa442040d34a9b1c8945d18 --- /dev/null +++ b/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/staged/realtabformer/model_input_manifest.json @@ -0,0 +1,249 @@ +{ 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/realtabformer/rtf-m11-20260424_175432/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/realtabformer/rtf-m11-20260424_175432/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/train_20260424_175434.log b/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/train_20260424_175434.log new file mode 100644 index 0000000000000000000000000000000000000000..237475103f578b1941ca769c1f0f6870298cb4f2 --- /dev/null +++ b/syntheticSuccess/m11/realtabformer/rtf-m11-20260424_175432/train_20260424_175434.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dc9e09374c7c3975af8e0fabc628da5a406da89c8e680a81fe769bb9dbab5c00 +size 70521616 diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/_tabddpm_sample.py b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/_tabddpm_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..d7fb90c6c617fd26443b342351786c8ba267fe8e --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/_tabddpm_sample.py @@ -0,0 +1,66 @@ +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 304887 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260425_061531.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/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/m11/tabddpm/tabddpm-m11-20260321_154503/tabddpm-m11-304887-20260425_061531.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/tabddpm-m11-304887-20260425_061531.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/_tabddpm_train.py b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ac806c6f1ccab49669a046ea42358f75bafacb43 --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/_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/m11/tabddpm/tabddpm-m11-20260321_154503/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config.toml b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..4fc16fe775c24b0d730a0a1eb110367b7a09ef76 --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/output" +real_data_path = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/data" +model_type = "mlp" +num_numerical_features = 6 +device = "cuda:0" + +[model_params] +d_in = 11 +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/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260424_201551.toml b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260424_201551.toml new file mode 100644 index 0000000000000000000000000000000000000000..1e4862cf38780317444d7edc4e84c111d9f87f64 --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260424_201551.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/output" +real_data_path = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/data" +model_type = "mlp" +num_numerical_features = 6 +device = "cuda:0" + +[model_params] +d_in = 11 +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 = 304887 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260425_023041.toml b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260425_023041.toml new file mode 100644 index 0000000000000000000000000000000000000000..1e4862cf38780317444d7edc4e84c111d9f87f64 --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260425_023041.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/output" +real_data_path = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/data" +model_type = "mlp" +num_numerical_features = 6 +device = "cuda:0" + +[model_params] +d_in = 11 +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 = 304887 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260425_061531.toml b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260425_061531.toml new file mode 100644 index 0000000000000000000000000000000000000000..1e4862cf38780317444d7edc4e84c111d9f87f64 --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/config_sample_20260425_061531.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/output" +real_data_path = "/work/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/data" +model_type = "mlp" +num_numerical_features = 6 +device = "cuda:0" + +[model_params] +d_in = 11 +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 = 304887 +batch_size = 1000 +seed = 0 diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/data/X_cat_test.npy b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/data/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..bf0afa51bff4b11a1aa0b6b405fdc7fe3c86d72f --- /dev/null +++ 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diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/output/data/info.json b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/output/data/info.json new file mode 100644 index 0000000000000000000000000000000000000000..c95f4d6a24832edfc2de197a17bf579fdfdb662f --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/output/data/info.json @@ -0,0 +1,40 @@ +{ + "name": "benchmark_dataset", + "task_type": "multiclass", + "n_num_features": 6, + "n_cat_features": 5, + "train_size": 304887, + "num_col_idx": [ + 0, + 1, + 2, + 3, + 4, + 5 + ], + "cat_col_idx": [ + 6, + 7, + 8, + 9, + 10 + ], + "target_col_idx": [ + 11 + ], + "column_names": [ + "id", + "Age", + "Region_Code", + "Annual_Premium", + "Policy_Sales_Channel", + "Vintage", + "Gender", + "Driving_License", + "Vehicle_Age", + "Vehicle_Damage", + "Response", + "Previously_Insured" + ], + "num_classes": 2 +} \ No newline at end of file diff --git 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"public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabddpm/tabddpm-m11-20260321_154503/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/tabddpm-m11-1000-20260321_155200.csv b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/tabddpm-m11-1000-20260321_155200.csv new file mode 100644 index 0000000000000000000000000000000000000000..430c1b3b71779af7c5bbd5536beb32738c4a61a3 --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/tabddpm-m11-1000-20260321_155200.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e24f6334becf266a4e6d044ddababb55d85c62163c5653b5eae41828c361f28 +size 85344 diff --git a/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/tabddpm-m11-304887-20260425_061531.csv b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/tabddpm-m11-304887-20260425_061531.csv new file mode 100644 index 0000000000000000000000000000000000000000..576b98f9802d097dc4c04b84cc73db4817e38ed9 --- /dev/null +++ b/syntheticSuccess/m11/tabddpm/tabddpm-m11-20260321_154503/tabddpm-m11-304887-20260425_061531.csv @@ 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b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_sample.py @@ -0,0 +1,39 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916" +dataname = "tabsyn_m11" +output_csv = "/work/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/tabsyn-m11-304887-20260427_005302.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 304887 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/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_train.py b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..3b9be84840f218365a0b5fcda7b160bb99dc879c --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/_tabsyn_train.py @@ -0,0 +1,63 @@ +import os, sys, subprocess + +work_dir = "/work/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916" +dataname = "tabsyn_m11" +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") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +_te = 1000 +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/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_test.npy b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..e5e4a16e567f2dcf0bb3c7fdcc48978c3bd19e22 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27480c329674397c8ad4fe21092011c17aed9b26a6da2d39f3d2ecf94ae7f66a +size 12195608 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_train.npy b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..e5e4a16e567f2dcf0bb3c7fdcc48978c3bd19e22 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27480c329674397c8ad4fe21092011c17aed9b26a6da2d39f3d2ecf94ae7f66a +size 12195608 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_num_test.npy b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..b6f069f6ba3943214509bdb62b3a22991a3bed3a --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d5f5c8cae5f4eff29b6dddc227ad5e86666e96261d2c244dc799c8833de4e3f +size 7317416 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_num_train.npy b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b6f069f6ba3943214509bdb62b3a22991a3bed3a --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d5f5c8cae5f4eff29b6dddc227ad5e86666e96261d2c244dc799c8833de4e3f +size 7317416 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/info.json b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/info.json new file mode 100644 index 0000000000000000000000000000000000000000..50fb3e4894342b5a19e4f9a19a7cfed3107f4173 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/data/tabsyn_m11/info.json @@ -0,0 +1,134 @@ +{ + "name": "tabsyn_m11", + "task_type": "multiclass", + "n_num_features": 6, + "n_cat_features": 5, + "train_size": 304887, + "num_col_idx": [ + 0, + 2, + 4, + 8, + 9, + 10 + ], + "cat_col_idx": [ + 1, + 3, + 6, + 7, + 11 + ], + "target_col_idx": [ + 5 + ], + "column_names": [ + "id", + "Gender", + "Age", + "Driving_License", + "Region_Code", + "Previously_Insured", + "Vehicle_Age", + "Vehicle_Damage", + "Annual_Premium", + "Policy_Sales_Channel", + "Vintage", + "Response" + ], + "train_num": 304887, + "test_num": 304887, + "header": 0, + "file_type": "csv", + "data_path": 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b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..0b2402fec31a5a265e98680aef6ea40a037f14b5 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:929bf5af6a31a08475878fb8f509e60d9b9aa0a48344ec40053e242f33eb15aa +size 17145673 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/val.csv b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..e576899dddbc20c2142f10c7293c6465a6255543 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd630014d7f4fb9141831d29ca94cf2849de0e2a950913df63df846778180600 +size 2143479 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/adapter_report.json b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..1fd07d1fe5d0379bfdd564980b608f5b56a81279 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/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/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/model_input_manifest.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/adapter_transforms_applied.json b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/model_input_manifest.json b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..30deb4a3edcfe66509fbd8cba3e54bb351b22d1e --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/staged/tabsyn/model_input_manifest.json @@ -0,0 +1,249 @@ +{ + "dataset_id": "m11", + "model": "tabsyn", + "target_column": "Previously_Insured", + "task_type": "classification", + "column_schema": [ + { + "name": "id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.065598, + "example_values": [ + "117073", + "228488", + "340589", + "168907", + "342998" + ] + } + }, + { + "name": "Gender", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "Male", + "Female" + ] + } + }, + { + "name": "Age", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 66, + "unique_ratio": 0.000216, + "example_values": [ + "52", + "49", + "25", + "43", + "35" + ] + } + }, + { + "name": "Driving_License", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + 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"feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 152, + "unique_ratio": 0.000499, + "example_values": [ + "26.0", + "120.0", + "152.0", + "7.0", + "160.0" + ] + } + }, + { + "name": "Vintage", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 290, + "unique_ratio": 0.000951, + "example_values": [ + "27", + "71", + "42", + "52", + "138" + ] + } + }, + { + "name": "Response", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 7e-06, + "example_values": [ + "0", + "1" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m11/tabsyn/tabsyn-m11-20260426_220916/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/real.csv b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/real.csv new file mode 100644 index 0000000000000000000000000000000000000000..348df9e604521bc5198952727ce4bf681c714521 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/real.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:beb736ac056307756ee2182709007b114cc1911ea976b608e4e002c4c7f782b4 +size 13345416 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/test.csv b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..348df9e604521bc5198952727ce4bf681c714521 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/synthetic/tabsyn_m11/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:beb736ac056307756ee2182709007b114cc1911ea976b608e4e002c4c7f782b4 +size 13345416 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/tabsyn-m11-304887-20260427_005302.csv b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/tabsyn-m11-304887-20260427_005302.csv new file mode 100644 index 0000000000000000000000000000000000000000..65b6906991e81082704cfc3c4a8f35f4a1bedd34 --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/tabsyn-m11-304887-20260427_005302.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45617388d16b2b8e23cca4b2419d9dfb696d0bb2b2732e715b02640cb477be67 +size 16438377 diff --git a/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/train_20260426_220925.log b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/train_20260426_220925.log new file mode 100644 index 0000000000000000000000000000000000000000..0ab3830f39824b734a9bea157fa63d73a9b7fdcc --- /dev/null +++ b/syntheticSuccess/m11/tabsyn/tabsyn-m11-20260426_220916/train_20260426_220925.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97076516c9a29b5f48347ca4e76a1dc0f714a9fa83ead2eaa1368b1fc17b125d +size 9228025 diff --git a/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/_tvae_generate.py b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..b0dbbfd516cb9bcbd9e9a515b8de7e1275a01085 --- /dev/null +++ b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/_tvae_generate.py @@ -0,0 +1,5 @@ +from ctgan.synthesizers.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/models_300epochs/tvae_300epochs.pt") +samples = model.sample(304887) +samples.to_csv("/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/tvae-m11-304887-20260330_065910.csv", index=False) +print(f"[TVAE] Generated 304887 rows -> /work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/tvae-m11-304887-20260330_065910.csv") diff --git a/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/_tvae_train.py b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..cc85589492a9cb0e0e802406b68fc9c0ef8820f3 --- /dev/null +++ b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/_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/m11/tvae/tvae-m11-20260328_052639/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/m11/tvae/tvae-m11-20260328_052639/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/m11/tvae/tvae-m11-20260328_052639/gen_20260328_133409.log b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/gen_20260328_133409.log new file mode 100644 index 0000000000000000000000000000000000000000..332e93e5526fa9d46298b7189bf3090aad35e9f1 --- /dev/null +++ b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/gen_20260328_133409.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49973cba0b17e4ce2bfb6e29a1055e4bf58315c8fc28036f2c800f1f10c61732 +size 129 diff --git a/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/gen_20260330_065910.log b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/gen_20260330_065910.log new file mode 100644 index 0000000000000000000000000000000000000000..9c180583e2f41ef733fb7e09b06d945138b76ad3 --- /dev/null +++ b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/gen_20260330_065910.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:439c7ebca3b3f6d79b51d867e4d9f5c97cdba6fe368086890e79d7ff9dba08b0 +size 133 diff --git a/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/input_snapshot.json b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..0e6d9a93a000380d89a467b817d4b3859efbcba7 --- /dev/null +++ b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m11", + "model": "tvae", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-train.csv", + "exists": true, + "size": 17450561, + "sha256": "01325179667586bada6e9b69ae7d1fe3f59df8ff4ddc3f41822af5e99f6b6e32" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-val.csv", + "exists": true, + "size": 2181590, + "sha256": "1066848f54a0895112eb7fa66933159bc43ebd8ec01b60d70aa6b1a9b87d1db1" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m11/m11-test.csv", + "exists": true, + "size": 2181600, + "sha256": "bf23f0509554f8ee3a54a40d5fb242b0191a63d0d36c2a0f4c066ee89939b168" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m11/m11-dataset_profile.json", + "exists": true, + "size": 5042, + "sha256": "16ed6d26c47d5128e66a3420dfa9ed6baf8bf95b281df3cf6ab41f304f623433" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m11/m11-dataset_contract_v1.json", + "exists": true, + "size": 5995, + "sha256": "fd026c5f7619286326c84b02dceae6378a6ef481b60818d54b9b7ac1a77c79f8" + } + } +} \ No newline at end of file diff --git a/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/models_300epochs/train_20260328_052648.log b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/models_300epochs/train_20260328_052648.log new file mode 100644 index 0000000000000000000000000000000000000000..51629f23ee7872c47d39f9a2de4047e916888342 --- /dev/null +++ b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/models_300epochs/train_20260328_052648.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7be0e3a5723832f7a1888d8e8c75660d5370ce30a4884d58befdd9a0ca249240 +size 174 diff --git a/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/models_300epochs/tvae_300epochs.pt b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/models_300epochs/tvae_300epochs.pt new file mode 100644 index 0000000000000000000000000000000000000000..3017191acac264f6e2d8aa6c4e829e16cf2b6b53 --- /dev/null +++ b/syntheticSuccess/m11/tvae/tvae-m11-20260328_052639/models_300epochs/tvae_300epochs.pt @@ -0,0 +1,3 @@ +version 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