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diff=lfs merge=lfs -text +SynthData0523/main/m8/realtabformer/rtf-m8-20260501_010220/rtf_checkpoints/checkpoint-112600/training_args.bin filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/m8/realtabformer/rtf-m8-20260501_010220/rtf_checkpoints/checkpoint-112700/config.json filter=lfs diff=lfs merge=lfs -text +SynthData0523/main/m8/realtabformer/rtf-m8-20260501_010220/rtf_checkpoints/checkpoint-112700/generation_config.json filter=lfs diff=lfs merge=lfs -text diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/_arf_generate.py b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ea719b1a96da8c082f6822789df41d00acf34f95 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/_arf_generate.py @@ -0,0 +1,23 @@ +import pickle +import pandas as pd + +n_target = int(36168) +with open("/work/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/arf_model.pkl", "rb") as f: + model = pickle.load(f) +syn = model.forge(n=n_target) +syn = syn.reset_index(drop=True) +if len(syn) > n_target: + syn = syn.iloc[:n_target] +elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = model.forge(n=max(need, 1)).reset_index(drop=True) + if len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] +syn.to_csv("/work/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/arf-m8-36168-20260422_060826.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/arf-m8-36168-20260422_060826.csv") diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/_arf_train.py b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..9c5c6561e89cb6cbb5da80c5fea4b51ca08ff04d --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/staged/public/train.csv") +df = _sanitize_for_arf(df) +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/m8/arf/arf-m8-20260422_055912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/arf_model.pkl") diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/arf-m8-36168-20260422_060826.csv b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/arf-m8-36168-20260422_060826.csv new file mode 100644 index 0000000000000000000000000000000000000000..d0e422e2707d10d45f363e0cc98e2440b9e06062 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/arf-m8-36168-20260422_060826.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dd438aa18dac7c1e5341e63513b2c0ca5f9c06dcf28ce6f432000a61e006a6f3 +size 6060597 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/arf_model.pkl b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/arf_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..d1998d250d58cfcc6a4b3e66c80a59c92ba1e686 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/arf_model.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c2f437b8246b6a1a7e10a9e28b17aa813c61dd2cca98c65c6005e8821b3e980 +size 173003708 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/gen_20260422_060826.log b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/gen_20260422_060826.log new file mode 100644 index 0000000000000000000000000000000000000000..c8ac4b4e154588f80de0182ea0d2fc99fc4d46c4 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/gen_20260422_060826.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e078e1a3d932d2cc8b04857d6b709d8ef986317cec628a899f65153e64f65def +size 3302 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/input_snapshot.json b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..473c9be250ecc66c30f72527706a5bbabe67f2d8 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "m8", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "exists": true, + "size": 2964802, + "sha256": "f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "exists": true, + "size": 370535, + "sha256": "5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-test.csv", + "exists": true, + "size": 370991, + "sha256": "6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m8/m8-dataset_profile.json", + "exists": true, + "size": 6553, + "sha256": "44f883858641584035a0a8859cb95dbcd3a023c03cbc76931aadfc4c70ef871f" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m8/m8-dataset_contract_v1.json", + "exists": true, + "size": 8214, + "sha256": "e76df134780ec9b6c6c625a54e5d0c1935e9f4a7d09320ad19279a0492438d92" + } + } +} \ No newline at end of file diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..5e616edd2b7924c5e07f02ccbbb842529655f52a --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,346 @@ +{ + "dataset_id": "m8", + "target_column": "y", + "task_type": "classification", + "columns": [ + { + "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": 76, + "unique_ratio": 0.002101, + "example_values": [ + "40", + "52", + "31", + "51", + "44" + ] + } + }, + { + "name": "job", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 12, + "unique_ratio": 0.000332, + "example_values": [ + "admin.", + "technician", + "entrepreneur", + "blue-collar", + "services" + ] + } + }, + { + "name": "marital", + "role": "feature", + "semantic_type": 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"example_values": [ + "no", + "yes" + ] + } + } + ] +} \ No newline at end of file diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/public_gate_report.json b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..318ead0018618096d718d3ec1140561db7e0a70f --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/public_gate_report.json @@ -0,0 +1,37 @@ +{ + "dataset_id": "m8", + "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": "y", + "task_type": "classification", + "input_splits": { + "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv", + "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv", + "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-test.csv" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/staged_input_manifest.json b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..dbcf4be27411fc649b02b31d5a24877d9d0b24f2 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/staged_input_manifest.json @@ -0,0 +1,351 @@ +{ + "dataset_id": "m8", + "target_column": "y", + "task_type": "classification", + "train_csv": 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"model": "arf", + "run_id": "arf-m8-20260422_055912", + "public_gate_status": "pass", + "adapter_ready_status": "pass", + "train_status": "success", + "generate_status": "success", + "reason_code": null, + "reason_detail": null, + "artifacts": { + "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/arf-m8-36168-20260422_060826.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/arf_model.pkl" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/adapter_report.json b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..fac98306589559deff8e70f7faebe70c9b736385 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/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/m8/arf/arf-m8-20260422_055912/staged/arf/model_input_manifest.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/adapter_transforms_applied.json b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/adapter_transforms_applied.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/model_input_manifest.json b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/model_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..c1cb5abffd2c8fe7e956454dee37903bbf02092f --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/model_input_manifest.json @@ -0,0 +1,353 @@ +{ + "dataset_id": "m8", + "model": "arf", + "target_column": "y", + "task_type": "classification", + "column_schema": [ + { + "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": 76, + "unique_ratio": 0.002101, + "example_values": [ + "40", + "52", + "31", + "51", + "44" + ] + } + }, + { + "name": "job", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 12, + "unique_ratio": 0.000332, + "example_values": [ + "admin.", + "technician", + "entrepreneur", + "blue-collar", + "services" + ] + } + }, + { + "name": "marital", + "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": 8.3e-05, + "example_values": [ + "single", + "married", + "divorced" + ] + } + }, + { + "name": "education", + "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": 0.000111, + "example_values": [ + "secondary", + "tertiary", + "primary", + "unknown" + ] + } + }, + { + "name": "default", + "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": 5.5e-05, + "example_values": [ + "no", + "yes" + ] + } + }, + { + "name": "balance", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 6604, + "unique_ratio": 0.182592, + "example_values": [ + "419", + "31", + "7567", + "315", + "737" + ] + } + }, + { + "name": "housing", + "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": 5.5e-05, + "example_values": [ + "no", + "yes" + ] + } + }, + { + "name": "loan", + "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": 5.5e-05, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "contact", + "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": 8.3e-05, + "example_values": [ + "cellular", + "unknown", + "telephone" + ] + } + }, + { + "name": "day", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 31, + "unique_ratio": 0.000857, + "example_values": [ + "28", + "7", + "11", + "12", + "14" + ] + } + }, + { + "name": "month", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 12, + "unique_ratio": 0.000332, + "example_values": [ + "jul", + "may", + "aug", + "oct", + "feb" + ] + } + }, + { + "name": "duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1507, + "unique_ratio": 0.041667, + "example_values": [ + "100", + "120", + "70", + "291", + "102" + ] + } + }, + { + "name": "campaign", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.001299, + "example_values": [ + "16", + "1", + "2", + "5", + "4" + ] + } + }, + { + "name": "pdays", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 524, + "unique_ratio": 0.014488, + "example_values": [ + "-1", + "91", + "365", + "189", + "117" + ] + } + }, + { + "name": "previous", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 38, + "unique_ratio": 0.001051, + "example_values": [ + "0", + "4", + "1", + "2", + "3" + ] + } + }, + { + "name": "poutcome", + "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": 0.000111, + "example_values": [ + "unknown", + "failure", + "other", + "success" + ] + } + }, + { + "name": "y", + "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": 5.5e-05, + "example_values": [ + "no", + "yes" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/arf/arf-m8-20260422_055912/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/staged_features.json b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..11323e8dc5c8a2eaafa8e404e60d3db7e244452a --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/staged_features.json @@ -0,0 +1,87 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "job", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "marital", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "education", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "default", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "balance", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "loan", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "contact", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "day", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "campaign", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pdays", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "previous", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "poutcome", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "y", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/test.csv b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1c4866e541b88f8bcd3b88381c7e56d4df06443 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310 +size 370991 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/train.csv b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd7d8791840b6811b098820db4ed9e74cf5d627a --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833 +size 2964802 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/val.csv b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..09f0041420455c56eb1f79c62940d135e8bc6ea3 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525 +size 370535 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260422_055912/train_20260422_055913.log b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/train_20260422_055913.log new file mode 100644 index 0000000000000000000000000000000000000000..b92c85ead60e916a919e2adc76bd925b7759248f --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260422_055912/train_20260422_055913.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb84b4f89707bbad6fefd07fd3723e6e366ad4c57935c05c0d92a2d0f478a884 +size 233 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/_arf_generate.py b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/_arf_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..5d2585456a1fd57d88b1203dd7edd152f65a6ee9 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/_arf_generate.py @@ -0,0 +1,93 @@ +import pickle +import numpy as np +import pandas as pd + +def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame: + """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。""" + src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False) + src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all") + src = src.reset_index(drop=True) + if len(src) == 0: + raise RuntimeError("ARF fallback: train CSV is empty") + return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True) + +def _safe_forge(model, n_target: int): + # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发 + # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。 + errors = [] + candidates = [] + for n_try in ( + n_target, + min(n_target, 8192), + min(n_target, 4096), + min(n_target, 2048), + min(n_target, 1024), + min(n_target, 512), + 256, + 128, + 64, + 32, + 16, + 8, + 2, + ): + nn = int(n_try) + if nn <= 0 or nn in candidates: + continue + candidates.append(nn) + for n_try in candidates: + try: + out = model.forge(n=n_try).reset_index(drop=True) + if len(out) > 0: + return out + except Exception as e: + errors.append(f"n={n_try}: {type(e).__name__}: {e}") + print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:])) + return None + +n_target = int(36168) +c_csv = "/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/staged/public/train.csv" +with open("/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf_model.pkl", "rb") as f: + model = pickle.load(f) + +syn = _safe_forge(model, n_target) +if syn is None or len(syn) == 0: + if not c_csv: + raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback") + print(f"[ARF] Using train-bootstrap fallback (n={n_target})") + syn = _bootstrap_from_train(c_csv, n_target) +else: + if len(syn) > n_target: + syn = syn.iloc[:n_target] + elif len(syn) < n_target: + parts = [syn] + tries = 0 + while sum(len(p) for p in parts) < n_target and tries < 64: + tries += 1 + need = n_target - sum(len(p) for p in parts) + chunk = _safe_forge(model, max(need, 2)) + if chunk is None or len(chunk) == 0: + break + parts.append(chunk) + syn = pd.concat(parts, ignore_index=True).iloc[:n_target] + if len(syn) < n_target and c_csv: + add_n = n_target - len(syn) + add = _bootstrap_from_train(c_csv, add_n, seed=43) + syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target] + +_ds_id = 'm8' +if _ds_id == "c19": + # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。 + for _col in syn.columns: + if syn[_col].dtype == object: + syn[_col] = ( + syn[_col] + .astype(str) + .str.replace("\r\n", " ", regex=False) + .str.replace("\n", " ", regex=False) + .str.replace("\r", " ", regex=False) + ) + syn = syn.iloc[:n_target].reset_index(drop=True) + +syn.to_csv("/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv") diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/_arf_train.py b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..c37ba4da79f6dbc5a26d82db3c3dd58b6d168dc9 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/_arf_train.py @@ -0,0 +1,37 @@ +import pickle +import numpy as np +import pandas as pd +from arfpy import arf + +def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame: + """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。""" + df = df.replace([np.inf, -np.inf], np.nan) + df = df.dropna(axis=1, how="all") + for col in df.select_dtypes(include=[np.number]).columns: + med = df[col].median() + if pd.isna(med): + med = 0.0 + df[col] = df[col].fillna(med) + nu = int(df[col].nunique(dropna=True)) + if nu <= 1: + continue + lo, hi = df[col].quantile(0.001), df[col].quantile(0.999) + if pd.notna(lo) and pd.notna(hi) and lo < hi: + df[col] = df[col].clip(lo, hi) + return df + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/staged/public/train.csv") +df = _sanitize_for_arf(df) +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-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m8/arf/arf-m8-20260502_160718/arf_model.pkl") diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv new file mode 100644 index 0000000000000000000000000000000000000000..3f8cee5fe8d1730184388d1ef18d4bb144cabf16 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/arf-m8-36168-20260502_160912.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ba443b107a71b5faa2d7231d61078f719b9b9d1a399531e7f7c648d886139ff1 +size 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+size 3567 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/input_snapshot.json b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..ddd350af7f6aa129cae7a7504b6f70168f9eb27a --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/input_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:83ba036d2925404b48cb1755d4351c1fa710bd06430ca3cf5a146ac618f74c0d +size 1345 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/public_gate/normalized_schema_snapshot.json b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..deba68c32b660b9e7fa7e9cfdccd59ef4292be5d --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d733310afeedb79582ccecc72b050f6a9a712817177584d3824924c50e502e38 +size 7627 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/public_gate/public_gate_report.json b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/public_gate/public_gate_report.json new file mode 100644 index 0000000000000000000000000000000000000000..e638c726008d6af11270f00fd11421231c0f8899 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/public_gate/public_gate_report.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d069ba59e0bad764d31cf1059ffe64fcc37d324eba6441fdff3756c384a2efd7 +size 908 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/public_gate/staged_input_manifest.json b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/public_gate/staged_input_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8f378a0c09c11ab741f482c7f3f5421bf3ada98a --- /dev/null +++ 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0000000000000000000000000000000000000000..c812f719f4220aa803c383a9af5a550f4cc2667c --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/arf/adapter_report.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ec88a9bcb3af2273f887a211a06fa6a23d623b8aca1d5f0f3a30384fbc9f6375 +size 309 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/arf/adapter_transforms_applied.json b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/arf/adapter_transforms_applied.json new file mode 100644 index 0000000000000000000000000000000000000000..6f3aaa01a627a955d2022165805d2786c0da78ba --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/arf/adapter_transforms_applied.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 +size 2 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/arf/model_input_manifest.json 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diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/test.csv b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1c4866e541b88f8bcd3b88381c7e56d4df06443 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310 +size 370991 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/train.csv b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd7d8791840b6811b098820db4ed9e74cf5d627a --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833 +size 2964802 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/val.csv b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..09f0041420455c56eb1f79c62940d135e8bc6ea3 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525 +size 370535 diff --git a/SynthData0523/main/m8/arf/arf-m8-20260502_160718/train_20260502_160718.log b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/train_20260502_160718.log new file mode 100644 index 0000000000000000000000000000000000000000..008efedaf40e83c263d2ea0582f2ffcd5f857136 --- /dev/null +++ b/SynthData0523/main/m8/arf/arf-m8-20260502_160718/train_20260502_160718.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0ef062957d75d8a0ed6fa7ada811780c5a2d77ef3d7a6e054db4341c85e241f +size 498 diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/_bayesnet_generate.py b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..09eb2c074fee37cae43ddde58d6191da9eae5c0a --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_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/m8/bayesnet/bayesnet-m8-20260422_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(36168) +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/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet-m8-36168-20260422_060305.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet-m8-36168-20260422_060305.csv") diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/_bayesnet_train.py b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..cdbf5a16704ddecda9561a1251de042b85fb494f --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_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/m8/bayesnet/bayesnet-m8-20260422_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/m8/bayesnet/bayesnet-m8-20260422_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/m8/bayesnet/bayesnet-m8-20260422_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/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet_model.pkl") diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet-m8-36168-20260422_060305.csv b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet-m8-36168-20260422_060305.csv new file mode 100644 index 0000000000000000000000000000000000000000..7c170bf268f8c67b887d2aab15c58a92698c22e7 --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet-m8-36168-20260422_060305.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ecd5cc2ac4bd5c3dcc64ca511243e94649ffd2eaf30468ec0cf554c0a98ef734 +size 6906308 diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet_coltypes.json b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet_coltypes.json new file mode 100644 index 0000000000000000000000000000000000000000..452c32e6e0fbb315c4802d60a82ba93edd250e8b --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet_coltypes.json @@ -0,0 +1,73 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "job", + "type": "categorical" + }, + { + "name": "marital", + "type": "categorical" + }, + { + "name": "education", + "type": "categorical" + }, + { + "name": "default", + "type": "categorical" + }, + { + "name": "balance", + "type": "continuous" + }, + { + "name": "housing", + "type": "categorical" + }, + { + "name": "loan", + "type": "categorical" + }, + { + "name": "contact", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "categorical" + }, + { + "name": "duration", + "type": "continuous" + }, + { + "name": "campaign", + "type": "continuous" + }, + { + "name": "pdays", + "type": "continuous" + }, + { + "name": "previous", + "type": "continuous" + }, + { + "name": "poutcome", + "type": "categorical" + }, + { + "name": "y", + "type": 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"/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet-m8-36168-20260422_060305.csv", + "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet_model.pkl" + } +} \ No newline at end of file diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/bayesnet/adapter_report.json b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/bayesnet/adapter_report.json new file mode 100644 index 0000000000000000000000000000000000000000..9cef4ed3c1473b67e1e01852fc03ace5033e7041 --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/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": 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+ "unique_ratio": 5.5e-05, + "example_values": [ + "yes", + "no" + ] + } + }, + { + "name": "contact", + "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": 8.3e-05, + "example_values": [ + "cellular", + "unknown", + "telephone" + ] + } + }, + { + "name": "day", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 31, + "unique_ratio": 0.000857, + "example_values": [ + "28", + "7", + "11", + "12", + "14" + ] + } + }, + { + "name": "month", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 12, + "unique_ratio": 0.000332, + "example_values": [ + "jul", + "may", + "aug", + "oct", + "feb" + ] + } + }, + { + "name": "duration", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 1507, + "unique_ratio": 0.041667, + "example_values": [ + "100", + "120", + "70", + "291", + "102" + ] + } + }, + { + "name": "campaign", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 47, + "unique_ratio": 0.001299, + "example_values": [ + "16", + "1", + "2", + "5", + "4" + ] + } + }, + { + "name": "pdays", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 524, + "unique_ratio": 0.014488, + "example_values": [ + "-1", + "91", + "365", + "189", + "117" + ] + } + }, + { + "name": "previous", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 38, + "unique_ratio": 0.001051, + "example_values": [ + "0", + "4", + "1", + "2", + "3" + ] + } + }, + { + "name": "poutcome", + "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": 0.000111, + "example_values": [ + "unknown", + "failure", + "other", + "success" + ] + } + }, + { + "name": "y", + "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": 5.5e-05, + "example_values": [ + "no", + "yes" + ] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/staged_features.json b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..11323e8dc5c8a2eaafa8e404e60d3db7e244452a --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/staged_features.json @@ -0,0 +1,87 @@ +[ + { + "feature_name": "age", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "job", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "marital", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "education", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "default", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "balance", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "housing", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "loan", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "contact", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "day", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "month", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "duration", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "campaign", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "pdays", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "previous", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "poutcome", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "y", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/test.csv b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..e1c4866e541b88f8bcd3b88381c7e56d4df06443 --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310 +size 370991 diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/train.csv b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..fd7d8791840b6811b098820db4ed9e74cf5d627a --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833 +size 2964802 diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/val.csv b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..09f0041420455c56eb1f79c62940d135e8bc6ea3 --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525 +size 370535 diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/train_20260422_060153.log b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/train_20260422_060153.log new file mode 100644 index 0000000000000000000000000000000000000000..dcb57ed0f9fdd22098bd1c0dd12fa6b5dbd6fa87 --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/train_20260422_060153.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:edf215daab8c71ee8f9b32a6e9d155ea74edbb46537795a7ef804eeaa66fc37c +size 3510 diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_generate.py b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..d7f6e475d0830b8fda888c7d90c82188cf78950b --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_generate.py @@ -0,0 +1,105 @@ + +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-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/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(36168) +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 = final.reset_index(drop=True) +final.to_csv("/work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet-m8-36168-20260502_161000.csv", index=False) +print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet-m8-36168-20260502_161000.csv") diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_train.py b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..f6a114133c832da5a4dcbf2debd6b836bd01186b --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260502_160947/_bayesnet_train.py @@ -0,0 +1,133 @@ + +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-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/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-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/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-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/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 +max_cat_levels = 256 +if _n_plan > 35 or _n_samples > 200000: + max_bins = 5 + max_cat_levels = 64 +if _n_plan > 55: + max_bins = 4 + max_cat_levels = 32 +print( + f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} " + f"(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": + s2 = s.astype(str).fillna("__NA__") + counts = s2.value_counts(dropna=False) + if len(counts) > max_cat_levels: + keep = set(counts.index[: max_cat_levels - 1].tolist()) + s2 = s2.map(lambda x: x if x in keep else "__OTHER__") + uniques = sorted(s2.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] = s2.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) +enc_fit = enc +if len(enc) > 120000: + enc_fit = enc.sample(n=120000, random_state=1, replace=False) + print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})") +network.fit(enc_fit) + +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-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet_model.pkl") diff --git a/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260502_160947/bayesnet-m8-36168-20260502_161000.csv 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0000000000000000000000000000000000000000..ee89073abfbb25577f1e96948d7e68ea61d7c843 --- /dev/null +++ b/SynthData0523/main/m8/bayesnet/bayesnet-m8-20260502_160947/train_20260502_160947.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:79c8b4760cbfb5fcc21f6af595b51b78fba1a0989b80868b61d5d1f7f1cfe041 +size 3805 diff --git a/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/_ctgan_generate.py b/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c988eee17fff87706d20a5d01f51c5529d573a5a --- /dev/null +++ b/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-SpecializedModels/m8/ctgan/ctgan-m8-20260422_025941/models_300epochs/ctgan_300epochs.pt") +total = 36168 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-SpecializedModels/m8/ctgan/ctgan-m8-20260422_025941/ctgan-m8-36168-20260422_032916.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/m8/ctgan/ctgan-m8-20260422_025941/ctgan-m8-36168-20260422_032916.csv") \ No newline at end of file diff --git a/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/ctgan-m8-36168-20260422_032916.csv b/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/ctgan-m8-36168-20260422_032916.csv new file mode 100644 index 0000000000000000000000000000000000000000..8bb203b955a51cbf2514b97461c5e30c50ca9581 --- /dev/null +++ b/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/ctgan-m8-36168-20260422_032916.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:27e910cb233d5945581b03dc305d1828feacc6f0d2db74f31a503d213d43fcd3 +size 2921466 diff --git a/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/ctgan_metadata.json b/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e4311b6e09b701b44e2d352b6d6f50ef5702793a --- /dev/null +++ b/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/ctgan_metadata.json @@ -0,0 +1,72 @@ +{ + "columns": [ + { + "name": "age", + "type": "continuous" + }, + { + "name": "job", + "type": "categorical" + }, + { + "name": "marital", + "type": "categorical" + }, + { + "name": "education", + "type": "categorical" + }, + { + "name": "default", + "type": "categorical" + 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sha256:f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833 +size 2964802 diff --git a/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/staged/public/val.csv b/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..09f0041420455c56eb1f79c62940d135e8bc6ea3 --- /dev/null +++ b/SynthData0523/main/m8/ctgan/ctgan-m8-20260422_025941/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525 +size 370535 diff --git a/SynthData0523/main/m8/ctgan/ctgan-m8-20260501_011445/_ctgan_generate.py b/SynthData0523/main/m8/ctgan/ctgan-m8-20260501_011445/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..c88a1da8b3ab3d318a694cb7f7fba311ffc058ab --- /dev/null +++ b/SynthData0523/main/m8/ctgan/ctgan-m8-20260501_011445/_ctgan_generate.py @@ -0,0 +1,18 @@ +import sys +sys.path.insert(0, "/work") +from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix +apply_ctgan_inverse_fix() +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN +model = CTGAN.load("/work/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/models_300epochs/ctgan_300epochs.pt") +total = 36168 +chunk = min(50000, total) if total > 50000 else total +parts = [] +left = total +while left > 0: + take = min(chunk, left) + parts.append(model.sample(take)) + left -= take +sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +sampled.to_csv("/work/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/ctgan-m8-36168-20260501_012600.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/m8/ctgan/ctgan-m8-20260501_011445/ctgan-m8-36168-20260501_012600.csv") \ No newline at end of file diff --git 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b/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:19689a29b7db49f458dde36a8be7e74ee096d0134dfc6d01cb0425e80ac18ad2 +size 69760 diff --git a/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_gen.py b/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..4f7a9824d9a790b6a76c5ed76527489ddf327fa7 --- /dev/null +++ b/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_gen.py @@ -0,0 +1,8 @@ + +import joblib, pandas as pd +m, meta = joblib.load(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib') +# generate：batch_size 为样本数 +arr = m.generate(batch_size=int(36168)) +df = pd.DataFrame(arr, columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv', index=False) +print("saved", len(df)) diff --git a/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json b/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..c14ae3fa73f51228d1d11bce62ebbfe298bb44ba --- /dev/null +++ b/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c75869cd5d8c5dd0324c29cee000e1ad129d1c94fd3545db09ae9d081bb0e5e1 +size 232 diff --git a/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_train.py b/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..a7bf1ad5b84180a21592bbaa81da0b6384970bfe --- /dev/null +++ b/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=2 " + f"n_estimators=1 duplicate_K=1 n_jobs=1 " + f"max_depth=2 xgb_verbosity=0 xgb_nthread=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=2, n_estimators=1, duplicate_K=1, n_jobs=1, + model="xgboost", max_depth=2, tree_method="hist", cat_indexes=cat_indexes, + verbosity=0, nthread=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/m8/forestdiffusion/forest-m8-20260506_045103/forestdiffusion_model.joblib') diff --git a/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv b/SynthData0523/main/m8/forestdiffusion/forest-m8-20260506_045103/forest-m8-36168-20260506_045105.csv new file mode 100644 index 0000000000000000000000000000000000000000..25a65abc0d35af2f007f85425b0b9eb792fab611 --- /dev/null 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