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SynthData0523/main/m8/arf/arf-m8-20260422_055912/_arf_generate.py

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+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")

SynthData0523/main/m8/arf/arf-m8-20260422_055912/_arf_train.py

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+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")

SynthData0523/main/m8/arf/arf-m8-20260422_055912/arf-m8-36168-20260422_060826.csv

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+size 6060597

SynthData0523/main/m8/arf/arf-m8-20260422_055912/arf_model.pkl

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+size 173003708

SynthData0523/main/m8/arf/arf-m8-20260422_055912/gen_20260422_060826.log

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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"
+    }
+  }
+}

SynthData0523/main/m8/arf/arf-m8-20260422_055912/public_gate/normalized_schema_snapshot.json

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+  "dataset_id": "m8",
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+}

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"
+    },
+    {
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+      "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"
+  }
+}

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": "/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",
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+}

SynthData0523/main/m8/arf/arf-m8-20260422_055912/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "m8",
+  "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"
+  }
+}

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"
+}

SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/adapter_transforms_applied.json

@@ -0,0 +1 @@
+[]

SynthData0523/main/m8/arf/arf-m8-20260422_055912/staged/arf/model_input_manifest.json

@@ -0,0 +1,353 @@
+{
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+      "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": [],
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+      "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,
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+      "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"
+}

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
+  }
+]

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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")

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")

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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")

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")

SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet-m8-36168-20260422_060305.csv

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SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/bayesnet_coltypes.json

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+{
+  "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": "categorical"
+    }
+  ],
+  "integer_columns": []
+}

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SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/gen_20260422_060305.log

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SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/input_snapshot.json

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+{
+  "dataset_id": "m8",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-train.csv",
+      "exists": true,
+      "size": 2964802,
+      "sha256": "f9cbb71aa793de19869a138d41aea5808f772b31082741b185ffb8ca7b821833"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-val.csv",
+      "exists": true,
+      "size": 370535,
+      "sha256": "5ee8612128aae92155906abc0fdc752ac24fd04d63c78c080c89e3900efe6525"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m8/m8-test.csv",
+      "exists": true,
+      "size": 370991,
+      "sha256": "6221943e422e75c8317b79b7ef93e9cd01f61fdd8de6ce42909a8e4610966310"
+    },
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+      "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"
+    }
+  }
+}

SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/public_gate/normalized_schema_snapshot.json

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+  "dataset_id": "m8",
+  "target_column": "y",
+  "task_type": "classification",
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+    {
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+      "role": "feature",
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+        "unique_ratio": 0.002101,
+        "example_values": [
+          "40",
+          "52",
+          "31",
+          "51",
+          "44"
+        ]
+      }
+    },
+    {
+      "name": "job",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
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+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.000332,
+        "example_values": [
+          "admin.",
+          "technician",
+          "entrepreneur",
+          "blue-collar",
+          "services"
+        ]
+      }
+    },
+    {
+      "name": "marital",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+        "example_values": [
+          "single",
+          "married",
+          "divorced"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
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+          "telephone"
+        ]
+      }
+    },
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+      "role": "feature",
+      "semantic_type": "numeric",
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+        ]
+      }
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+        "unique_ratio": 0.000332,
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+          "may",
+          "aug",
+          "oct",
+          "feb"
+        ]
+      }
+    },
+    {
+      "name": "duration",
+      "role": "feature",
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+          "102"
+        ]
+      }
+    },
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+      "role": "feature",
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+      "role": "feature",
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+        "example_values": [
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+}

SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/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"
+  }
+}

SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/public_gate/staged_input_manifest.json

@@ -0,0 +1,351 @@
+{
+  "dataset_id": "m8",
+  "target_column": "y",
+  "task_type": "classification",
+  "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",
+  "column_schema": [
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+      "impute_strategy": "mode",
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+          "technician",
+          "entrepreneur",
+          "blue-collar",
+          "services"
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+    {
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+        "unique_ratio": 8.3e-05,
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+    {
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+          "telephone"
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+    {
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+      "profile_stats": {
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+        "unique_ratio": 0.041667,
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+    {
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+        "example_values": [
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+        ]
+      }
+    },
+    {
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+      "missing_tokens": [],
+      "parse_format": null,
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+    }
+  ]
+}

SynthData0523/main/m8/bayesnet/bayesnet-m8-20260422_060152/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "m8",
+  "model": "bayesnet",
+  "run_id": "bayesnet-m8-20260422_060152",
+  "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/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"
+  }
+}

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": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m8/bayesnet/bayesnet-m8-20260422_060152/staged/bayesnet/model_input_manifest.json"
+}