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SynthData0523/main/m6/arf/arf-m6-20260423_090001/_arf_generate.py

@@ -0,0 +1,79 @@
+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(9864)
+c_csv = "/work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/staged/public/train.csv"
+with open("/work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/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]
+
+syn.to_csv("/work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv")

SynthData0523/main/m6/arf/arf-m6-20260423_090001/_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/m6/arf/arf-m6-20260423_090001/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/m6/arf/arf-m6-20260423_090001/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/arf_model.pkl")

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+{
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+  "model": "arf",
+  "inputs": {
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+      "size": 107758,
+      "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f"
+    },
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+      "exists": true,
+      "size": 107996,
+      "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de"
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+      "exists": true,
+      "size": 7622,
+      "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98"
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+      "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90"
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+}

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+        ]
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SynthData0523/main/m6/arf/arf-m6-20260423_090001/public_gate/public_gate_report.json

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+  "status": "pass",
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+      "status": "pass"
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+      "status": "pass"
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+      "status": "pass"
+    },
+    {
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+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
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+  "task_type": "classification",
+  "input_splits": {
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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv"
+  }
+}

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+          "Mar"
+        ]
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+      "role": "feature",
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+          "3",
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+          "1"
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+      "role": "target",
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+        "unique_ratio": 0.000304,
+        "example_values": [
+          "Returning_Visitor",
+          "New_Visitor",
+          "Other"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+        "example_values": [
+          "FALSE",
+          "TRUE"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
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+          "TRUE"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/m6/arf/arf-m6-20260423_090001/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "m6",
+  "model": "arf",
+  "run_id": "arf-m6-20260423_090001",
+  "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/m6/arf/arf-m6-20260423_090001/arf-m6-9864-20260423_090902.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/arf_model.pkl"
+  }
+}

SynthData0523/main/m6/arf/arf-m6-20260423_090001/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/m6/arf/arf-m6-20260423_090001/staged/arf/model_input_manifest.json"
+}

SynthData0523/main/m6/arf/arf-m6-20260423_090001/staged/arf/adapter_transforms_applied.json

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

SynthData0523/main/m6/arf/arf-m6-20260423_090001/staged/arf/model_input_manifest.json

@@ -0,0 +1,384 @@
+{
+  "dataset_id": "m6",
+  "model": "arf",
+  "target_column": "VisitorType",
+  "task_type": "classification",
+  "column_schema": [
+    {
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+        "unique_ratio": 0.282745,
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+          "77.7",
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+      "impute_strategy": "median",
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+        "unique_ratio": 0.15957,
+        "example_values": [
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+          "0",
+          "0.00952381",
+          "0.022857143",
+          "0.004166667"
+        ]
+      }
+    },
+    {
+      "name": "ExitRates",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 3953,
+        "unique_ratio": 0.40075,
+        "example_values": [
+          "0.045421245",
+          "0.05",
+          "0.017460317",
+          "0.040606061",
+          "0.133333333"
+        ]
+      }
+    },
+    {
+      "name": "PageValues",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.215835,
+        "example_values": [
+          "0",
+          "7.945368291",
+          "10.37042373",
+          "18.99269231",
+          "44.33548922"
+        ]
+      }
+    },
+    {
+      "name": "SpecialDay",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.000608,
+        "example_values": [
+          "0",
+          "0.4",
+          "0.2",
+          "0.8",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "Month",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.001014,
+        "example_values": [
+          "Nov",
+          "Dec",
+          "Jul",
+          "Aug",
+          "Mar"
+        ]
+      }
+    },
+    {
+      "name": "OperatingSystems",
+      "role": "feature",
+      "semantic_type": "numeric",
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+          "2",
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+          "4",
+          "1"
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+      }
+    },
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+      "name": "Browser",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "median",
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+        "unique_ratio": 0.001318,
+        "example_values": [
+          "2",
+          "1",
+          "5",
+          "4",
+          "3"
+        ]
+      }
+    },
+    {
+      "name": "Region",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.000912,
+        "example_values": [
+          "6",
+          "3",
+          "4",
+          "1",
+          "9"
+        ]
+      }
+    },
+    {
+      "name": "TrafficType",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
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+        "unique_ratio": 0.002028,
+        "example_values": [
+          "2",
+          "8",
+          "3",
+          "11",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "VisitorType",
+      "role": "target",
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+      "nullable": false,
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+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000304,
+        "example_values": [
+          "Returning_Visitor",
+          "New_Visitor",
+          "Other"
+        ]
+      }
+    },
+    {
+      "name": "Weekend",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
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+        "unique_ratio": 0.000203,
+        "example_values": [
+          "FALSE",
+          "TRUE"
+        ]
+      }
+    },
+    {
+      "name": "Revenue",
+      "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": 0.000203,
+        "example_values": [
+          "FALSE",
+          "TRUE"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m6/arf/arf-m6-20260423_090001/public_gate/public_gate_report.json"
+}

SynthData0523/main/m6/arf/arf-m6-20260423_090001/staged/public/staged_features.json

@@ -0,0 +1,92 @@
+[
+  {
+    "feature_name": "Administrative",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Administrative_Duration",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Informational",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Informational_Duration",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ProductRelated",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ProductRelated_Duration",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "BounceRates",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ExitRates",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "PageValues",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "SpecialDay",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Month",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "OperatingSystems",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Browser",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "Region",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "TrafficType",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "VisitorType",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "Weekend",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "Revenue",
+    "data_type": "binary",
+    "is_target": false
+  }
+]

SynthData0523/main/m6/arf/arf-m6-20260423_090001/staged/public/test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d16d0389cb0fe4b23bab344dc10070de6678357a9452f9f620d0eeba66a6b12d
+size 116376

SynthData0523/main/m6/arf/arf-m6-20260423_090001/staged/public/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e1f0338c367408dc21d5a4ec9cdc5d3fe8188916db6085f3fd326304a55551e1
+size 924849

SynthData0523/main/m6/arf/arf-m6-20260423_090001/staged/public/val.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cb9169f3c7213420491d9b709ee1cc650aeaed732fa77dcbd6ffe3583366b2d4
+size 116198

SynthData0523/main/m6/arf/arf-m6-20260423_090001/train_20260423_090001.log

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+size 348

SynthData0523/main/m6/arf/arf-m6-20260429_032047/_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(9864)
+c_csv = "/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/staged/public/train.csv"
+with open("/work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/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 = 'm6'
+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/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf-m6-9864-20260429_032614.csv")

SynthData0523/main/m6/arf/arf-m6-20260429_032047/_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/m6/arf/arf-m6-20260429_032047/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/m6/arf/arf-m6-20260429_032047/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/m6/arf/arf-m6-20260429_032047/arf_model.pkl")

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SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_generate.py

@@ -0,0 +1,43 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=9864).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+if os.path.exists(const_path):
+    with open(const_path) as _f:
+        const_cols = _json.load(_f)
+    for col, val in const_cols.items():
+        syn[col] = val
+        print(f"[BayesNet] Restored constant column '{col}' = {val}")
+
+syn.to_csv("/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-9864-20260330_065702.csv", index=False)
+print(f"[BayesNet] Generated 9864 rows -> /work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet-m6-9864-20260330_065702.csv")

SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/_bayesnet_train.py

@@ -0,0 +1,62 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...")
+        # Install synthcity with numpy<2 to avoid conflicts
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        # Remove torch/torchvision from pip_libs to avoid shadowing system versions
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+from synthcity.plugins import Plugins
+import pickle
+import pandas as pd
+from synthcity.plugins.core.dataloader import GenericDataLoader
+
+df = pd.read_csv("/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+
+# Drop zero-variance columns (only 1 unique value) to avoid
+# synthcity encoder KeyError during generation
+import json as _json
+const_cols = {}
+for col in list(df.columns):
+    nuniq = df[col].nunique()
+    if nuniq <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}' (value={const_cols[col]})")
+
+# Save constant columns info so generate can restore them
+const_path = "/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w") as _f:
+    _json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+print(f"[BayesNet] Training on {len(df)} rows, {len(df.columns)} cols")
+
+loader = GenericDataLoader(df)
+plugin = Plugins().get("bayesian_network")
+plugin.fit(loader)
+
+with open("/work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m6/bayesnet/bayesnet-m6-20260321_075851/bayesnet_model.pkl")

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+{
+  "dataset_id": "m6",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-train.csv",
+      "exists": true,
+      "size": 856785,
+      "sha256": "a5d1c487a8f2611385915fcc5a52bad546680ddbc8d23fc695f442cdd6dafa0c"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv",
+      "exists": true,
+      "size": 107758,
+      "sha256": "598196cecc227cfba95c9796b80bc1baf684a0117e6673b8662b89482cdcb78f"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv",
+      "exists": true,
+      "size": 107996,
+      "sha256": "ec939ad96a3b14dd960886359fb6c5d45591adc8a734661ade3dee1417a015de"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_profile.json",
+      "exists": true,
+      "size": 7622,
+      "sha256": "859f1fe93806c8ecdea9c9db9db34fb6cf94bc112b5c0a66b2436e8ef71c2e98"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m6/m6-dataset_contract_v1.json",
+      "exists": true,
+      "size": 8990,
+      "sha256": "01142eeb121af615a644c3e312f5f3e79d805396339f40d5a300ba3560cf8e90"
+    }
+  }
+}

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+  "target_column": "VisitorType",
+  "task_type": "classification",
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+      "missing_tokens": [],
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SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
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+      "check_id": "PG001_csv_parse_ok",
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+      "status": "pass"
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+      "status": "pass"
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+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
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+      "status": "pass"
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+  ],
+  "target_column": "VisitorType",
+  "task_type": "classification",
+  "input_splits": {
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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m6/m6-test.csv"
+  }
+}

SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/public_gate/staged_input_manifest.json

@@ -0,0 +1,382 @@
+{
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+}

SynthData0523/main/m6/bayesnet/bayesnet-m6-20260321_075851/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "m6",
+  "model": "bayesnet",
+  "run_id": "bayesnet-m6-20260321_075851",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
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+  "generate_status": "success",
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+  }
+}