Add files using upload-large-folder tool

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/_arf_generate.py

@@ -0,0 +1,23 @@
+import pickle
+import pandas as pd
+
+n_target = int(480000)
+with open("/work/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/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/c15/arf/arf-c15-20260422_192349/arf-c15-480000-20260423_003225.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/arf-c15-480000-20260423_003225.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/_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/c15/arf/arf-c15-20260422_192349/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/c15/arf/arf-c15-20260422_192349/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/gen_20260423_003225.log

@@ -0,0 +1,37 @@
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+[ARF] Generated 480000 rows (requested 480000) -> /work/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/arf-c15-480000-20260423_003225.csv

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c15",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-train.csv",
+      "exists": true,
+      "size": 68720503,
+      "sha256": "b2c9c7218c38c30f955677ff4f014696d393c4a1989b132f3ec5105ce729f1a8"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-val.csv",
+      "exists": true,
+      "size": 8588883,
+      "sha256": "797cea41b1f718b431a93dfdba88dce89147b5cac8671490bde57f695da6e464"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-test.csv",
+      "exists": true,
+      "size": 8590453,
+      "sha256": "1d6193a8b1dadfbb6a33ae7e63ea7bd22bbbbbc60ddfd06ada3e34fbf207ac24"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c15/c15-dataset_profile.json",
+      "exists": true,
+      "size": 9902,
+      "sha256": "2af6a0c86c2286da0abe6bf4b86a42f17623204744ecc15990aa228600980db6"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c15/c15-dataset_contract_v1.json",
+      "exists": true,
+      "size": 12033,
+      "sha256": "572403793859e059ebed0d0d5e2a9b306bf402681341439c0b78710a29aff262"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,509 @@
+{
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SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
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+  "input_splits": {
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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/public_gate/staged_input_manifest.json

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SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/runtime_result.json

@@ -0,0 +1,15 @@
+{
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+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/arf_model.pkl"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/staged/arf/adapter_report.json

@@ -0,0 +1,7 @@
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+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/staged/arf/adapter_transforms_applied.json

@@ -0,0 +1 @@
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SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/staged/arf/model_input_manifest.json

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+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030421,
+        "unique_count": 3,
+        "unique_ratio": 6e-06,
+        "example_values": [
+          "Red",
+          "Blue",
+          "Green"
+        ]
+      }
+    },
+    {
+      "name": "nom_1",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030152,
+        "unique_count": 6,
+        "unique_ratio": 1.3e-05,
+        "example_values": [
+          "Star",
+          "Triangle",
+          "Trapezoid",
+          "Polygon",
+          "Square"
+        ]
+      }
+    },
+    {
+      "name": "nom_2",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.03011,
+        "unique_count": 6,
+        "unique_ratio": 1.3e-05,
+        "example_values": [
+          "Dog",
+          "Lion",
+          "Hamster",
+          "Cat",
+          "Axolotl"
+        ]
+      }
+    },
+    {
+      "name": "nom_3",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030267,
+        "unique_count": 6,
+        "unique_ratio": 1.3e-05,
+        "example_values": [
+          "Costa Rica",
+          "India",
+          "Russia",
+          "Canada",
+          "Finland"
+        ]
+      }
+    },
+    {
+      "name": "nom_4",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030035,
+        "unique_count": 4,
+        "unique_ratio": 9e-06,
+        "example_values": [
+          "Piano",
+          "Theremin",
+          "Bassoon",
+          "Oboe"
+        ]
+      }
+    },
+    {
+      "name": "nom_5",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.02974,
+        "unique_count": 1220,
+        "unique_ratio": 0.00262,
+        "example_values": [
+          "90217d5cf",
+          "e00572717",
+          "7997c8f99",
+          "febb8819e",
+          "9e7d06868"
+        ]
+      }
+    },
+    {
+      "name": "nom_6",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030229,
+        "unique_count": 1518,
+        "unique_ratio": 0.003261,
+        "example_values": [
+          "76d705530",
+          "c7690caae",
+          "316fb49a2",
+          "93ecdf869",
+          "4f027736c"
+        ]
+      }
+    },
+    {
+      "name": "nom_7",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030133,
+        "unique_count": 222,
+        "unique_ratio": 0.000477,
+        "example_values": [
+          "21955ee26",
+          "0764632bc",
+          "2c4f9614e",
+          "c3ad85934",
+          "5627228e9"
+        ]
+      }
+    },
+    {
+      "name": "nom_8",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.029587,
+        "unique_count": 222,
+        "unique_ratio": 0.000477,
+        "example_values": [
+          "77d41330d",
+          "fbea9bb4a",
+          "8dc88fa15",
+          "46a005ddf",
+          "a89e1c1dc"
+        ]
+      }
+    },
+    {
+      "name": "nom_9",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030183,
+        "unique_count": 2218,
+        "unique_ratio": 0.004765,
+        "example_values": [
+          "5a93db805",
+          "47e50cd99",
+          "517281aaf",
+          "b9b1cd7f3",
+          "c2b0e1171"
+        ]
+      }
+    },
+    {
+      "name": "ord_0",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.030606,
+        "unique_count": 3,
+        "unique_ratio": 6e-06,
+        "example_values": [
+          "3.0",
+          "2.0",
+          "1.0"
+        ]
+      }
+    },
+    {
+      "name": "ord_1",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030181,
+        "unique_count": 5,
+        "unique_ratio": 1.1e-05,
+        "example_values": [
+          "Contributor",
+          "Grandmaster",
+          "Novice",
+          "Master",
+          "Expert"
+        ]
+      }
+    },
+    {
+      "name": "ord_2",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030212,
+        "unique_count": 6,
+        "unique_ratio": 1.3e-05,
+        "example_values": [
+          "Cold",
+          "Boiling Hot",
+          "Freezing",
+          "Hot",
+          "Warm"
+        ]
+      }
+    },
+    {
+      "name": "ord_3",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.029827,
+        "unique_count": 15,
+        "unique_ratio": 3.2e-05,
+        "example_values": [
+          "h",
+          "a",
+          "f",
+          "m",
+          "i"
+        ]
+      }
+    },
+    {
+      "name": "ord_4",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030029,
+        "unique_count": 26,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "E",
+          "M",
+          "N",
+          "X",
+          "Y"
+        ]
+      }
+    },
+    {
+      "name": "ord_5",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.029473,
+        "unique_count": 190,
+        "unique_ratio": 0.000408,
+        "example_values": [
+          "lA",
+          "Rv",
+          "fV",
+          "xG",
+          "QV"
+        ]
+      }
+    },
+    {
+      "name": "day",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.029971,
+        "unique_count": 7,
+        "unique_ratio": 1.5e-05,
+        "example_values": [
+          "5.0",
+          "7.0",
+          "1.0",
+          "3.0",
+          "6.0"
+        ]
+      }
+    },
+    {
+      "name": "month",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0299,
+        "unique_count": 12,
+        "unique_ratio": 2.6e-05,
+        "example_values": [
+          "9.0",
+          "1.0",
+          "12.0",
+          "6.0",
+          "5.0"
+        ]
+      }
+    },
+    {
+      "name": "target",
+      "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": 4e-06,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/staged/public/staged_features.json

@@ -0,0 +1,127 @@
+[
+  {
+    "feature_name": "id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_0",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_1",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_2",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_3",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_4",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_0",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_6",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_7",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_8",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_9",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_0",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "day",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "month",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "target",
+    "data_type": "binary",
+    "is_target": true
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/train_20260422_192418.log

@@ -0,0 +1,4 @@
+[ARF] Training on 480000 rows, 25 cols
+Initial accuracy is 0.52530625
+Iteration number 1 reached accuracy of 0.4767927083333333.
+[ARF] Model saved -> /work/output-SpecializedModels/c15/arf/arf-c15-20260422_192349/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_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(480000)
+c_csv = "/work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/train.csv"
+with open("/work/output-SpecializedModels/c15/arf/arf-c15-20260423_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/c15/arf/arf-c15-20260423_090001/arf-c15-480000-20260423_133619.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/arf-c15-480000-20260423_133619.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_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/c15/arf/arf-c15-20260423_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/c15/arf/arf-c15-20260423_090001/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/gen_20260423_133619.log

@@ -0,0 +1,37 @@
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+/usr/local/lib/python3.10/site-packages/arfpy/arf.py:329: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
+  if self.factor_cols[j]:
+[ARF] Generated 480000 rows (requested 480000) -> /work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/arf-c15-480000-20260423_133619.csv

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c15",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-train.csv",
+      "exists": true,
+      "size": 68720503,
+      "sha256": "b2c9c7218c38c30f955677ff4f014696d393c4a1989b132f3ec5105ce729f1a8"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-val.csv",
+      "exists": true,
+      "size": 8588883,
+      "sha256": "797cea41b1f718b431a93dfdba88dce89147b5cac8671490bde57f695da6e464"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-test.csv",
+      "exists": true,
+      "size": 8590453,
+      "sha256": "1d6193a8b1dadfbb6a33ae7e63ea7bd22bbbbbc60ddfd06ada3e34fbf207ac24"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c15/c15-dataset_profile.json",
+      "exists": true,
+      "size": 9902,
+      "sha256": "2af6a0c86c2286da0abe6bf4b86a42f17623204744ecc15990aa228600980db6"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c15/c15-dataset_contract_v1.json",
+      "exists": true,
+      "size": 12033,
+      "sha256": "572403793859e059ebed0d0d5e2a9b306bf402681341439c0b78710a29aff262"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,509 @@
+{
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+      "impute_strategy": "median",
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+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.029965,
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+        "example_values": [
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+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 2,
+        "unique_ratio": 4e-06,
+        "example_values": [
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+      "impute_strategy": "median",
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+          "Polygon",
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+          "Y"
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+      }
+    },
+    {
+      "name": "ord_5",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
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+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 190,
+        "unique_ratio": 0.000408,
+        "example_values": [
+          "lA",
+          "Rv",
+          "fV",
+          "xG",
+          "QV"
+        ]
+      }
+    },
+    {
+      "name": "day",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 7,
+        "unique_ratio": 1.5e-05,
+        "example_values": [
+          "5.0",
+          "7.0",
+          "1.0",
+          "3.0",
+          "6.0"
+        ]
+      }
+    },
+    {
+      "name": "month",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 12,
+        "unique_ratio": 2.6e-05,
+        "example_values": [
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+          "1.0",
+          "12.0",
+          "6.0",
+          "5.0"
+        ]
+      }
+    },
+    {
+      "name": "target",
+      "role": "target",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 2,
+        "unique_ratio": 4e-06,
+        "example_values": [
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+          "1"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c15",
+  "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": "target",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/public_gate/staged_input_manifest.json

@@ -0,0 +1,514 @@
+{
+  "dataset_id": "c15",
+  "target_column": "target",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 20000,
+        "unique_ratio": 0.041667,
+        "example_values": [
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+          "509036",
+          "432991",
+          "567594",
+          "490005"
+        ]
+      }
+    },
+    {
+      "name": "bin_0",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
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SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/runtime_result.json

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SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/arf/adapter_report.json

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SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/arf/adapter_transforms_applied.json

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SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/arf/model_input_manifest.json

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+        "example_values": [
+          "77d41330d",
+          "fbea9bb4a",
+          "8dc88fa15",
+          "46a005ddf",
+          "a89e1c1dc"
+        ]
+      }
+    },
+    {
+      "name": "nom_9",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030183,
+        "unique_count": 2218,
+        "unique_ratio": 0.004765,
+        "example_values": [
+          "5a93db805",
+          "47e50cd99",
+          "517281aaf",
+          "b9b1cd7f3",
+          "c2b0e1171"
+        ]
+      }
+    },
+    {
+      "name": "ord_0",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.030606,
+        "unique_count": 3,
+        "unique_ratio": 6e-06,
+        "example_values": [
+          "3.0",
+          "2.0",
+          "1.0"
+        ]
+      }
+    },
+    {
+      "name": "ord_1",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030181,
+        "unique_count": 5,
+        "unique_ratio": 1.1e-05,
+        "example_values": [
+          "Contributor",
+          "Grandmaster",
+          "Novice",
+          "Master",
+          "Expert"
+        ]
+      }
+    },
+    {
+      "name": "ord_2",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030212,
+        "unique_count": 6,
+        "unique_ratio": 1.3e-05,
+        "example_values": [
+          "Cold",
+          "Boiling Hot",
+          "Freezing",
+          "Hot",
+          "Warm"
+        ]
+      }
+    },
+    {
+      "name": "ord_3",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.029827,
+        "unique_count": 15,
+        "unique_ratio": 3.2e-05,
+        "example_values": [
+          "h",
+          "a",
+          "f",
+          "m",
+          "i"
+        ]
+      }
+    },
+    {
+      "name": "ord_4",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030029,
+        "unique_count": 26,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "E",
+          "M",
+          "N",
+          "X",
+          "Y"
+        ]
+      }
+    },
+    {
+      "name": "ord_5",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.029473,
+        "unique_count": 190,
+        "unique_ratio": 0.000408,
+        "example_values": [
+          "lA",
+          "Rv",
+          "fV",
+          "xG",
+          "QV"
+        ]
+      }
+    },
+    {
+      "name": "day",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.029971,
+        "unique_count": 7,
+        "unique_ratio": 1.5e-05,
+        "example_values": [
+          "5.0",
+          "7.0",
+          "1.0",
+          "3.0",
+          "6.0"
+        ]
+      }
+    },
+    {
+      "name": "month",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0299,
+        "unique_count": 12,
+        "unique_ratio": 2.6e-05,
+        "example_values": [
+          "9.0",
+          "1.0",
+          "12.0",
+          "6.0",
+          "5.0"
+        ]
+      }
+    },
+    {
+      "name": "target",
+      "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": 4e-06,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/staged_features.json

@@ -0,0 +1,127 @@
+[
+  {
+    "feature_name": "id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_0",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_1",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_2",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_3",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_4",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_0",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_6",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_7",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_8",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_9",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_0",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "day",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "month",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "target",
+    "data_type": "binary",
+    "is_target": true
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/train_20260423_090029.log

@@ -0,0 +1,4 @@
+[ARF] Training on 480000 rows, 25 cols
+Initial accuracy is 0.5257479166666666
+Iteration number 1 reached accuracy of 0.4757958333333333.
+[ARF] Model saved -> /work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260318_043827/_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/DatasetNew/c15/c15-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/c15/bayesnet/bayesnet-c15-20260318_043827/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/c15/bayesnet/bayesnet-c15-20260318_043827/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260318_043827/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260318_043827/train_20260318_043827.log

@@ -0,0 +1,104 @@
+[2026-03-17T20:38:58.224166+0000][1][CRITICAL] Error importing TabularGoggle: No module named 'dgl'
+[2026-03-17T20:38:58.234833+0000][1][CRITICAL] module disabled: /pip_libs/synthcity/plugins/generic/plugin_goggle.py
+OpenBLAS warning: precompiled NUM_THREADS exceeded, adding auxiliary array for thread metadata.
+To avoid this warning, please rebuild your copy of OpenBLAS with a larger NUM_THREADS setting
+or set the environment variable OPENBLAS_NUM_THREADS to 64 or lower
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or set the environment variable OPENBLAS_NUM_THREADS to
+a sufficiently small number. This error typically occurs when the software that relies on
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS calls BLAS functions from many threads in parallel, or when your computer has more
+cpu cores than what OpenBLAS was configured to handle.
+OpenBLAS : Program is Terminated. Because you tried to allocate too many memory regions.
+This library was built to support a maximum of 128 threads - either rebuild OpenBLAS
+with a larger NUM_THREADS value or

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_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(480000)
+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/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet-c15-480000-20260422_060347.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet-c15-480000-20260422_060347.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet_coltypes.json

@@ -0,0 +1,105 @@
+{
+  "columns": [
+    {
+      "name": "id",
+      "type": "continuous"
+    },
+    {
+      "name": "bin_0",
+      "type": "continuous"
+    },
+    {
+      "name": "bin_1",
+      "type": "continuous"
+    },
+    {
+      "name": "bin_2",
+      "type": "continuous"
+    },
+    {
+      "name": "bin_3",
+      "type": "categorical"
+    },
+    {
+      "name": "bin_4",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_0",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_1",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_2",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_3",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_4",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_5",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_6",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_7",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_8",
+      "type": "categorical"
+    },
+    {
+      "name": "nom_9",
+      "type": "categorical"
+    },
+    {
+      "name": "ord_0",
+      "type": "continuous"
+    },
+    {
+      "name": "ord_1",
+      "type": "categorical"
+    },
+    {
+      "name": "ord_2",
+      "type": "categorical"
+    },
+    {
+      "name": "ord_3",
+      "type": "categorical"
+    },
+    {
+      "name": "ord_4",
+      "type": "categorical"
+    },
+    {
+      "name": "ord_5",
+      "type": "categorical"
+    },
+    {
+      "name": "day",
+      "type": "continuous"
+    },
+    {
+      "name": "month",
+      "type": "continuous"
+    },
+    {
+      "name": "target",
+      "type": "categorical"
+    }
+  ],
+  "integer_columns": []
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/gen_20260422_060347.log

@@ -0,0 +1,48 @@
+
+==========
+== CUDA ==
+==========
+
+CUDA Version 12.8.1
+
+Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
+
+WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
+   Use the NVIDIA Container Toolkit to start this container with GPU support; see
+   https://docs.nvidia.com/datacenter/cloud-native/ .
+
+/usr/local/lib/python3.10/dist-packages/pgmpy/estimators/__init__.py:4: FutureWarning: `pgmpy.estimators.StructureScore` is deprecated and will be removed in a future release. Use `pgmpy.structure_score` instead.
+  from .StructureScore import (
+ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+synthcity 0.2.12 requires arfpy, which is not installed.
+synthcity 0.2.12 requires be-great>=0.0.5; python_version >= "3.9", which is not installed.
+synthcity 0.2.12 requires decaf-synthetic-data>=0.1.6, which is not installed.
+synthcity 0.2.12 requires fastai<2.8, which is not installed.
+synthcity 0.2.12 requires fastcore<1.8, which is not installed.
+synthcity 0.2.12 requires fflows, which is not installed.
+synthcity 0.2.12 requires geomloss, which is not installed.
+synthcity 0.2.12 requires importlib-metadata, which is not installed.
+synthcity 0.2.12 requires lifelines<0.30.0,>=0.29.0, which is not installed.
+synthcity 0.2.12 requires monai, which is not installed.
+synthcity 0.2.12 requires nflows>=0.14, which is not installed.
+synthcity 0.2.12 requires opacus>=1.3, which is not installed.
+synthcity 0.2.12 requires pycox, which is not installed.
+synthcity 0.2.12 requires pykeops, which is not installed.
+synthcity 0.2.12 requires redis, which is not installed.
+synthcity 0.2.12 requires shap, which is not installed.
+synthcity 0.2.12 requires tenacity, which is not installed.
+synthcity 0.2.12 requires tsai; python_version > "3.7", which is not installed.
+synthcity 0.2.12 requires xgbse>=0.3.1, which is not installed.
+synthcity 0.2.12 requires networkx<3.0,>2.0, but you have networkx 3.4.2 which is incompatible.
+synthcity 0.2.12 requires numpy<2.0,>=1.20, but you have numpy 2.2.6 which is incompatible.
+synthcity 0.2.12 requires pgmpy<1.0, but you have pgmpy 1.1.0 which is incompatible.
+synthcity 0.2.12 requires torch<2.3,>=2.1, but you have torch 2.8.0+cu128 which is incompatible.
+synthcity 0.2.12 requires xgboost<3.0.0, but you have xgboost 3.2.0 which is incompatible.
+WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
+[BayesNet] Generated 480000 rows (requested 480000) -> /work/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet-c15-480000-20260422_060347.csv

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c15",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-train.csv",
+      "exists": true,
+      "size": 68720503,
+      "sha256": "b2c9c7218c38c30f955677ff4f014696d393c4a1989b132f3ec5105ce729f1a8"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-val.csv",
+      "exists": true,
+      "size": 8588883,
+      "sha256": "797cea41b1f718b431a93dfdba88dce89147b5cac8671490bde57f695da6e464"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c15/c15-test.csv",
+      "exists": true,
+      "size": 8590453,
+      "sha256": "1d6193a8b1dadfbb6a33ae7e63ea7bd22bbbbbc60ddfd06ada3e34fbf207ac24"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c15/c15-dataset_profile.json",
+      "exists": true,
+      "size": 9902,
+      "sha256": "2af6a0c86c2286da0abe6bf4b86a42f17623204744ecc15990aa228600980db6"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c15/c15-dataset_contract_v1.json",
+      "exists": true,
+      "size": 12033,
+      "sha256": "572403793859e059ebed0d0d5e2a9b306bf402681341439c0b78710a29aff262"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,509 @@
+{
+  "dataset_id": "c15",
+  "target_column": "target",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 20000,
+        "unique_ratio": 0.041667,
+        "example_values": [
+          "394544",
+          "509036",
+          "432991",
+          "567594",
+          "490005"
+        ]
+      }
+    },
+    {
+      "name": "bin_0",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.029965,
+        "unique_count": 2,
+        "unique_ratio": 4e-06,
+        "example_values": [
+          "1.0",
+          "0.0"
+        ]
+      }
+    },
+    {
+      "name": "bin_1",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.029906,
+        "unique_count": 2,
+        "unique_ratio": 4e-06,
+        "example_values": [
+          "0.0",
+          "1.0"
+        ]
+      }
+    },
+    {
+      "name": "bin_2",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.029908,
+        "unique_count": 2,
+        "unique_ratio": 4e-06,
+        "example_values": [
+          "0.0",
+          "1.0"
+        ]
+      }
+    },
+    {
+      "name": "bin_3",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030123,
+        "unique_count": 2,
+        "unique_ratio": 4e-06,
+        "example_values": [
+          "F",
+          "T"
+        ]
+      }
+    },
+    {
+      "name": "bin_4",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030206,
+        "unique_count": 2,
+        "unique_ratio": 4e-06,
+        "example_values": [
+          "N",
+          "Y"
+        ]
+      }
+    },
+    {
+      "name": "nom_0",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030421,
+        "unique_count": 3,
+        "unique_ratio": 6e-06,
+        "example_values": [
+          "Red",
+          "Blue",
+          "Green"
+        ]
+      }
+    },
+    {
+      "name": "nom_1",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030152,
+        "unique_count": 6,
+        "unique_ratio": 1.3e-05,
+        "example_values": [
+          "Star",
+          "Triangle",
+          "Trapezoid",
+          "Polygon",
+          "Square"
+        ]
+      }
+    },
+    {
+      "name": "nom_2",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.03011,
+        "unique_count": 6,
+        "unique_ratio": 1.3e-05,
+        "example_values": [
+          "Dog",
+          "Lion",
+          "Hamster",
+          "Cat",
+          "Axolotl"
+        ]
+      }
+    },
+    {
+      "name": "nom_3",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030267,
+        "unique_count": 6,
+        "unique_ratio": 1.3e-05,
+        "example_values": [
+          "Costa Rica",
+          "India",
+          "Russia",
+          "Canada",
+          "Finland"
+        ]
+      }
+    },
+    {
+      "name": "nom_4",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030035,
+        "unique_count": 4,
+        "unique_ratio": 9e-06,
+        "example_values": [
+          "Piano",
+          "Theremin",
+          "Bassoon",
+          "Oboe"
+        ]
+      }
+    },
+    {
+      "name": "nom_5",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.02974,
+        "unique_count": 1220,
+        "unique_ratio": 0.00262,
+        "example_values": [
+          "90217d5cf",
+          "e00572717",
+          "7997c8f99",
+          "febb8819e",
+          "9e7d06868"
+        ]
+      }
+    },
+    {
+      "name": "nom_6",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030229,
+        "unique_count": 1518,
+        "unique_ratio": 0.003261,
+        "example_values": [
+          "76d705530",
+          "c7690caae",
+          "316fb49a2",
+          "93ecdf869",
+          "4f027736c"
+        ]
+      }
+    },
+    {
+      "name": "nom_7",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.030133,
+        "unique_count": 222,
+        "unique_ratio": 0.000477,
+        "example_values": [
+          "21955ee26",
+          "0764632bc",
+          "2c4f9614e",
+          "c3ad85934",
+          "5627228e9"
+        ]
+      }
+    },
+    {
+      "name": "nom_8",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.029587,
+        "unique_count": 222,
+        "unique_ratio": 0.000477,
+        "example_values": [
+          "77d41330d",
+          "fbea9bb4a",
+          "8dc88fa15",
+          "46a005ddf",
+          "a89e1c1dc"
+        ]
+      }
+    },
+    {
+      "name": "nom_9",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
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SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/public_gate/public_gate_report.json

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+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/public_gate/staged_input_manifest.json

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SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/runtime_result.json

@@ -0,0 +1,15 @@
+{
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+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/bayesnet/adapter_report.json

@@ -0,0 +1,7 @@
+{
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+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/bayesnet/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/bayesnet/adapter_transforms_applied.json

@@ -0,0 +1 @@
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SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/bayesnet/model_input_manifest.json

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+        "unique_ratio": 1.3e-05,
+        "example_values": [
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+    {
+      "name": "month",
+      "role": "feature",
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+      "role": "target",
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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/staged/public/staged_features.json

@@ -0,0 +1,127 @@
+[
+  {
+    "feature_name": "id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_0",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_1",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_2",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_3",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "bin_4",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_0",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_6",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_7",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_8",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "nom_9",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_0",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ord_5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "day",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "month",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "target",
+    "data_type": "binary",
+    "is_target": true
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/train_20260422_060228.log

@@ -0,0 +1,55 @@
+
+==========
+== CUDA ==
+==========
+
+CUDA Version 12.8.1
+
+Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
+
+WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
+   Use the NVIDIA Container Toolkit to start this container with GPU support; see
+   https://docs.nvidia.com/datacenter/cloud-native/ .
+
+/usr/local/lib/python3.10/dist-packages/pgmpy/estimators/__init__.py:4: FutureWarning: `pgmpy.estimators.StructureScore` is deprecated and will be removed in a future release. Use `pgmpy.structure_score` instead.
+  from .StructureScore import (
+ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+synthcity 0.2.12 requires arfpy, which is not installed.
+synthcity 0.2.12 requires be-great>=0.0.5; python_version >= "3.9", which is not installed.
+synthcity 0.2.12 requires decaf-synthetic-data>=0.1.6, which is not installed.
+synthcity 0.2.12 requires fastai<2.8, which is not installed.
+synthcity 0.2.12 requires fastcore<1.8, which is not installed.
+synthcity 0.2.12 requires fflows, which is not installed.
+synthcity 0.2.12 requires geomloss, which is not installed.
+synthcity 0.2.12 requires importlib-metadata, which is not installed.
+synthcity 0.2.12 requires lifelines<0.30.0,>=0.29.0, which is not installed.
+synthcity 0.2.12 requires monai, which is not installed.
+synthcity 0.2.12 requires nflows>=0.14, which is not installed.
+synthcity 0.2.12 requires opacus>=1.3, which is not installed.
+synthcity 0.2.12 requires pycox, which is not installed.
+synthcity 0.2.12 requires pykeops, which is not installed.
+synthcity 0.2.12 requires redis, which is not installed.
+synthcity 0.2.12 requires shap, which is not installed.
+synthcity 0.2.12 requires tenacity, which is not installed.
+synthcity 0.2.12 requires tsai; python_version > "3.7", which is not installed.
+synthcity 0.2.12 requires xgbse>=0.3.1, which is not installed.
+synthcity 0.2.12 requires networkx<3.0,>2.0, but you have networkx 3.4.2 which is incompatible.
+synthcity 0.2.12 requires numpy<2.0,>=1.20, but you have numpy 2.2.6 which is incompatible.
+synthcity 0.2.12 requires pgmpy<1.0, but you have pgmpy 1.1.0 which is incompatible.
+synthcity 0.2.12 requires torch<2.3,>=2.1, but you have torch 2.8.0+cu128 which is incompatible.
+synthcity 0.2.12 requires xgboost<3.0.0, but you have xgboost 3.2.0 which is incompatible.
+WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
+[BayesNet] max_bins=5 (cols_in_df=25, rows=480000)
+[BayesNet] Training on 480000 rows, 25 cols (encoded)
+[BayesNet] TreeSearch on 25000 rows (subsample; full n=480000)
+[BayesNet] Added isolated node to DAG: bin_0
+[BayesNet] Added isolated node to DAG: bin_1
+[BayesNet] Added isolated node to DAG: bin_2
+[BayesNet] Added isolated node to DAG: ord_0
+[BayesNet] Model saved -> /work/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c15/realtabformer/rtf-c15-20260424_180000/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c15",
+  "model": "realtabformer",
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+      "exists": true,
+      "size": 8588883,
+      "sha256": "797cea41b1f718b431a93dfdba88dce89147b5cac8671490bde57f695da6e464"
+    },
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+      "sha256": "2af6a0c86c2286da0abe6bf4b86a42f17623204744ecc15990aa228600980db6"
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+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c15/c15-dataset_contract_v1.json",
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+}