Add files using upload-large-folder tool

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=1000)
+syn.to_csv("/work/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/arf-c11-1000-20260320_042546.csv", index=False)
+print(f"[ARF] Generated 1000 rows -> /work/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/arf-c11-1000-20260320_042546.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/DatasetNew/c11/c11-train.csv")
+df = df.dropna(axis=1, how="all")
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/gen_20260320_042546.log

@@ -0,0 +1,87 @@
+/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]:
+/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]:
+/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 1000 rows -> /work/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/arf-c11-1000-20260320_042546.csv

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/train_20260320_030600.log

@@ -0,0 +1,7 @@
+[ARF] Training on 54045 rows, 43 cols
+Initial accuracy is 0.9712554352854103
+Iteration number 1 reached accuracy of 0.6937737070959386.
+Iteration number 2 reached accuracy of 0.6076417799981497.
+Iteration number 3 reached accuracy of 0.6042279581829957.
+Iteration number 4 reached accuracy of 0.6047367934128967.
+[ARF] Model saved -> /work/output-SpecializedModels/c11/arf/arf-c11-20260320_030600/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=54045)
+syn.to_csv("/work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/arf-c11-54045-20260330_065348.csv", index=False)
+print(f"[ARF] Generated 54045 rows -> /work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/arf-c11-54045-20260330_065348.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/gen_20260321_133718.log

@@ -0,0 +1,87 @@
+/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]:
+/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]:
+/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 1000 rows -> /work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/arf-c11-1000-20260321_133718.csv

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/gen_20260330_065348.log

@@ -0,0 +1,87 @@
+/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]:
+/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]:
+/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 54045 rows -> /work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/arf-c11-54045-20260330_065348.csv

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c11",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c11/c11-train.csv",
+      "exists": true,
+      "size": 4828646,
+      "sha256": "bed2dabdf7082070f69164cbe9701e2e93753c20bafd0386a63a2208f46dbd4b"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c11/c11-val.csv",
+      "exists": true,
+      "size": 603585,
+      "sha256": "43bf4a406d9ad580d8bddff90f9649eaf909bff3e95a94a59b4ac70695fbf57a"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c11/c11-test.csv",
+      "exists": true,
+      "size": 603825,
+      "sha256": "585bd7e31b07354186f2dd38fedec7b8d4a88d1a2909db7e54be7b572f7e0e91"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c11/c11-dataset_profile.json",
+      "exists": true,
+      "size": 13793,
+      "sha256": "511531728c8d5902b1edf2b7fcc5eae14d6af6749296089501b93202c182d693"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c11/c11-dataset_contract_v1.json",
+      "exists": true,
+      "size": 18508,
+      "sha256": "640ed9b37d9083ca08a678117325282f1c73b57a0ada8df5eff2c15395c260a0"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,824 @@
+{
+  "dataset_id": "c11",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "a1",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "o",
+          "x"
+        ]
+      }
+    },
+    {
+      "name": "a2",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "a3",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "a4",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "a5",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "a6",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "o",
+          "x"
+        ]
+      }
+    },
+    {
+      "name": "b1",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "b2",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "b3",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "b4",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "o",
+          "x"
+        ]
+      }
+    },
+    {
+      "name": "b5",
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SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/public_gate/public_gate_report.json

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SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/public_gate/staged_input_manifest.json

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SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/runtime_result.json

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SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/staged/arf/adapter_report.json

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SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/staged/arf/adapter_transforms_applied.json

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SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/staged/public/staged_features.json

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+  },
+  {
+    "feature_name": "d3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "d4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "d5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "d6",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "e1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "e2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "e3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "e4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "e5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "e6",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "f1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "f2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "f3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "f4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "f5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "f6",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "g1",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "g2",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "g3",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "g4",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "g5",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "g6",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/train_20260321_113609.log

@@ -0,0 +1,11 @@
+[ARF] Training on 54045 rows, 43 cols
+Initial accuracy is 0.9719770561569063
+Iteration number 1 reached accuracy of 0.6949856600980664.
+Iteration number 2 reached accuracy of 0.6136830419095198.
+Iteration number 3 reached accuracy of 0.6049033213063189.
+/usr/local/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:615: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable OOB estimates.
+  warn(
+Iteration number 4 reached accuracy of 0.6021463595152188.
+Iteration number 5 reached accuracy of 0.5986862799518919.
+Iteration number 6 reached accuracy of 0.6137478027569618.
+[ARF] Model saved -> /work/output-SpecializedModels/c11/arf/arf-c11-20260321_113607/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/_bayesnet_generate.py

@@ -0,0 +1,43 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=54045).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+if os.path.exists(const_path):
+    with open(const_path) as _f:
+        const_cols = _json.load(_f)
+    for col, val in const_cols.items():
+        syn[col] = val
+        print(f"[BayesNet] Restored constant column '{col}' = {val}")
+
+syn.to_csv("/work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/bayesnet-c11-54045-20260318_034124.csv", index=False)
+print(f"[BayesNet] Generated 54045 rows -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/bayesnet-c11-54045-20260318_034124.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/_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/c11/c11-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/c11/bayesnet/bayesnet-c11-20260318_034037/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/c11/bayesnet/bayesnet-c11-20260318_034037/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/gen_20260318_034124.log

@@ -0,0 +1,8 @@
+03/17/2026 19:41:54:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:41:55:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:41:55:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:41:59:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:42:00:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:42:00:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Generated 54045 rows -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/bayesnet-c11-54045-20260318_034124.csv

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/train_20260318_034037.log

@@ -0,0 +1,5 @@
+[2026-03-17T19:41:03.196642+0000][1][CRITICAL] Error importing TabularGoggle: No module named 'dgl'
+[2026-03-17T19:41:03.207281+0000][1][CRITICAL] module disabled: /pip_libs/synthcity/plugins/generic/plugin_goggle.py
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Training on 54045 rows, 43 cols
+[BayesNet] Model saved -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260318_034037/bayesnet_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/_bayesnet_generate.py

@@ -0,0 +1,43 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=54045).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+if os.path.exists(const_path):
+    with open(const_path) as _f:
+        const_cols = _json.load(_f)
+    for col, val in const_cols.items():
+        syn[col] = val
+        print(f"[BayesNet] Restored constant column '{col}' = {val}")
+
+syn.to_csv("/work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet-c11-54045-20260330_065348.csv", index=False)
+print(f"[BayesNet] Generated 54045 rows -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet-c11-54045-20260330_065348.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/_bayesnet_train.py

@@ -0,0 +1,62 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...")
+        # Install synthcity with numpy<2 to avoid conflicts
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        # Remove torch/torchvision from pip_libs to avoid shadowing system versions
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+from synthcity.plugins import Plugins
+import pickle
+import pandas as pd
+from synthcity.plugins.core.dataloader import GenericDataLoader
+
+df = pd.read_csv("/work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+
+# Drop zero-variance columns (only 1 unique value) to avoid
+# synthcity encoder KeyError during generation
+import json as _json
+const_cols = {}
+for col in list(df.columns):
+    nuniq = df[col].nunique()
+    if nuniq <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}' (value={const_cols[col]})")
+
+# Save constant columns info so generate can restore them
+const_path = "/work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/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/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/gen_20260321_062451.log

@@ -0,0 +1,8 @@
+03/20/2026 22:25:19:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:25:19:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:25:19:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:25:20:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:25:20:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:25:20:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Generated 1000 rows -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet-c11-1000-20260321_062451.csv

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/gen_20260330_065348.log

@@ -0,0 +1,8 @@
+03/29/2026 22:54:20:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:21:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:21:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:25:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:26:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:26:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Generated 54045 rows -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet-c11-54045-20260330_065348.csv

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c11",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c11/c11-train.csv",
+      "exists": true,
+      "size": 4828646,
+      "sha256": "bed2dabdf7082070f69164cbe9701e2e93753c20bafd0386a63a2208f46dbd4b"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c11/c11-val.csv",
+      "exists": true,
+      "size": 603585,
+      "sha256": "43bf4a406d9ad580d8bddff90f9649eaf909bff3e95a94a59b4ac70695fbf57a"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c11/c11-test.csv",
+      "exists": true,
+      "size": 603825,
+      "sha256": "585bd7e31b07354186f2dd38fedec7b8d4a88d1a2909db7e54be7b572f7e0e91"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c11/c11-dataset_profile.json",
+      "exists": true,
+      "size": 13793,
+      "sha256": "511531728c8d5902b1edf2b7fcc5eae14d6af6749296089501b93202c182d693"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c11/c11-dataset_contract_v1.json",
+      "exists": true,
+      "size": 18508,
+      "sha256": "640ed9b37d9083ca08a678117325282f1c73b57a0ada8df5eff2c15395c260a0"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,824 @@
+{
+  "dataset_id": "c11",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "a1",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "o",
+          "x"
+        ]
+      }
+    },
+    {
+      "name": "a2",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "a3",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 5.6e-05,
+        "example_values": [
+          "b",
+          "x",
+          "o"
+        ]
+      }
+    },
+    {
+      "name": "a4",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
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SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/public_gate/public_gate_report.json

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+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c11/c11-test.csv"
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SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/public_gate/staged_input_manifest.json

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SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/runtime_result.json

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SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/staged/bayesnet/adapter_report.json

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+}

SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/staged/public/staged_features.json

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SynthesizePipeline_Archive/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/train_20260321_062400.log

@@ -0,0 +1,5 @@
+[2026-03-20T22:24:27.032719+0000][1][CRITICAL] Error importing TabularGoggle: No module named 'dgl'
+[2026-03-20T22:24:27.043452+0000][1][CRITICAL] module disabled: /pip_libs/synthcity/plugins/generic/plugin_goggle.py
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Training on 54045 rows, 43 cols
+[BayesNet] Model saved -> /work/output-SpecializedModels/c11/bayesnet/bayesnet-c11-20260321_062357/bayesnet_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c11/ctgan/ctgan-c11-20260320_051632/ctgan_metadata.json

@@ -0,0 +1,176 @@
+{
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SynthesizePipeline_Archive/output-SpecializedModels/c11/ctgan/ctgan-c11-20260320_051632/models_300epochs/train_20260320_051632.log

@@ -0,0 +1,2 @@
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+  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass

SynthesizePipeline_Archive/output-SpecializedModels/c11/ctgan/ctgan-c11-20260320_235904/ctgan_metadata.json

@@ -0,0 +1,176 @@
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SynthesizePipeline_Archive/output-SpecializedModels/c11/ctgan/ctgan-c11-20260321_182415/ctgan_metadata.json

@@ -0,0 +1,176 @@
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SynthesizePipeline_Archive/output-SpecializedModels/c11/ctgan/ctgan-c11-20260321_182415/input_snapshot.json

@@ -0,0 +1,36 @@
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