Add files using upload-large-folder tool

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=200)
+syn.to_csv("/work/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/arf-c2-200-20260318_003533.csv", index=False)
+print(f"[ARF] Generated 200 rows -> /work/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/arf-c2-200-20260318_003533.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/DatasetNew/c2/c2-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/c2/arf/arf-c2-20260318_003516/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/gen_20260318_003533.log

@@ -0,0 +1,15 @@
+/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 200 rows -> /work/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/arf-c2-200-20260318_003533.csv

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/train_20260318_003516.log

@@ -0,0 +1,4 @@
+[ARF] Training on 1382 rows, 7 cols
+Initial accuracy is 0.5962373371924746
+Iteration number 1 reached accuracy of 0.3994211287988423.
+[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260318_003516/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=1382)
+syn.to_csv("/work/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/arf-c2-1382-20260318_032516.csv", index=False)
+print(f"[ARF] Generated 1382 rows -> /work/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/arf-c2-1382-20260318_032516.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/DatasetNew/c2/c2-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/c2/arf/arf-c2-20260318_032459/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/gen_20260318_032516.log

@@ -0,0 +1,15 @@
+/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 1382 rows -> /work/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/arf-c2-1382-20260318_032516.csv

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/train_20260318_032459.log

@@ -0,0 +1,4 @@
+[ARF] Training on 1382 rows, 7 cols
+Initial accuracy is 0.5958755426917511
+Iteration number 1 reached accuracy of 0.38929088277858176.
+[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260318_032459/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=1000)
+syn.to_csv("/work/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/arf-c2-1000-20260321_052706.csv", index=False)
+print(f"[ARF] Generated 1000 rows -> /work/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/arf-c2-1000-20260321_052706.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/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/c2/arf/arf-c2-20260321_052647/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/gen_20260321_052706.log

@@ -0,0 +1,15 @@
+/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/c2/arf/arf-c2-20260321_052647/arf-c2-1000-20260321_052706.csv

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c2",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+      "exists": true,
+      "size": 42948,
+      "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+      "exists": true,
+      "size": 5349,
+      "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv",
+      "exists": true,
+      "size": 5448,
+      "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json",
+      "exists": true,
+      "size": 3240,
+      "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3731,
+      "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,144 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "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": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "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": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "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": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c2",
+  "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": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/public_gate/staged_input_manifest.json

@@ -0,0 +1,149 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "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": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
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+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
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+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
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+      "role": "target",
+      "semantic_type": "categorical",
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+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c2",
+  "model": "arf",
+  "run_id": "arf-c2-20260321_052647",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "success",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/arf-c2-1000-20260321_052706.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/arf_model.pkl"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/arf/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/arf/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/arf/adapter_transforms_applied.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/arf/model_input_manifest.json

@@ -0,0 +1,151 @@
+{
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+        ]
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+          "4"
+        ]
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+        ]
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+        ]
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+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/staged_features.json

@@ -0,0 +1,37 @@
+[
+  {
+    "feature_name": "buying",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "maint",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "doors",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "persons",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "lug_boot",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "safety",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/test.csv

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SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/train.csv

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+high,vhigh,2,more,med,high,unacc
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+high,low,3,4,small,med,unacc
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+low,high,5more,more,big,high,vgood
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+high,vhigh,5more,4,big,high,unacc
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+high,low,4,2,big,low,unacc
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+vhigh,vhigh,4,more,big,low,unacc
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+med,low,4,more,big,high,vgood
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+vhigh,low,5more,2,small,high,unacc
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+low,med,3,2,big,med,unacc
+high,med,3,more,small,med,unacc
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+low,low,3,2,med,low,unacc
+low,high,3,4,small,low,unacc
+high,high,5more,4,big,high,acc
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+med,high,4,2,big,low,unacc
+vhigh,low,2,more,small,high,unacc
+high,low,2,2,small,low,unacc
+high,vhigh,5more,4,med,low,unacc
+med,med,5more,4,med,low,unacc
+high,vhigh,2,4,big,low,unacc
+low,med,3,more,small,low,unacc
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SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/staged/public/val.csv

@@ -0,0 +1,173 @@
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+low,low,3,more,med,med,good
+low,med,2,4,big,med,good
+high,vhigh,2,2,small,high,unacc
+med,vhigh,2,2,med,low,unacc
+med,low,4,2,small,med,unacc
+med,med,2,2,small,med,unacc
+low,med,5more,2,small,high,unacc
+vhigh,low,2,4,big,low,unacc
+med,med,3,more,small,med,acc
+med,high,2,2,small,high,unacc
+low,vhigh,4,2,small,high,unacc
+vhigh,med,3,more,small,high,acc
+vhigh,med,3,4,big,low,unacc
+med,med,2,4,small,high,acc
+vhigh,high,3,2,big,med,unacc
+med,high,2,more,big,low,unacc
+low,high,5more,4,med,high,vgood
+vhigh,high,4,4,med,high,unacc
+low,low,3,4,big,high,vgood
+low,med,5more,4,med,low,unacc
+high,vhigh,4,2,big,high,unacc
+vhigh,high,2,4,small,high,unacc
+high,vhigh,4,2,med,high,unacc
+high,high,5more,4,small,low,unacc
+high,med,5more,4,big,low,unacc
+vhigh,med,2,4,med,high,acc
+vhigh,vhigh,3,4,med,med,unacc
+med,high,3,2,small,high,unacc
+high,med,2,4,big,med,acc
+med,low,3,4,med,low,unacc
+vhigh,vhigh,2,2,small,high,unacc
+med,med,2,2,big,med,unacc
+med,med,2,more,big,med,acc
+vhigh,low,2,2,big,med,unacc
+high,low,2,4,small,med,unacc
+low,med,5more,more,med,med,good
+low,low,5more,2,small,med,unacc
+low,low,2,4,small,high,good
+low,med,3,2,small,low,unacc
+low,vhigh,2,4,med,med,unacc
+high,high,4,more,small,low,unacc
+med,low,5more,4,med,low,unacc
+vhigh,med,2,2,small,high,unacc
+med,high,4,4,med,high,acc
+vhigh,low,3,4,big,low,unacc
+med,high,4,2,small,low,unacc
+high,high,2,2,small,low,unacc
+low,high,4,4,big,med,acc
+low,high,4,more,med,low,unacc
+vhigh,vhigh,2,2,big,med,unacc
+med,vhigh,4,4,med,low,unacc
+vhigh,low,2,2,small,low,unacc
+med,vhigh,3,2,small,low,unacc
+high,high,4,2,big,high,unacc
+low,low,5more,more,big,med,good
+low,low,4,4,small,high,good
+low,vhigh,2,4,big,high,acc
+low,low,2,4,big,med,good

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/train_20260321_052647.log

@@ -0,0 +1,4 @@
+[ARF] Training on 1382 rows, 7 cols
+Initial accuracy is 0.6175832127351665
+Iteration number 1 reached accuracy of 0.39363241678726485.
+[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260321_052647/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=1382)
+syn.to_csv("/work/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/arf-c2-1382-20260330_065244.csv", index=False)
+print(f"[ARF] Generated 1382 rows -> /work/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/arf-c2-1382-20260330_065244.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/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/c2/arf/arf-c2-20260321_060616/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/gen_20260321_060635.log

@@ -0,0 +1,15 @@
+/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/c2/arf/arf-c2-20260321_060616/arf-c2-1000-20260321_060635.csv

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/gen_20260330_065244.log

@@ -0,0 +1,15 @@
+/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 1382 rows -> /work/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/arf-c2-1382-20260330_065244.csv

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c2",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+      "exists": true,
+      "size": 42948,
+      "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+      "exists": true,
+      "size": 5349,
+      "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv",
+      "exists": true,
+      "size": 5448,
+      "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json",
+      "exists": true,
+      "size": 3240,
+      "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3731,
+      "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,144 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "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": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "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": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "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": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c2",
+  "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": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/public_gate/staged_input_manifest.json

@@ -0,0 +1,149 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "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": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "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": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "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": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c2",
+  "model": "arf",
+  "run_id": "arf-c2-20260321_060616",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "skipped",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/arf-c2-1382-20260330_065244.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/arf/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/arf/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/arf/adapter_transforms_applied.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/arf/model_input_manifest.json

@@ -0,0 +1,151 @@
+{
+  "dataset_id": "c2",
+  "model": "arf",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
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+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
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+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
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+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/staged_features.json

@@ -0,0 +1,37 @@
+[
+  {
+    "feature_name": "buying",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "maint",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "doors",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "persons",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "lug_boot",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "safety",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/test.csv

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+low,vhigh,5more,4,small,med,unacc
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+vhigh,low,5more,more,med,low,unacc
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+med,low,2,more,med,low,unacc
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+high,med,2,4,small,med,unacc
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+high,vhigh,5more,2,med,med,unacc
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+high,low,4,more,small,med,unacc
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+low,high,3,2,big,high,unacc
+high,low,2,more,big,low,unacc
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+high,low,3,4,big,low,unacc
+high,low,5more,more,big,low,unacc
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+vhigh,high,2,more,small,high,unacc
+high,low,5more,4,big,low,unacc
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+high,med,3,2,med,low,unacc
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SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/staged/public/val.csv

@@ -0,0 +1,173 @@
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SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/train_20260321_060616.log

@@ -0,0 +1,4 @@
+[ARF] Training on 1382 rows, 7 cols
+Initial accuracy is 0.5741678726483358
+Iteration number 1 reached accuracy of 0.40123010130246023.
+[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260321_060616/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/_arf_generate.py

@@ -0,0 +1,23 @@
+import pickle
+import pandas as pd
+
+n_target = int(1382)
+with open("/work/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=n_target)
+syn = syn.reset_index(drop=True)
+if len(syn) > n_target:
+    syn = syn.iloc[:n_target]
+elif len(syn) < n_target:
+    parts = [syn]
+    tries = 0
+    while sum(len(p) for p in parts) < n_target and tries < 64:
+        tries += 1
+        need = n_target - sum(len(p) for p in parts)
+        chunk = model.forge(n=max(need, 1)).reset_index(drop=True)
+        if len(chunk) == 0:
+            break
+        parts.append(chunk)
+    syn = pd.concat(parts, ignore_index=True).iloc[:n_target]
+syn.to_csv("/work/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/arf-c2-1382-20260422_055931.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/arf-c2-1382-20260422_055931.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/_arf_train.py

@@ -0,0 +1,37 @@
+import pickle
+import numpy as np
+import pandas as pd
+from arfpy import arf
+
+def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame:
+    """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。"""
+    df = df.replace([np.inf, -np.inf], np.nan)
+    df = df.dropna(axis=1, how="all")
+    for col in df.select_dtypes(include=[np.number]).columns:
+        med = df[col].median()
+        if pd.isna(med):
+            med = 0.0
+        df[col] = df[col].fillna(med)
+        nu = int(df[col].nunique(dropna=True))
+        if nu <= 1:
+            continue
+        lo, hi = df[col].quantile(0.001), df[col].quantile(0.999)
+        if pd.notna(lo) and pd.notna(hi) and lo < hi:
+            df[col] = df[col].clip(lo, hi)
+    return df
+
+df = pd.read_csv("/work/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/staged/public/train.csv")
+df = _sanitize_for_arf(df)
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/gen_20260422_055931.log

@@ -0,0 +1,15 @@
+/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 1382 rows (requested 1382) -> /work/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/arf-c2-1382-20260422_055931.csv

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c2",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+      "exists": true,
+      "size": 42948,
+      "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+      "exists": true,
+      "size": 5349,
+      "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv",
+      "exists": true,
+      "size": 5448,
+      "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json",
+      "exists": true,
+      "size": 3240,
+      "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3731,
+      "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,144 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "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": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "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": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "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": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c2",
+  "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": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/public_gate/staged_input_manifest.json

@@ -0,0 +1,149 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "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": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "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": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "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": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c2",
+  "model": "arf",
+  "run_id": "arf-c2-20260422_055912",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "success",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/arf-c2-1382-20260422_055931.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/arf_model.pkl"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/staged/arf/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c2/arf/arf-c2-20260422_055912/staged/arf/model_input_manifest.json"
+}