Add syntheticFail c2

syntheticFail/c2/arf/arf-c2-20260504_204543/_arf_train.py

@@ -0,0 +1,46 @@
+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
+        q_low = float(os.environ.get("ARF_CLIP_QUANTILE_LOW", "0.001"))
+        q_high = float(os.environ.get("ARF_CLIP_QUANTILE_HIGH", "0.999"))
+        lo, hi = df[col].quantile(q_low), df[col].quantile(q_high)
+        if pd.notna(lo) and pd.notna(hi) and lo < hi:
+            df[col] = df[col].clip(lo, hi)
+    return df
+
+df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/train.csv")
+df = _sanitize_for_arf(df)
+num_trees = int(os.environ.get("ARF_NUM_TREES", "30"))
+delta = float(os.environ.get("ARF_DELTA", "0"))
+max_iters = int(os.environ.get("ARF_MAX_ITERS", "10"))
+early_stop = (os.environ.get("ARF_EARLY_STOP", "true").strip().lower() in ("1", "true", "yes"))
+verbose = (os.environ.get("ARF_VERBOSE", "true").strip().lower() in ("1", "true", "yes"))
+min_node_size = int(os.environ.get("ARF_MIN_NODE_SIZE", "5"))
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+print(f"[ARF] Config num_trees={num_trees} delta={delta} max_iters={max_iters} early_stop={early_stop} min_node_size={min_node_size}")
+
+model = arf.arf(x=df, num_trees=num_trees, delta=delta, max_iters=max_iters, early_stop=early_stop, verbose=verbose, min_node_size=min_node_size)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/arf_model.pkl")

syntheticFail/c2/arf/arf-c2-20260504_204543/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"
+    }
+  }
+}

syntheticFail/c2/arf/arf-c2-20260504_204543/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"
+        ]
+      }
+    }
+  ]
+}

syntheticFail/c2/arf/arf-c2-20260504_204543/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"
+  }
+}

syntheticFail/c2/arf/arf-c2-20260504_204543/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-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/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"
+        ]
+      }
+    }
+  ]
+}

syntheticFail/c2/arf/arf-c2-20260504_204543/run_config.json

@@ -0,0 +1,43 @@
+{
+  "schema_version": 1,
+  "recorded_at": "2026-05-04T20:45:43",
+  "dataset_id": "c2",
+  "model": "arf",
+  "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543",
+  "dataset_source_requested": "new",
+  "dataset_source_resolved": "new",
+  "cli_args": {
+    "model": "arf",
+    "dataset": "c2",
+    "dataset_source": "new",
+    "train": true,
+    "generate": true,
+    "num_rows": 0,
+    "epochs": null,
+    "output_dir": null,
+    "model_dir": null,
+    "work_dir": null,
+    "resume": false,
+    "no_stats": false
+  },
+  "resolved": {
+    "num_rows": 1382,
+    "model_path": null,
+    "output_csv": null
+  },
+  "input_artifacts": {
+    "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/public_gate/public_gate_report.json",
+    "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/public_gate/staged_input_manifest.json",
+    "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/arf/model_input_manifest.json",
+    "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/train.csv",
+    "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/staged_features.json",
+    "target_column": "class",
+    "task_type": "classification"
+  },
+  "env_overrides": {
+    "ARF_DELTA": "0.01",
+    "ARF_MAX_ITERS": "3",
+    "ARF_MIN_NODE_SIZE": "7",
+    "ARF_NUM_TREES": "11"
+  }
+}

syntheticFail/c2/arf/arf-c2-20260504_204543/runtime_result.json

@@ -0,0 +1,24 @@
+{
+  "dataset_id": "c2",
+  "model": "arf",
+  "run_id": "arf-c2-20260504_204543",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "fail",
+  "generate_status": "skipped",
+  "reason_code": "adapter_runtime_error",
+  "reason_detail": "Command '['docker', 'run', '--rm', '--init', '--cidfile', '/tmp/bench_docker_arf_7ftz3yhi/container.cid', '-e', 'ARF_NUM_TREES=11', '-e', 'ARF_MAX_ITERS=3', '-e', 'ARF_MIN_NODE_SIZE=7', '-e', 'ARF_DELTA=0.01', '-v', '/data/jialinzhang/SynthesizePipeline-server:/work', '-w', '/work', 'benchmark:arf-zjl', 'python', '/work/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/_arf_train.py']' returned non-zero exit status 1.",
+  "artifacts": {},
+  "timings": {
+    "train": {
+      "started_at": "2026-05-04T20:45:43",
+      "ended_at": "2026-05-04T20:45:45",
+      "duration_sec": 2.089
+    },
+    "generate": {
+      "started_at": null,
+      "ended_at": null,
+      "duration_sec": null
+    }
+  }
+}

syntheticFail/c2/arf/arf-c2-20260504_204543/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-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/arf/model_input_manifest.json"
+}

syntheticFail/c2/arf/arf-c2-20260504_204543/staged/arf/adapter_transforms_applied.json

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

syntheticFail/c2/arf/arf-c2-20260504_204543/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": [],
+      "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"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/arf/arf-c2-20260504_204543/public_gate/public_gate_report.json"
+}

syntheticFail/c2/arf/arf-c2-20260504_204543/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
+  }
+]

syntheticFail/c2/arf/arf-c2-20260504_204543/staged/public/test.csv

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+size 5273

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+size 5176

syntheticFail/c2/arf/arf-c2-20260504_204543/train_20260504_204543.log

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+size 500

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/_bayesnet_train.py

@@ -0,0 +1,146 @@
+
+            import json
+import os
+            import pickle
+            import subprocess
+            import sys
+            import warnings
+
+            import numpy as np
+            import pandas as pd
+            from pgmpy.estimators import TreeSearch
+            from pgmpy.models import DiscreteBayesianNetwork
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            def _ensure_cloudpickle():
+                try:
+                    import cloudpickle  # noqa: F401
+                except ModuleNotFoundError:
+                    subprocess.check_call(
+                        [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"],
+                    )
+
+            _ensure_cloudpickle()
+
+            with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/bayesnet_coltypes.json", "r", encoding="utf-8") as _f:
+                colmeta = json.load(_f)
+            integer_columns = set(colmeta.get("integer_columns") or [])
+
+            df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/train.csv")
+            df = df.dropna(axis=1, how="all")
+            full_column_order = list(df.columns)
+
+            const_cols = {}
+            for col in list(df.columns):
+                if df[col].nunique(dropna=True) <= 1:
+                    const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+                    df = df.drop(columns=[col])
+                    print(f"[BayesNet] Dropped zero-variance column '{col}'")
+
+            const_path = "/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+            with open(const_path, "w", encoding="utf-8") as _f:
+                json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+            inverse = {"categorical": {}, "continuous": {}}
+            enc = pd.DataFrame(index=df.index)
+            _n_samples = len(df)
+            _n_plan = sum(
+                1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns
+            )
+            max_bins = int(os.environ.get("BAYESNET_MAX_BINS", "0"))
+            max_cat_levels = int(os.environ.get("BAYESNET_MAX_CAT_LEVELS", "0"))
+            if max_bins <= 0:
+                max_bins = 10
+            if max_cat_levels <= 0:
+                max_cat_levels = 256
+            auto_caps = os.environ.get("BAYESNET_DISABLE_AUTO_CAPS", "0").strip().lower() not in ("1", "true", "yes")
+            if auto_caps and max_bins == 10 and max_cat_levels == 256:
+                if _n_plan > 35 or _n_samples > 200000:
+                    max_bins = 5
+                    max_cat_levels = 64
+                if _n_plan > 55:
+                    max_bins = 4
+                    max_cat_levels = 32
+            struct_rows = int(os.environ.get("BAYESNET_STRUCT_ROWS", "25000"))
+            fit_rows = int(os.environ.get("BAYESNET_FIT_ROWS", "120000"))
+            estimator_type = (os.environ.get("BAYESNET_ESTIMATOR_TYPE", "chow-liu") or "chow-liu").strip()
+            edge_weights_fn = (os.environ.get("BAYESNET_EDGE_WEIGHTS_FN", "mutual_info") or "mutual_info").strip()
+            root_node = (os.environ.get("BAYESNET_ROOT_NODE", "") or "").strip() or None
+            n_jobs = int(os.environ.get("BAYESNET_N_JOBS", "1"))
+            print(
+                f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels}, struct_rows={struct_rows}, fit_rows={fit_rows}, estimator_type={estimator_type}, edge_weights_fn={edge_weights_fn}, root_node={root_node}, n_jobs={n_jobs} "
+                f"(cols_in_df={_n_plan}, rows={_n_samples})"
+            )
+
+            for entry in colmeta["columns"]:
+                name = entry["name"]
+                if name not in df.columns:
+                    continue
+                kind = entry["type"]
+                s = df[name]
+                if kind == "categorical":
+                    s2 = s.astype(str).fillna("__NA__")
+                    counts = s2.value_counts(dropna=False)
+                    if len(counts) > max_cat_levels:
+                        keep = set(counts.index[: max_cat_levels - 1].tolist())
+                        s2 = s2.map(lambda x: x if x in keep else "__OTHER__")
+                    uniques = sorted(s2.dropna().unique(), key=lambda x: str(x))
+                    mapping = {str(v): i for i, v in enumerate(uniques)}
+                    inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))]
+                    enc[name] = s2.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int)
+                else:
+                    s_num = pd.to_numeric(s, errors="coerce")
+                    nu = int(s_num.nunique(dropna=True))
+                    q = min(max_bins, max(2, nu))
+                    if nu < 2:
+                        enc[name] = np.zeros(len(s_num), dtype=int)
+                        lo, hi = float(s_num.min()), float(s_num.max())
+                        inverse["continuous"][name] = [lo, hi]
+                    else:
+                        try:
+                            _, bins = pd.qcut(
+                                s_num, q=q, retbins=True, duplicates="drop"
+                            )
+                        except Exception:
+                            med = float(s_num.median())
+                            s2 = s_num.fillna(med)
+                            _, bins = pd.qcut(
+                                s2, q=min(q, 3), retbins=True, duplicates="drop"
+                            )
+                        bins = np.asarray(bins, dtype=float)
+                        lab = pd.cut(
+                            s_num, bins=bins, labels=False, include_lowest=True
+                        )
+                        enc[name] = lab.fillna(0).astype(int)
+                        inverse["continuous"][name] = bins.tolist()
+
+            print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)")
+
+            enc_struct = enc
+            if len(enc) > struct_rows:
+                enc_struct = enc.sample(n=struct_rows, random_state=0, replace=False)
+                print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})")
+            dag = TreeSearch(enc_struct, root_node=root_node, n_jobs=n_jobs).estimate(estimator_type=estimator_type, edge_weights_fn=edge_weights_fn, show_progress=False)
+            for col in enc.columns:
+                if col not in dag.nodes():
+                    dag.add_node(col)
+                    print(f"[BayesNet] Added isolated node to DAG: {col}")
+            network = DiscreteBayesianNetwork(dag)
+            enc_fit = enc
+            if len(enc) > fit_rows:
+                enc_fit = enc.sample(n=fit_rows, random_state=1, replace=False)
+                print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})")
+            network.fit(enc_fit)
+
+            bundle = {
+                "network": network,
+                "inverse": inverse,
+                "column_order": list(enc.columns),
+                "full_column_order": full_column_order,
+                "integer_columns": list(integer_columns),
+                "original_dtypes": {c: str(df[c].dtype) for c in enc.columns},
+                "const_cols": const_cols,
+            }
+            with open("/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/bayesnet_model.pkl", "wb") as _f:
+                pickle.dump(bundle, _f)
+            print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/bayesnet_model.pkl")

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/bayesnet_coltypes.json

@@ -0,0 +1,33 @@
+{
+  "columns": [
+    {
+      "name": "buying",
+      "type": "categorical"
+    },
+    {
+      "name": "maint",
+      "type": "categorical"
+    },
+    {
+      "name": "doors",
+      "type": "categorical"
+    },
+    {
+      "name": "persons",
+      "type": "categorical"
+    },
+    {
+      "name": "lug_boot",
+      "type": "categorical"
+    },
+    {
+      "name": "safety",
+      "type": "categorical"
+    },
+    {
+      "name": "class",
+      "type": "categorical"
+    }
+  ],
+  "integer_columns": []
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c2",
+  "model": "bayesnet",
+  "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"
+    }
+  }
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/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"
+        ]
+      }
+    }
+  ]
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/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"
+  }
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/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-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/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": [],
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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": {
+        "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,
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/run_config.json

@@ -0,0 +1,46 @@
+{
+  "schema_version": 1,
+  "recorded_at": "2026-05-04T20:45:46",
+  "dataset_id": "c2",
+  "model": "bayesnet",
+  "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546",
+  "dataset_source_requested": "new",
+  "dataset_source_resolved": "new",
+  "cli_args": {
+    "model": "bayesnet",
+    "dataset": "c2",
+    "dataset_source": "new",
+    "train": true,
+    "generate": true,
+    "num_rows": 0,
+    "epochs": null,
+    "output_dir": null,
+    "model_dir": null,
+    "work_dir": null,
+    "resume": false,
+    "no_stats": false
+  },
+  "resolved": {
+    "num_rows": 1382,
+    "model_path": null,
+    "output_csv": null
+  },
+  "input_artifacts": {
+    "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/public_gate/public_gate_report.json",
+    "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/public_gate/staged_input_manifest.json",
+    "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/bayesnet/model_input_manifest.json",
+    "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/train.csv",
+    "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/staged_features.json",
+    "target_column": "class",
+    "task_type": "classification"
+  },
+  "env_overrides": {
+    "BAYESNET_EDGE_WEIGHTS_FN": "mutual_info",
+    "BAYESNET_ESTIMATOR_TYPE": "chow-liu",
+    "BAYESNET_FIT_ROWS": "2000",
+    "BAYESNET_MAX_BINS": "7",
+    "BAYESNET_MAX_CAT_LEVELS": "50",
+    "BAYESNET_N_JOBS": "1",
+    "BAYESNET_STRUCT_ROWS": "1000"
+  }
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/runtime_result.json

@@ -0,0 +1,24 @@
+{
+  "dataset_id": "c2",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c2-20260504_204546",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "fail",
+  "generate_status": "skipped",
+  "reason_code": "adapter_runtime_error",
+  "reason_detail": "Command '['docker', 'run', '--rm', '--init', '--cidfile', '/tmp/bench_docker_bayesnet_58e6fje8/container.cid', '-e', 'PYTHONNOUSERSITE=1', '-e', 'OPENBLAS_NUM_THREADS=8', '-e', 'MKL_NUM_THREADS=8', '-e', 'OMP_NUM_THREADS=8', '-e', 'BAYESNET_MAX_BINS=7', '-e', 'BAYESNET_MAX_CAT_LEVELS=50', '-e', 'BAYESNET_STRUCT_ROWS=1000', '-e', 'BAYESNET_FIT_ROWS=2000', '-e', 'BAYESNET_ESTIMATOR_TYPE=chow-liu', '-e', 'BAYESNET_EDGE_WEIGHTS_FN=mutual_info', '-e', 'BAYESNET_N_JOBS=1', '-v', '/data/jialinzhang/SynthesizePipeline-server:/work', '-w', '/work', 'benchmark:bayesnet-zjl', 'python', '/work/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/_bayesnet_train.py']' returned non-zero exit status 1.",
+  "artifacts": {},
+  "timings": {
+    "train": {
+      "started_at": "2026-05-04T20:45:46",
+      "ended_at": "2026-05-04T20:45:46",
+      "duration_sec": 0.606
+    },
+    "generate": {
+      "started_at": null,
+      "ended_at": null,
+      "duration_sec": null
+    }
+  }
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/staged/bayesnet/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/bayesnet/model_input_manifest.json"
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/staged/bayesnet/adapter_transforms_applied.json

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

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,151 @@
+{
+  "dataset_id": "c2",
+  "model": "bayesnet",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "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",
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+          "4"
+        ]
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+      "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"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/bayesnet/bayesnet-c2-20260504_204546/public_gate/public_gate_report.json"
+}

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/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
+  }
+]

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b48114a7d0bc5bd9a07920f903c8d4aba8bf98bf2a66a050da03588b0245ca73
+size 5273

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4aed00c2c2b3f88a55a7ebff31b2e1b5e0e32fb0a7267e0b9d2779cd23e434dd
+size 41565

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26e90c1170a57a14c05832ac88027722b1f3848f9662c7c09ef7c93dcba4cc01
+size 5176

syntheticFail/c2/bayesnet/bayesnet-c2-20260504_204546/train_20260504_204546.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7660ea9707027fd645d486f67322b7881fca15144309b6d68e667dad469e127
+size 1180

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/_ctgan_train.py

@@ -0,0 +1,17 @@
+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+
+data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/train.csv")
+discrete_columns = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class']
+model = CTGAN(
+    embedding_dim=16,
+    generator_dim=(32, 32),
+    discriminator_dim=(32, 32),
+    batch_size=64,
+    pac=5,
+    epochs=100,
+    verbose=True,
+)
+model.fit(data, discrete_columns)
+model.save("/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/models_100epochs/ctgan_100epochs.pt")
+print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/models_100epochs/ctgan_100epochs.pt")

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/ctgan_metadata.json

@@ -0,0 +1,32 @@
+{
+  "columns": [
+    {
+      "name": "buying",
+      "type": "categorical"
+    },
+    {
+      "name": "maint",
+      "type": "categorical"
+    },
+    {
+      "name": "doors",
+      "type": "categorical"
+    },
+    {
+      "name": "persons",
+      "type": "categorical"
+    },
+    {
+      "name": "lug_boot",
+      "type": "categorical"
+    },
+    {
+      "name": "safety",
+      "type": "categorical"
+    },
+    {
+      "name": "class",
+      "type": "categorical"
+    }
+  ]
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c2",
+  "model": "ctgan",
+  "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"
+    }
+  }
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/models_100epochs/train_20260504_152620.log

@@ -0,0 +1,3 @@
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+size 2421

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/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": [],
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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": {
+        "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,
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/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"
+  }
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/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-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
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+        "example_values": [
+          "2",
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+          "3",
+          "4"
+        ]
+      }
+    },
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+      "role": "feature",
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+        "example_values": [
+          "2",
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+        ]
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+      "role": "feature",
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+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
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+      "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": [],
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+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/run_config.json

@@ -0,0 +1,46 @@
+{
+  "schema_version": 1,
+  "recorded_at": "2026-05-04T15:26:20",
+  "dataset_id": "c2",
+  "model": "ctgan",
+  "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620",
+  "dataset_source_requested": "new",
+  "dataset_source_resolved": "new",
+  "cli_args": {
+    "model": "ctgan",
+    "dataset": "c2",
+    "dataset_source": "new",
+    "train": true,
+    "generate": true,
+    "num_rows": 0,
+    "epochs": null,
+    "output_dir": null,
+    "model_dir": null,
+    "work_dir": null,
+    "resume": false,
+    "no_stats": false
+  },
+  "resolved": {
+    "num_rows": 1382,
+    "model_path": null,
+    "output_csv": null
+  },
+  "input_artifacts": {
+    "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/public_gate/public_gate_report.json",
+    "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/public_gate/staged_input_manifest.json",
+    "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/ctgan/model_input_manifest.json",
+    "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/train.csv",
+    "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/staged_features.json",
+    "target_column": "class",
+    "task_type": "classification"
+  },
+  "env_overrides": {
+    "BENCHMARK_CTGAN_GPUS": "device=3",
+    "CTGAN_BATCH_SIZE": "64",
+    "CTGAN_DEFAULT_EPOCHS": "100",
+    "CTGAN_DISCRIMINATOR_DIMS": "32,32",
+    "CTGAN_EMBEDDING_DIM": "16",
+    "CTGAN_GENERATOR_DIMS": "32,32",
+    "CTGAN_PAC": "5"
+  }
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/runtime_result.json

@@ -0,0 +1,24 @@
+{
+  "dataset_id": "c2",
+  "model": "ctgan",
+  "run_id": "ctgan-c2-20260504_152620",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "fail",
+  "generate_status": "skipped",
+  "reason_code": "adapter_runtime_error",
+  "reason_detail": "Command '['docker', 'run', '--rm', '--init', '--cidfile', '/tmp/bench_docker_ctgan_adbgfvei/container.cid', '--gpus', 'device=3', '-e', 'OPENBLAS_NUM_THREADS=4', '-e', 'MKL_NUM_THREADS=4', '-e', 'HOME=/tmp', '-e', 'PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True', '-v', '/data/jialinzhang/SynthesizePipeline-server:/work', '-w', '/work', 'benchmark:ctgan-zjl', 'python', '/work/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/_ctgan_train.py']' returned non-zero exit status 1.",
+  "artifacts": {},
+  "timings": {
+    "train": {
+      "started_at": "2026-05-04T15:26:20",
+      "ended_at": "2026-05-04T15:26:32",
+      "duration_sec": 12.145
+    },
+    "generate": {
+      "started_at": null,
+      "ended_at": null,
+      "duration_sec": null
+    }
+  }
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/staged/ctgan/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-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/ctgan/model_input_manifest.json"
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/staged/ctgan/adapter_transforms_applied.json

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

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/staged/ctgan/model_input_manifest.json

@@ -0,0 +1,151 @@
+{
+  "dataset_id": "c2",
+  "model": "ctgan",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
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+          "med",
+          "high"
+        ]
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+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
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+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
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+        "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,
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/ctgan/ctgan-c2-20260504_152620/public_gate/public_gate_report.json"
+}

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/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
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+    "feature_name": "doors",
+    "data_type": "categorical",
+    "is_target": false
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+  {
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+    "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
+  }
+]

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/staged/public/test.csv

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+version https://git-lfs.github.com/spec/v1
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+size 5273

syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/staged/public/train.csv

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syntheticFail/c2/ctgan/ctgan-c2-20260504_152620/staged/public/val.csv

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+size 5176

syntheticFail/c2/goggle/goggle-c2-20260414_051945/_goggle_meta.json

@@ -0,0 +1 @@
+{"columns": ["buying", "maint", "doors", "persons", "lug_boot", "safety", "class"], "input_dim": 7}

syntheticFail/c2/goggle/goggle-c2-20260414_051945/_goggle_train.csv

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+version https://git-lfs.github.com/spec/v1
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syntheticFail/c2/goggle/goggle-c2-20260414_051945/_goggle_train.py

@@ -0,0 +1,16 @@
+
+import json, os, torch, pandas as pd
+os.environ["PYTHONPATH"] = "/workspace/GOGGLE/src:" + os.environ.get("PYTHONPATH", "")
+os.chdir("/work")
+from goggle.GoggleModel import GoggleModel
+with open(r"/work/output-SpecializedModels/c2/goggle/goggle-c2-20260414_051945/_goggle_meta.json") as f:
+    meta = json.load(f)
+df = pd.read_csv(r"/work/output-SpecializedModels/c2/goggle/goggle-c2-20260414_051945/_goggle_train.csv")
+m = GoggleModel(
+    "pipe", input_dim=meta["input_dim"], encoder_dim=64, decoder_dim=64,
+    encoder_l=2, decoder_l=2, decoder_arch="gcn", device="cuda",
+    epochs=1, batch_size=128, patience=min(20, 1), logging_epoch=max(1, 1//5),
+)
+m.fit(df)
+torch.save({"state_dict": m.model.state_dict(), "meta": meta}, r"/work/output-SpecializedModels/c2/goggle/goggle-c2-20260414_051945/goggle_state.pt")
+print("GOGGLE train OK")