Add syntheticSuccess c16

syntheticSuccess/c16/arf/arf-c16-20260422_055912/_arf_generate.py

@@ -0,0 +1,23 @@
+import pickle
+import pandas as pd
+
+n_target = int(5516)
+with open("/work/output-SpecializedModels/c16/arf/arf-c16-20260422_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/c16/arf/arf-c16-20260422_055912/arf-c16-5516-20260422_060318.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf-c16-5516-20260422_060318.csv")

syntheticSuccess/c16/arf/arf-c16-20260422_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/c16/arf/arf-c16-20260422_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/c16/arf/arf-c16-20260422_055912/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf_model.pkl")

syntheticSuccess/c16/arf/arf-c16-20260422_055912/arf-c16-5516-20260422_060318.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:199db55282a8be4dc780b9729b10e4be3be17fd729fb4608144d68c6d661a028
+size 1110402

syntheticSuccess/c16/arf/arf-c16-20260422_055912/arf_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34106615a87b437842d60a6f436b17b7bb682cd1ac8fde05fa1c5d29685d6d68
+size 53958752

syntheticSuccess/c16/arf/arf-c16-20260422_055912/gen_20260422_060318.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2704850d30351b272f1c4f16a12cd8a4bd4815c851dca6414125f8476b47b5a4
+size 3618

syntheticSuccess/c16/arf/arf-c16-20260422_055912/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c16",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+      "exists": true,
+      "size": 889767,
+      "sha256": "d87fe8c15e5364335255aabe0e5ac068dc98c8c772bcbbc52861739ec34e0914"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+      "exists": true,
+      "size": 111085,
+      "sha256": "149f25d0314c83ff898ddfd9550fd9b048af51daa289673d6bb491653dd89d83"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv",
+      "exists": true,
+      "size": 111822,
+      "sha256": "bf819d88a0bc2a2659f0a25aacfe0d15ca1b9d59b498ece178817ba81f76d3bf"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_profile.json",
+      "exists": true,
+      "size": 6130,
+      "sha256": "a01e7504e986616f132cc5da119064b3fe1a68c4b0475fe60628cdb608071157"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7074,
+      "sha256": "773f9641fef4054eef8038ec0bd570c990be631ca4c9748324249d2c92645ba6"
+    }
+  }
+}

syntheticSuccess/c16/arf/arf-c16-20260422_055912/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,270 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c16/arf/arf-c16-20260422_055912/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c16",
+  "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": "EYE",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv"
+  }
+}

syntheticSuccess/c16/arf/arf-c16-20260422_055912/public_gate/staged_input_manifest.json

@@ -0,0 +1,275 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c16/arf/arf-c16-20260422_055912/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c16",
+  "model": "arf",
+  "run_id": "arf-c16-20260422_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/c16/arf/arf-c16-20260422_055912/arf-c16-5516-20260422_060318.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/arf_model.pkl"
+  }
+}

syntheticSuccess/c16/arf/arf-c16-20260422_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/c16/arf/arf-c16-20260422_055912/staged/arf/model_input_manifest.json"
+}

syntheticSuccess/c16/arf/arf-c16-20260422_055912/staged/arf/adapter_transforms_applied.json

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

syntheticSuccess/c16/arf/arf-c16-20260422_055912/staged/arf/model_input_manifest.json

@@ -0,0 +1,277 @@
+{
+  "dataset_id": "c16",
+  "model": "arf",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/arf/arf-c16-20260422_055912/public_gate/public_gate_report.json"
+}

syntheticSuccess/c16/arf/arf-c16-20260422_055912/staged/public/staged_features.json

@@ -0,0 +1,67 @@
+[
+  {
+    "feature_name": "page_id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "name",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "urlslug",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "ID",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ALIGN",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "EYE",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "HAIR",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "SEX",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "GSM",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ALIVE",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "APPEARANCES",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "FIRST APPEARANCE",
+    "data_type": "timestamp",
+    "is_target": false
+  },
+  {
+    "feature_name": "YEAR",
+    "data_type": "continuous",
+    "is_target": false
+  }
+]

syntheticSuccess/c16/arf/arf-c16-20260422_055912/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:507d848740a1690656f8cdb841697266450f944c4a567ba0bcf3186a4e58b754
+size 113806

syntheticSuccess/c16/arf/arf-c16-20260422_055912/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be0950091b8a4c774fc54ff9926cf4e8b86b23efd5ec8e83b5592c72658ae351
+size 905642

syntheticSuccess/c16/arf/arf-c16-20260422_055912/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5a1815f0bdd0db2721a9d7bc2f1f0008829eaf9ff3d07e3d1975b42002cf744
+size 113063

syntheticSuccess/c16/arf/arf-c16-20260422_055912/train_20260422_055912.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa114adf9991d707fbb85ab1ec530eb84e31eba54504d6b42dd0578ff9cffea8
+size 350

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/_bayesnet_generate.py

@@ -0,0 +1,75 @@
+
+import pickle
+import warnings
+
+import numpy as np
+import pandas as pd
+from pgmpy.sampling import BayesianModelSampling
+
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+with open("/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl", "rb") as f:
+    bundle = pickle.load(f)
+
+network = bundle["network"]
+inverse = bundle["inverse"]
+cols = bundle["column_order"]
+integer_columns = set(bundle.get("integer_columns") or [])
+full_order = bundle.get("full_column_order") or cols
+const_cols = bundle.get("const_cols") or {}
+
+sampler = BayesianModelSampling(network)
+raw = sampler.forward_sample(size=5516, show_progress=False)
+
+out = pd.DataFrame(index=raw.index)
+rng = np.random.default_rng()
+
+for c in cols:
+    if c in inverse["categorical"]:
+        levels = inverse["categorical"][c]
+        idx = raw[c].astype(int).to_numpy()
+        idx = np.clip(idx, 0, max(0, len(levels) - 1))
+        out[c] = [levels[i] for i in idx]
+    else:
+        edges = np.asarray(inverse["continuous"][c], dtype=float)
+        if edges.size < 2:
+            out[c] = 0.0
+        else:
+            nbin = edges.size - 1
+            res = []
+            for k in raw[c].astype(int).to_numpy():
+                k = int(k)
+                if k < 0:
+                    k = 0
+                if k >= nbin:
+                    k = nbin - 1
+                lo, hi = float(edges[k]), float(edges[k + 1])
+                if hi < lo:
+                    lo, hi = hi, lo
+                v = rng.uniform(lo, hi)
+                if c in integer_columns:
+                    v = int(round(v))
+                res.append(v)
+            out[c] = res
+
+final = pd.DataFrame(index=out.index)
+for c in full_order:
+    if c in const_cols:
+        final[c] = const_cols[c]
+    elif c in out.columns:
+        final[c] = out[c]
+
+dtypes = bundle.get("original_dtypes") or {}
+for c, dts in dtypes.items():
+    if c not in final.columns:
+        continue
+    try:
+        if "int" in dts:
+            final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64")
+        elif "float" in dts:
+            final[c] = pd.to_numeric(final[c], errors="coerce")
+    except Exception:
+        pass
+
+final.to_csv("/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet-c16-5516-20260419_073509.csv", index=False)
+print(f"[BayesNet] Generated 5516 rows -> /work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet-c16-5516-20260419_073509.csv")

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/_bayesnet_train.py

@@ -0,0 +1,93 @@
+
+import json
+import pickle
+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)
+
+with open("/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_coltypes.json", "r", encoding="utf-8") as _f:
+    colmeta = json.load(_f)
+integer_columns = set(colmeta.get("integer_columns") or [])
+
+df = pd.read_csv("/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+full_column_order = list(df.columns)
+
+const_cols = {}
+for col in list(df.columns):
+    if df[col].nunique(dropna=True) <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}'")
+
+const_path = "/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/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)
+max_bins = 10
+
+for entry in colmeta["columns"]:
+    name = entry["name"]
+    if name not in df.columns:
+        continue
+    kind = entry["type"]
+    s = df[name]
+    if kind == "categorical":
+        uniques = sorted(s.dropna().unique(), key=lambda x: str(x))
+        mapping = {str(v): i for i, v in enumerate(uniques)}
+        inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))]
+        enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int)
+    else:
+        s_num = pd.to_numeric(s, errors="coerce")
+        nu = int(s_num.nunique(dropna=True))
+        q = min(max_bins, max(2, nu))
+        if nu < 2:
+            enc[name] = np.zeros(len(s_num), dtype=int)
+            lo, hi = float(s_num.min()), float(s_num.max())
+            inverse["continuous"][name] = [lo, hi]
+        else:
+            try:
+                _, bins = pd.qcut(
+                    s_num, q=q, retbins=True, duplicates="drop"
+                )
+            except Exception:
+                med = float(s_num.median())
+                s2 = s_num.fillna(med)
+                _, bins = pd.qcut(
+                    s2, q=min(q, 3), retbins=True, duplicates="drop"
+                )
+            bins = np.asarray(bins, dtype=float)
+            lab = pd.cut(
+                s_num, bins=bins, labels=False, include_lowest=True
+            )
+            enc[name] = lab.fillna(0).astype(int)
+            inverse["continuous"][name] = bins.tolist()
+
+print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)")
+
+dag = TreeSearch(enc).estimate(show_progress=False)
+for col in enc.columns:
+    if col not in dag.nodes():
+        dag.add_node(col)
+        print(f"[BayesNet] Added isolated node to DAG: {col}")
+network = DiscreteBayesianNetwork(dag)
+network.fit(enc)
+
+bundle = {
+    "network": network,
+    "inverse": inverse,
+    "column_order": list(enc.columns),
+    "full_column_order": full_column_order,
+    "integer_columns": list(integer_columns),
+    "original_dtypes": {c: str(df[c].dtype) for c in enc.columns},
+    "const_cols": const_cols,
+}
+with open("/work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl")

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet-c16-5516-20260419_073509.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef60ec341b4f1f4b4073369e4745b93bcdc99eca4aaa7acf23a9270bffb42db6
+size 1303655

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_coltypes.json

@@ -0,0 +1,57 @@
+{
+  "columns": [
+    {
+      "name": "page_id",
+      "type": "continuous"
+    },
+    {
+      "name": "name",
+      "type": "categorical"
+    },
+    {
+      "name": "urlslug",
+      "type": "categorical"
+    },
+    {
+      "name": "ID",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIGN",
+      "type": "categorical"
+    },
+    {
+      "name": "EYE",
+      "type": "categorical"
+    },
+    {
+      "name": "HAIR",
+      "type": "categorical"
+    },
+    {
+      "name": "SEX",
+      "type": "categorical"
+    },
+    {
+      "name": "GSM",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIVE",
+      "type": "categorical"
+    },
+    {
+      "name": "APPEARANCES",
+      "type": "continuous"
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "type": "categorical"
+    },
+    {
+      "name": "YEAR",
+      "type": "continuous"
+    }
+  ],
+  "integer_columns": []
+}

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ba641c1a165239c68e11518767ae62c1bcf63a77f56e72d09fa841b2f2fa1c1
+size 281702937

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/const_cols.json

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

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/gen_20260419_073509.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2563598d44ba90b36d99ad9202a36f9d7fd441a3ae55e3b250cdd718d81c3d3
+size 1137

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c16",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+      "exists": true,
+      "size": 889767,
+      "sha256": "d87fe8c15e5364335255aabe0e5ac068dc98c8c772bcbbc52861739ec34e0914"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+      "exists": true,
+      "size": 111085,
+      "sha256": "149f25d0314c83ff898ddfd9550fd9b048af51daa289673d6bb491653dd89d83"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv",
+      "exists": true,
+      "size": 111822,
+      "sha256": "bf819d88a0bc2a2659f0a25aacfe0d15ca1b9d59b498ece178817ba81f76d3bf"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_profile.json",
+      "exists": true,
+      "size": 6130,
+      "sha256": "a01e7504e986616f132cc5da119064b3fe1a68c4b0475fe60628cdb608071157"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7074,
+      "sha256": "773f9641fef4054eef8038ec0bd570c990be631ca4c9748324249d2c92645ba6"
+    }
+  }
+}

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,270 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c16",
+  "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": "EYE",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv"
+  }
+}

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/staged_input_manifest.json

@@ -0,0 +1,275 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c16",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c16-20260419_073440",
+  "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/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet-c16-5516-20260419_073509.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/bayesnet_model.pkl"
+  }
+}

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/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-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/bayesnet/model_input_manifest.json"
+}

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/staged/bayesnet/adapter_transforms_applied.json

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

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,277 @@
+{
+  "dataset_id": "c16",
+  "model": "bayesnet",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/bayesnet/bayesnet-c16-20260419_073440/public_gate/public_gate_report.json"
+}

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/staged_features.json

@@ -0,0 +1,67 @@
+[
+  {
+    "feature_name": "page_id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "name",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "urlslug",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "ID",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ALIGN",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "EYE",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "HAIR",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "SEX",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "GSM",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ALIVE",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "APPEARANCES",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "FIRST APPEARANCE",
+    "data_type": "timestamp",
+    "is_target": false
+  },
+  {
+    "feature_name": "YEAR",
+    "data_type": "continuous",
+    "is_target": false
+  }
+]

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:507d848740a1690656f8cdb841697266450f944c4a567ba0bcf3186a4e58b754
+size 113806

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be0950091b8a4c774fc54ff9926cf4e8b86b23efd5ec8e83b5592c72658ae351
+size 905642

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5a1815f0bdd0db2721a9d7bc2f1f0008829eaf9ff3d07e3d1975b42002cf744
+size 113063

syntheticSuccess/c16/bayesnet/bayesnet-c16-20260419_073440/train_20260419_073440.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4da056a6f5de72e3eb3b1677ce95a0dc0e4ec00aa202800eb767c1552574df88
+size 1162

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/_ctgan_generate.py

@@ -0,0 +1,18 @@
+import sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
+apply_ctgan_inverse_fix()
+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+model = CTGAN.load("/work/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/models_300epochs/ctgan_300epochs.pt")
+total = 5516
+chunk = min(50000, total) if total > 50000 else total
+parts = []
+left = total
+while left > 0:
+    take = min(chunk, left)
+    parts.append(model.sample(take))
+    left -= take
+sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
+sampled.to_csv("/work/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/ctgan-c16-5516-20260422_031613.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/ctgan-c16-5516-20260422_031613.csv")

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/ctgan-c16-5516-20260422_031613.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b49d7ff9d2eb65cd86a17cd70dd4cb73d66fc543d99cd77dc34607a90a96936b
+size 1052947

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/ctgan_metadata.json

@@ -0,0 +1,56 @@
+{
+  "columns": [
+    {
+      "name": "page_id",
+      "type": "continuous"
+    },
+    {
+      "name": "name",
+      "type": "categorical"
+    },
+    {
+      "name": "urlslug",
+      "type": "categorical"
+    },
+    {
+      "name": "ID",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIGN",
+      "type": "categorical"
+    },
+    {
+      "name": "EYE",
+      "type": "categorical"
+    },
+    {
+      "name": "HAIR",
+      "type": "categorical"
+    },
+    {
+      "name": "SEX",
+      "type": "categorical"
+    },
+    {
+      "name": "GSM",
+      "type": "categorical"
+    },
+    {
+      "name": "ALIVE",
+      "type": "categorical"
+    },
+    {
+      "name": "APPEARANCES",
+      "type": "continuous"
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "type": "categorical"
+    },
+    {
+      "name": "YEAR",
+      "type": "continuous"
+    }
+  ]
+}

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/ctgan_train_continuous_imputed.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:284f6938dfb09183455b3ea6d7545f05a936e78771da9910a5115d82c963e4c2
+size 906809

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/gen_20260422_031613.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26cb86a158ac285099021c141f3826f6bec118513c8041eb48c461d0b7fa0136
+size 295

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c16",
+  "model": "ctgan",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+      "exists": true,
+      "size": 889767,
+      "sha256": "d87fe8c15e5364335255aabe0e5ac068dc98c8c772bcbbc52861739ec34e0914"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+      "exists": true,
+      "size": 111085,
+      "sha256": "149f25d0314c83ff898ddfd9550fd9b048af51daa289673d6bb491653dd89d83"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv",
+      "exists": true,
+      "size": 111822,
+      "sha256": "bf819d88a0bc2a2659f0a25aacfe0d15ca1b9d59b498ece178817ba81f76d3bf"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_profile.json",
+      "exists": true,
+      "size": 6130,
+      "sha256": "a01e7504e986616f132cc5da119064b3fe1a68c4b0475fe60628cdb608071157"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c16/c16-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7074,
+      "sha256": "773f9641fef4054eef8038ec0bd570c990be631ca4c9748324249d2c92645ba6"
+    }
+  }
+}

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/models_300epochs/ctgan_300epochs.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0040aba42f44d1b56b6e55623648457f892357e596cf5790d4477fcd46b81204
+size 621310691

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/models_300epochs/train_20260422_025942.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:471c41240ff424ce9a3284adb6f6d6fe481f979c5eb0c824f85360afd2e559fb
+size 2472

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,270 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c16",
+  "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": "EYE",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c16/c16-test.csv"
+  }
+}

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/public_gate/staged_input_manifest.json

@@ -0,0 +1,275 @@
+{
+  "dataset_id": "c16",
+  "target_column": "EYE",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "page_id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "1941",
+          "127435",
+          "268584",
+          "144619",
+          "132754"
+        ]
+      }
+    },
+    {
+      "name": "name",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "Jeremy Tell (New Earth)",
+          "Thomas Jarred (New Earth)",
+          "Kusanagi (New Earth)",
+          "Cecile O'Malley (New Earth)",
+          "Rori Stroh (New Earth)"
+        ]
+      }
+    },
+    {
+      "name": "urlslug",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5516,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "\\/wiki\\/Jeremy_Tell_(New_Earth)",
+          "\\/wiki\\/Thomas_Jarred_(New_Earth)",
+          "\\/wiki\\/Kusanagi_(New_Earth)",
+          "\\/wiki\\/Cecile_O%27Malley_(New_Earth)",
+          "\\/wiki\\/Rori_Stroh_(New_Earth)"
+        ]
+      }
+    },
+    {
+      "name": "ID",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.292422,
+        "unique_count": 3,
+        "unique_ratio": 0.000769,
+        "example_values": [
+          "Public Identity",
+          "Secret Identity",
+          "Identity Unknown"
+        ]
+      }
+    },
+    {
+      "name": "ALIGN",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.087563,
+        "unique_count": 4,
+        "unique_ratio": 0.000795,
+        "example_values": [
+          "Bad Characters",
+          "Good Characters",
+          "Neutral Characters",
+          "Reformed Criminals"
+        ]
+      }
+    },
+    {
+      "name": "EYE",
+      "role": "target",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.525381,
+        "unique_count": 17,
+        "unique_ratio": 0.006494,
+        "example_values": [
+          "Black Eyes",
+          "Blue Eyes",
+          "Grey Eyes",
+          "Green Eyes",
+          "Brown Eyes"
+        ]
+      }
+    },
+    {
+      "name": "HAIR",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.3314,
+        "unique_count": 17,
+        "unique_ratio": 0.00461,
+        "example_values": [
+          "Brown Hair",
+          "Grey Hair",
+          "Red Hair",
+          "Black Hair",
+          "White Hair"
+        ]
+      }
+    },
+    {
+      "name": "SEX",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.018673,
+        "unique_count": 4,
+        "unique_ratio": 0.000739,
+        "example_values": [
+          "Male Characters",
+          "Female Characters",
+          "Genderless Characters",
+          "Transgender Characters"
+        ]
+      }
+    },
+    {
+      "name": "GSM",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.990392,
+        "unique_count": 2,
+        "unique_ratio": 0.037736,
+        "example_values": [
+          "Homosexual Characters",
+          "Bisexual Characters"
+        ]
+      }
+    },
+    {
+      "name": "ALIVE",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000544,
+        "unique_count": 2,
+        "unique_ratio": 0.000363,
+        "example_values": [
+          "Living Characters",
+          "Deceased Characters"
+        ]
+      }
+    },
+    {
+      "name": "APPEARANCES",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.051305,
+        "unique_count": 263,
+        "unique_ratio": 0.050258,
+        "example_values": [
+          "14",
+          "3",
+          "4",
+          "7",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "FIRST APPEARANCE",
+      "role": "feature",
+      "semantic_type": "datetime",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": "%Y-%m-%d",
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 758,
+        "unique_ratio": 0.138752,
+        "example_values": [
+          "2001, August",
+          "1990, February",
+          "2008, July",
+          "1984, April",
+          "1961, December"
+        ]
+      }
+    },
+    {
+      "name": "YEAR",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.009608,
+        "unique_count": 79,
+        "unique_ratio": 0.014461,
+        "example_values": [
+          "2001",
+          "1990",
+          "2008",
+          "1984",
+          "1961"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c16/ctgan/ctgan-c16-20260422_025941/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c16",
+  "model": "ctgan",
+  "run_id": "ctgan-c16-20260422_025941",
+  "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/c16/ctgan/ctgan-c16-20260422_025941/ctgan-c16-5516-20260422_031613.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c16/ctgan/ctgan-c16-20260422_025941/models_300epochs/ctgan_300epochs.pt"
+  }
+}