Add syntheticSuccess m7

syntheticSuccess/m7/arf/arf-m7-20260422_055912/_arf_generate.py

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+import pickle
+import pandas as pd
+
+n_target = int(4088)
+with open("/work/output-SpecializedModels/m7/arf/arf-m7-20260422_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/m7/arf/arf-m7-20260422_055912/arf-m7-4088-20260422_060013.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/arf-m7-4088-20260422_060013.csv")

syntheticSuccess/m7/arf/arf-m7-20260422_055912/_arf_train.py

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+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/m7/arf/arf-m7-20260422_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/m7/arf/arf-m7-20260422_055912/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/arf_model.pkl")

syntheticSuccess/m7/arf/arf-m7-20260422_055912/arf-m7-4088-20260422_060013.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5bef5cb3ca3d1d6f827e38f841a31518bd6c09c5318d903c10a6861e9fa08a61
+size 546352

syntheticSuccess/m7/arf/arf-m7-20260422_055912/arf_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ead2f35ac0c73d397295a8a4a7080b4a4ebf9a4b58fe5b777d13930a91c4dd9a
+size 18331589

syntheticSuccess/m7/arf/arf-m7-20260422_055912/gen_20260422_060013.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbfbdef036085ce71ada368ffab278301c6d149a4f14a6ae26bc81f51e782c6a
+size 2035

syntheticSuccess/m7/arf/arf-m7-20260422_055912/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "m7",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-train.csv",
+      "exists": true,
+      "size": 257258,
+      "sha256": "6458faf5495150c1433a95be37eb8d9605954f8474a3d40d9ab9d10adc4cf71e"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-val.csv",
+      "exists": true,
+      "size": 32142,
+      "sha256": "bb8674674733508f2c6940b78c07c601654827604862334f9b267e50a83ed9f7"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-test.csv",
+      "exists": true,
+      "size": 32328,
+      "sha256": "8b32eb82bd281650cd9f236ef26ec9cd6f02dbaba740770f0e3c4b94a189f3e7"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m7/m7-dataset_profile.json",
+      "exists": true,
+      "size": 5020,
+      "sha256": "ab77a37aa06b0bb854623b7f0caab0c4d4ed05cfce61d9aaf0320c279b20fae5"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m7/m7-dataset_contract_v1.json",
+      "exists": true,
+      "size": 5995,
+      "sha256": "aa9654fbc1dc4029d109adf4117767c5a5c8e90b53bd3f9c698d7b71345cf5b3"
+    }
+  }
+}

syntheticSuccess/m7/arf/arf-m7-20260422_055912/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,242 @@
+{
+  "dataset_id": "m7",
+  "target_column": "Residence_type",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4088,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "35296",
+          "36710",
+          "32733",
+          "1077",
+          "13223"
+        ]
+      }
+    },
+    {
+      "name": "gender",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Female",
+          "Male"
+        ]
+      }
+    },
+    {
+      "name": "age",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 104,
+        "unique_ratio": 0.02544,
+        "example_values": [
+          "58",
+          "64",
+          "28",
+          "77",
+          "53"
+        ]
+      }
+    },
+    {
+      "name": "hypertension",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "heart_disease",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "ever_married",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Yes",
+          "No"
+        ]
+      }
+    },
+    {
+      "name": "work_type",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.001223,
+        "example_values": [
+          "Private",
+          "Govt_job",
+          "Self-employed",
+          "children",
+          "Never_worked"
+        ]
+      }
+    },
+    {
+      "name": "Residence_type",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Rural",
+          "Urban"
+        ]
+      }
+    },
+    {
+      "name": "avg_glucose_level",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3337,
+        "unique_ratio": 0.816292,
+        "example_values": [
+          "100.42",
+          "62.21",
+          "106.68",
+          "106.03",
+          "86.39"
+        ]
+      }
+    },
+    {
+      "name": "bmi",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [
+        "N/A"
+      ],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.039873,
+        "unique_count": 396,
+        "unique_ratio": 0.100892,
+        "example_values": [
+          "39.5",
+          "28.3",
+          "29.3",
+          "30.2",
+          "48.9"
+        ]
+      }
+    },
+    {
+      "name": "smoking_status",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000978,
+        "example_values": [
+          "smokes",
+          "Unknown",
+          "never smoked",
+          "formerly smoked"
+        ]
+      }
+    },
+    {
+      "name": "stroke",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/m7/arf/arf-m7-20260422_055912/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "m7",
+  "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": "Residence_type",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-test.csv"
+  }
+}

syntheticSuccess/m7/arf/arf-m7-20260422_055912/public_gate/staged_input_manifest.json

@@ -0,0 +1,247 @@
+{
+  "dataset_id": "m7",
+  "target_column": "Residence_type",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4088,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "35296",
+          "36710",
+          "32733",
+          "1077",
+          "13223"
+        ]
+      }
+    },
+    {
+      "name": "gender",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Female",
+          "Male"
+        ]
+      }
+    },
+    {
+      "name": "age",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 104,
+        "unique_ratio": 0.02544,
+        "example_values": [
+          "58",
+          "64",
+          "28",
+          "77",
+          "53"
+        ]
+      }
+    },
+    {
+      "name": "hypertension",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "heart_disease",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "ever_married",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Yes",
+          "No"
+        ]
+      }
+    },
+    {
+      "name": "work_type",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.001223,
+        "example_values": [
+          "Private",
+          "Govt_job",
+          "Self-employed",
+          "children",
+          "Never_worked"
+        ]
+      }
+    },
+    {
+      "name": "Residence_type",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Rural",
+          "Urban"
+        ]
+      }
+    },
+    {
+      "name": "avg_glucose_level",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3337,
+        "unique_ratio": 0.816292,
+        "example_values": [
+          "100.42",
+          "62.21",
+          "106.68",
+          "106.03",
+          "86.39"
+        ]
+      }
+    },
+    {
+      "name": "bmi",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [
+        "N/A"
+      ],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.039873,
+        "unique_count": 396,
+        "unique_ratio": 0.100892,
+        "example_values": [
+          "39.5",
+          "28.3",
+          "29.3",
+          "30.2",
+          "48.9"
+        ]
+      }
+    },
+    {
+      "name": "smoking_status",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000978,
+        "example_values": [
+          "smokes",
+          "Unknown",
+          "never smoked",
+          "formerly smoked"
+        ]
+      }
+    },
+    {
+      "name": "stroke",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/m7/arf/arf-m7-20260422_055912/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "m7",
+  "model": "arf",
+  "run_id": "arf-m7-20260422_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/m7/arf/arf-m7-20260422_055912/arf-m7-4088-20260422_060013.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/arf_model.pkl"
+  }
+}

syntheticSuccess/m7/arf/arf-m7-20260422_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/m7/arf/arf-m7-20260422_055912/staged/arf/model_input_manifest.json"
+}

syntheticSuccess/m7/arf/arf-m7-20260422_055912/staged/arf/adapter_transforms_applied.json

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

syntheticSuccess/m7/arf/arf-m7-20260422_055912/staged/arf/model_input_manifest.json

@@ -0,0 +1,249 @@
+{
+  "dataset_id": "m7",
+  "model": "arf",
+  "target_column": "Residence_type",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "id",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4088,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "35296",
+          "36710",
+          "32733",
+          "1077",
+          "13223"
+        ]
+      }
+    },
+    {
+      "name": "gender",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Female",
+          "Male"
+        ]
+      }
+    },
+    {
+      "name": "age",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 104,
+        "unique_ratio": 0.02544,
+        "example_values": [
+          "58",
+          "64",
+          "28",
+          "77",
+          "53"
+        ]
+      }
+    },
+    {
+      "name": "hypertension",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "heart_disease",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    },
+    {
+      "name": "ever_married",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Yes",
+          "No"
+        ]
+      }
+    },
+    {
+      "name": "work_type",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.001223,
+        "example_values": [
+          "Private",
+          "Govt_job",
+          "Self-employed",
+          "children",
+          "Never_worked"
+        ]
+      }
+    },
+    {
+      "name": "Residence_type",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Rural",
+          "Urban"
+        ]
+      }
+    },
+    {
+      "name": "avg_glucose_level",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3337,
+        "unique_ratio": 0.816292,
+        "example_values": [
+          "100.42",
+          "62.21",
+          "106.68",
+          "106.03",
+          "86.39"
+        ]
+      }
+    },
+    {
+      "name": "bmi",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [
+        "N/A"
+      ],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.039873,
+        "unique_count": 396,
+        "unique_ratio": 0.100892,
+        "example_values": [
+          "39.5",
+          "28.3",
+          "29.3",
+          "30.2",
+          "48.9"
+        ]
+      }
+    },
+    {
+      "name": "smoking_status",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000978,
+        "example_values": [
+          "smokes",
+          "Unknown",
+          "never smoked",
+          "formerly smoked"
+        ]
+      }
+    },
+    {
+      "name": "stroke",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/arf/arf-m7-20260422_055912/public_gate/public_gate_report.json"
+}

syntheticSuccess/m7/arf/arf-m7-20260422_055912/staged/public/staged_features.json

@@ -0,0 +1,62 @@
+[
+  {
+    "feature_name": "id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "gender",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "age",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "hypertension",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "heart_disease",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "ever_married",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "work_type",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "Residence_type",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "avg_glucose_level",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bmi",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "smoking_status",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "stroke",
+    "data_type": "binary",
+    "is_target": false
+  }
+]

syntheticSuccess/m7/arf/arf-m7-20260422_055912/staged/public/test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed44e06410b9395c5c85e238168df0c6952d61bdfe49424863c651ef2b1f4bd8
+size 32934

syntheticSuccess/m7/arf/arf-m7-20260422_055912/staged/public/train.csv

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+size 262059

syntheticSuccess/m7/arf/arf-m7-20260422_055912/staged/public/val.csv

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+version https://git-lfs.github.com/spec/v1
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+size 32704

syntheticSuccess/m7/arf/arf-m7-20260422_055912/train_20260422_055912.log

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+version https://git-lfs.github.com/spec/v1
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+size 232

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/_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/m7/bayesnet/bayesnet-m7-20260420_035052/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=4088, 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/m7/bayesnet/bayesnet-m7-20260420_035052/bayesnet-m7-4088-20260420_035257.csv", index=False)
+print(f"[BayesNet] Generated 4088 rows -> /work/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/bayesnet-m7-4088-20260420_035257.csv")

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/_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/m7/bayesnet/bayesnet-m7-20260420_035052/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/m7/bayesnet/bayesnet-m7-20260420_035052/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/m7/bayesnet/bayesnet-m7-20260420_035052/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/m7/bayesnet/bayesnet-m7-20260420_035052/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/bayesnet_model.pkl")

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/bayesnet-m7-4088-20260420_035257.csv

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+size 471242

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/bayesnet_coltypes.json

@@ -0,0 +1,53 @@
+{
+  "columns": [
+    {
+      "name": "id",
+      "type": "continuous"
+    },
+    {
+      "name": "gender",
+      "type": "categorical"
+    },
+    {
+      "name": "age",
+      "type": "continuous"
+    },
+    {
+      "name": "hypertension",
+      "type": "categorical"
+    },
+    {
+      "name": "heart_disease",
+      "type": "categorical"
+    },
+    {
+      "name": "ever_married",
+      "type": "categorical"
+    },
+    {
+      "name": "work_type",
+      "type": "categorical"
+    },
+    {
+      "name": "Residence_type",
+      "type": "categorical"
+    },
+    {
+      "name": "avg_glucose_level",
+      "type": "continuous"
+    },
+    {
+      "name": "bmi",
+      "type": "continuous"
+    },
+    {
+      "name": "smoking_status",
+      "type": "categorical"
+    },
+    {
+      "name": "stroke",
+      "type": "categorical"
+    }
+  ],
+  "integer_columns": []
+}

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/bayesnet_model.pkl

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+version https://git-lfs.github.com/spec/v1
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+size 11519

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/const_cols.json

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+{}

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/gen_20260420_035257.log

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+version https://git-lfs.github.com/spec/v1
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syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/input_snapshot.json

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+{
+  "dataset_id": "m7",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-train.csv",
+      "exists": true,
+      "size": 257258,
+      "sha256": "6458faf5495150c1433a95be37eb8d9605954f8474a3d40d9ab9d10adc4cf71e"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-val.csv",
+      "exists": true,
+      "size": 32142,
+      "sha256": "bb8674674733508f2c6940b78c07c601654827604862334f9b267e50a83ed9f7"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-test.csv",
+      "exists": true,
+      "size": 32328,
+      "sha256": "8b32eb82bd281650cd9f236ef26ec9cd6f02dbaba740770f0e3c4b94a189f3e7"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m7/m7-dataset_profile.json",
+      "exists": true,
+      "size": 5020,
+      "sha256": "ab77a37aa06b0bb854623b7f0caab0c4d4ed05cfce61d9aaf0320c279b20fae5"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m7/m7-dataset_contract_v1.json",
+      "exists": true,
+      "size": 5995,
+      "sha256": "aa9654fbc1dc4029d109adf4117767c5a5c8e90b53bd3f9c698d7b71345cf5b3"
+    }
+  }
+}

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,242 @@
+{
+  "dataset_id": "m7",
+  "target_column": "Residence_type",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "id",
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+}

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
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+  "status": "pass",
+  "checks": [
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+      "status": "pass"
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+    {
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+      "status": "pass"
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+      "status": "pass"
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+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
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+    {
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+      "status": "pass"
+    }
+  ],
+  "target_column": "Residence_type",
+  "task_type": "classification",
+  "input_splits": {
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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-test.csv"
+  }
+}

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/public_gate/staged_input_manifest.json

@@ -0,0 +1,247 @@
+{
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+  "target_column": "Residence_type",
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syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "m7",
+  "model": "bayesnet",
+  "run_id": "bayesnet-m7-20260420_035052",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "success",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
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+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/bayesnet_model.pkl"
+  }
+}

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/staged/bayesnet/adapter_report.json

@@ -0,0 +1,7 @@
+{
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+  "adapter_transforms_applied": [],
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+}

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/staged/bayesnet/adapter_transforms_applied.json

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

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,249 @@
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+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/bayesnet/bayesnet-m7-20260420_035052/public_gate/public_gate_report.json"
+}

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/staged/public/staged_features.json

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+[
+  {
+    "feature_name": "id",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "gender",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "age",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "hypertension",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "heart_disease",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "ever_married",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "work_type",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "Residence_type",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "avg_glucose_level",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "bmi",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "smoking_status",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "stroke",
+    "data_type": "binary",
+    "is_target": false
+  }
+]

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/staged/public/test.csv

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+version https://git-lfs.github.com/spec/v1
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+size 32934

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/staged/public/train.csv

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+version https://git-lfs.github.com/spec/v1
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+size 262059

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/staged/public/val.csv

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+size 32704

syntheticSuccess/m7/bayesnet/bayesnet-m7-20260420_035052/train_20260420_035053.log

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+version https://git-lfs.github.com/spec/v1
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+size 1160

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/_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/m7/ctgan/ctgan-m7-20260422_031259/models_300epochs/ctgan_300epochs.pt")
+total = 4088
+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/m7/ctgan/ctgan-m7-20260422_031259/ctgan-m7-4088-20260422_031701.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/m7/ctgan/ctgan-m7-20260422_031259/ctgan-m7-4088-20260422_031701.csv")

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/ctgan-m7-4088-20260422_031701.csv

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+version https://git-lfs.github.com/spec/v1
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+size 427328

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/ctgan_metadata.json

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+{
+  "columns": [
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+      "name": "id",
+      "type": "continuous"
+    },
+    {
+      "name": "gender",
+      "type": "categorical"
+    },
+    {
+      "name": "age",
+      "type": "continuous"
+    },
+    {
+      "name": "hypertension",
+      "type": "categorical"
+    },
+    {
+      "name": "heart_disease",
+      "type": "categorical"
+    },
+    {
+      "name": "ever_married",
+      "type": "categorical"
+    },
+    {
+      "name": "work_type",
+      "type": "categorical"
+    },
+    {
+      "name": "Residence_type",
+      "type": "categorical"
+    },
+    {
+      "name": "avg_glucose_level",
+      "type": "continuous"
+    },
+    {
+      "name": "bmi",
+      "type": "continuous"
+    },
+    {
+      "name": "smoking_status",
+      "type": "categorical"
+    },
+    {
+      "name": "stroke",
+      "type": "categorical"
+    }
+  ]
+}

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/ctgan_train_continuous_imputed.csv

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+version https://git-lfs.github.com/spec/v1
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+size 262711

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/gen_20260422_031701.log

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+size 292

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/input_snapshot.json

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+{
+  "dataset_id": "m7",
+  "model": "ctgan",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-train.csv",
+      "exists": true,
+      "size": 257258,
+      "sha256": "6458faf5495150c1433a95be37eb8d9605954f8474a3d40d9ab9d10adc4cf71e"
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+      "exists": true,
+      "size": 32142,
+      "sha256": "bb8674674733508f2c6940b78c07c601654827604862334f9b267e50a83ed9f7"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-test.csv",
+      "exists": true,
+      "size": 32328,
+      "sha256": "8b32eb82bd281650cd9f236ef26ec9cd6f02dbaba740770f0e3c4b94a189f3e7"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m7/m7-dataset_profile.json",
+      "exists": true,
+      "size": 5020,
+      "sha256": "ab77a37aa06b0bb854623b7f0caab0c4d4ed05cfce61d9aaf0320c279b20fae5"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m7/m7-dataset_contract_v1.json",
+      "exists": true,
+      "size": 5995,
+      "sha256": "aa9654fbc1dc4029d109adf4117767c5a5c8e90b53bd3f9c698d7b71345cf5b3"
+    }
+  }
+}

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/models_300epochs/ctgan_300epochs.pt

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+size 1365155

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+{
+  "dataset_id": "m7",
+  "target_column": "Residence_type",
+  "task_type": "classification",
+  "columns": [
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+      "role": "feature",
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+          "30.2",
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+    },
+    {
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+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "keep_raw",
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+        "unique_ratio": 0.000978,
+        "example_values": [
+          "smokes",
+          "Unknown",
+          "never smoked",
+          "formerly smoked"
+        ]
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+    {
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+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
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+      "impute_strategy": "mode",
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+    }
+  ]
+}

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/public_gate/public_gate_report.json

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+{
+  "dataset_id": "m7",
+  "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": "Residence_type",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m7/m7-test.csv"
+  }
+}

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+{
+  "dataset_id": "m7",
+  "target_column": "Residence_type",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/ctgan/ctgan-m7-20260422_031259/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/ctgan/ctgan-m7-20260422_031259/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/ctgan/ctgan-m7-20260422_031259/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/ctgan/ctgan-m7-20260422_031259/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/ctgan/ctgan-m7-20260422_031259/public_gate/public_gate_report.json",
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+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 5,
+        "unique_ratio": 0.001223,
+        "example_values": [
+          "Private",
+          "Govt_job",
+          "Self-employed",
+          "children",
+          "Never_worked"
+        ]
+      }
+    },
+    {
+      "name": "Residence_type",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "Rural",
+          "Urban"
+        ]
+      }
+    },
+    {
+      "name": "avg_glucose_level",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3337,
+        "unique_ratio": 0.816292,
+        "example_values": [
+          "100.42",
+          "62.21",
+          "106.68",
+          "106.03",
+          "86.39"
+        ]
+      }
+    },
+    {
+      "name": "bmi",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": true,
+      "missing_tokens": [
+        "N/A"
+      ],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.039873,
+        "unique_count": 396,
+        "unique_ratio": 0.100892,
+        "example_values": [
+          "39.5",
+          "28.3",
+          "29.3",
+          "30.2",
+          "48.9"
+        ]
+      }
+    },
+    {
+      "name": "smoking_status",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000978,
+        "example_values": [
+          "smokes",
+          "Unknown",
+          "never smoked",
+          "formerly smoked"
+        ]
+      }
+    },
+    {
+      "name": "stroke",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000489,
+        "example_values": [
+          "0",
+          "1"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/m7/ctgan/ctgan-m7-20260422_031259/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "m7",
+  "model": "ctgan",
+  "run_id": "ctgan-m7-20260422_031259",
+  "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/m7/ctgan/ctgan-m7-20260422_031259/ctgan-m7-4088-20260422_031701.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/m7/ctgan/ctgan-m7-20260422_031259/models_300epochs/ctgan_300epochs.pt"
+  }
+}