Add syntheticSuccess c9

syntheticSuccess/c9/arf/arf-c9-20260321_065512/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=26215)
+syn.to_csv("/work/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/arf-c9-26215-20260330_065333.csv", index=False)
+print(f"[ARF] Generated 26215 rows -> /work/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/arf-c9-26215-20260330_065333.csv")

syntheticSuccess/c9/arf/arf-c9-20260321_065512/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/arf_model.pkl")

syntheticSuccess/c9/arf/arf-c9-20260321_065512/arf-c9-1000-20260321_070552.csv

@@ -0,0 +1,3 @@
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+size 156250

syntheticSuccess/c9/arf/arf-c9-20260321_065512/arf-c9-26215-20260330_065333.csv

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+size 4067087

syntheticSuccess/c9/arf/arf-c9-20260321_065512/arf_model.pkl

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+size 118258108

syntheticSuccess/c9/arf/arf-c9-20260321_065512/gen_20260321_070552.log

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+size 438

syntheticSuccess/c9/arf/arf-c9-20260321_065512/gen_20260330_065333.log

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+size 440

syntheticSuccess/c9/arf/arf-c9-20260321_065512/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c9",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-train.csv",
+      "exists": true,
+      "size": 1656550,
+      "sha256": "7fe9e4b6e2719c346fd93c7ada4977430d0c593cc5bdae7442a78886f7b71b9e"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-val.csv",
+      "exists": true,
+      "size": 207139,
+      "sha256": "01557a0b4304c4b5435df0909d16016ec0ba4ce2c3b31a8815f813ef1376b0b9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-test.csv",
+      "exists": true,
+      "size": 207206,
+      "sha256": "6de1346ef61646cbd096588223a049bf26f2b26229ac65b2734fed139f3c06bb"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c9/c9-dataset_profile.json",
+      "exists": true,
+      "size": 4540,
+      "sha256": "536d3ace5469df116ca00b7544ee8af409fc29edb7e85f365e465a91244a564e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c9/c9-dataset_contract_v1.json",
+      "exists": true,
+      "size": 5389,
+      "sha256": "d63ab9aee52c7f931f14b7ad2c9a601b670ba5cd739494b2c4d2e4005430ea6d"
+    }
+  }
+}

syntheticSuccess/c9/arf/arf-c9-20260321_065512/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,214 @@
+{
+  "dataset_id": "c9",
+  "target_column": "ROLE_FAMILY",
+  "task_type": "regression",
+  "columns": [
+    {
+      "name": "ACTION",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_ratio": 7.6e-05,
+        "example_values": [
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.016861,
+        "example_values": [
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+          "119092",
+          "81476",
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+          "117941"
+        ]
+      }
+    },
+    {
+      "name": "ROLE_TITLE",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 334,
+        "unique_ratio": 0.012741,
+        "example_values": [
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+          "119962",
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+        ]
+      }
+    },
+    {
+      "name": "ROLE_FAMILY_DESC",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "median",
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+        ]
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+    },
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 334,
+        "unique_ratio": 0.012741,
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+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c9/arf/arf-c9-20260321_065512/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c9",
+  "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": "ROLE_FAMILY",
+  "task_type": "regression",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-test.csv"
+  }
+}

syntheticSuccess/c9/arf/arf-c9-20260321_065512/public_gate/staged_input_manifest.json

@@ -0,0 +1,219 @@
+{
+  "dataset_id": "c9",
+  "target_column": "ROLE_FAMILY",
+  "task_type": "regression",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "ACTION",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "example_values": [
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+          "0"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "median",
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+        ]
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+        ]
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+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
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+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "median",
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+  ]
+}

syntheticSuccess/c9/arf/arf-c9-20260321_065512/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c9",
+  "model": "arf",
+  "run_id": "arf-c9-20260321_065512",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "skipped",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/arf-c9-26215-20260330_065333.csv"
+  }
+}

syntheticSuccess/c9/arf/arf-c9-20260321_065512/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/c9/arf/arf-c9-20260321_065512/staged/arf/model_input_manifest.json"
+}

syntheticSuccess/c9/arf/arf-c9-20260321_065512/staged/arf/adapter_transforms_applied.json

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

syntheticSuccess/c9/arf/arf-c9-20260321_065512/staged/arf/model_input_manifest.json

@@ -0,0 +1,221 @@
+{
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+  "model": "arf",
+  "target_column": "ROLE_FAMILY",
+  "task_type": "regression",
+  "column_schema": [
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+      "nullable": false,
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+          "117910",
+          "117961",
+          "118595",
+          "118200",
+          "117978"
+        ]
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+      "name": "ROLE_ROLLUP_2",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 174,
+        "unique_ratio": 0.006637,
+        "example_values": [
+          "117911",
+          "118225",
+          "118596",
+          "117962",
+          "118201"
+        ]
+      }
+    },
+    {
+      "name": "ROLE_DEPTNAME",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 442,
+        "unique_ratio": 0.016861,
+        "example_values": [
+          "117912",
+          "119092",
+          "81476",
+          "119223",
+          "117941"
+        ]
+      }
+    },
+    {
+      "name": "ROLE_TITLE",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 334,
+        "unique_ratio": 0.012741,
+        "example_values": [
+          "118568",
+          "122849",
+          "118054",
+          "119962",
+          "117885"
+        ]
+      }
+    },
+    {
+      "name": "ROLE_FAMILY_DESC",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.084112,
+        "example_values": [
+          "198040",
+          "119094",
+          "120238",
+          "168365",
+          "117913"
+        ]
+      }
+    },
+    {
+      "name": "ROLE_FAMILY",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
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+        "unique_ratio": 0.002556,
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+          "19721",
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+      "name": "ROLE_CODE",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
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+        "unique_ratio": 0.012741,
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+          "118570",
+          "122850",
+          "118055",
+          "119964",
+          "117888"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c9/arf/arf-c9-20260321_065512/public_gate/public_gate_report.json"
+}

syntheticSuccess/c9/arf/arf-c9-20260321_065512/staged/public/staged_features.json

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+[
+  {
+    "feature_name": "ACTION",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "RESOURCE",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "MGR_ID",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ROLE_ROLLUP_1",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ROLE_ROLLUP_2",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ROLE_DEPTNAME",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ROLE_TITLE",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ROLE_FAMILY_DESC",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "ROLE_FAMILY",
+    "data_type": "continuous",
+    "is_target": true
+  },
+  {
+    "feature_name": "ROLE_CODE",
+    "data_type": "continuous",
+    "is_target": false
+  }
+]

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syntheticSuccess/c9/bayesnet/bayesnet-c9-20260321_062102/_bayesnet_generate.py

@@ -0,0 +1,43 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/c9/bayesnet/bayesnet-c9-20260321_062102/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=26215).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c9/bayesnet/bayesnet-c9-20260321_062102/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+if os.path.exists(const_path):
+    with open(const_path) as _f:
+        const_cols = _json.load(_f)
+    for col, val in const_cols.items():
+        syn[col] = val
+        print(f"[BayesNet] Restored constant column '{col}' = {val}")
+
+syn.to_csv("/work/output-SpecializedModels/c9/bayesnet/bayesnet-c9-20260321_062102/bayesnet-c9-26215-20260330_065334.csv", index=False)
+print(f"[BayesNet] Generated 26215 rows -> /work/output-SpecializedModels/c9/bayesnet/bayesnet-c9-20260321_062102/bayesnet-c9-26215-20260330_065334.csv")

syntheticSuccess/c9/bayesnet/bayesnet-c9-20260321_062102/_bayesnet_train.py

@@ -0,0 +1,62 @@
+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...")
+        # Install synthcity with numpy<2 to avoid conflicts
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        # Remove torch/torchvision from pip_libs to avoid shadowing system versions
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+from synthcity.plugins import Plugins
+import pickle
+import pandas as pd
+from synthcity.plugins.core.dataloader import GenericDataLoader
+
+df = pd.read_csv("/work/output-SpecializedModels/c9/bayesnet/bayesnet-c9-20260321_062102/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+
+# Drop zero-variance columns (only 1 unique value) to avoid
+# synthcity encoder KeyError during generation
+import json as _json
+const_cols = {}
+for col in list(df.columns):
+    nuniq = df[col].nunique()
+    if nuniq <= 1:
+        const_cols[col] = df[col].iloc[0] if len(df) > 0 else None
+        df = df.drop(columns=[col])
+        print(f"[BayesNet] Dropped zero-variance column '{col}' (value={const_cols[col]})")
+
+# Save constant columns info so generate can restore them
+const_path = "/work/output-SpecializedModels/c9/bayesnet/bayesnet-c9-20260321_062102/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w") as _f:
+    _json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+print(f"[BayesNet] Training on {len(df)} rows, {len(df.columns)} cols")
+
+loader = GenericDataLoader(df)
+plugin = Plugins().get("bayesian_network")
+plugin.fit(loader)
+
+with open("/work/output-SpecializedModels/c9/bayesnet/bayesnet-c9-20260321_062102/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c9/bayesnet/bayesnet-c9-20260321_062102/bayesnet_model.pkl")

syntheticSuccess/c9/bayesnet/bayesnet-c9-20260321_062102/bayesnet-c9-1000-20260321_062232.csv

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+size 1629793

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syntheticSuccess/c9/bayesnet/bayesnet-c9-20260321_062102/const_cols.json

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+{}

syntheticSuccess/c9/bayesnet/bayesnet-c9-20260321_062102/gen_20260321_062232.log

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syntheticSuccess/c9/bayesnet/bayesnet-c9-20260321_062102/input_snapshot.json

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+{
+  "dataset_id": "c9",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-train.csv",
+      "exists": true,
+      "size": 1656550,
+      "sha256": "7fe9e4b6e2719c346fd93c7ada4977430d0c593cc5bdae7442a78886f7b71b9e"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-val.csv",
+      "exists": true,
+      "size": 207139,
+      "sha256": "01557a0b4304c4b5435df0909d16016ec0ba4ce2c3b31a8815f813ef1376b0b9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c9/c9-test.csv",
+      "exists": true,
+      "size": 207206,
+      "sha256": "6de1346ef61646cbd096588223a049bf26f2b26229ac65b2734fed139f3c06bb"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c9/c9-dataset_profile.json",
+      "exists": true,
+      "size": 4540,
+      "sha256": "536d3ace5469df116ca00b7544ee8af409fc29edb7e85f365e465a91244a564e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c9/c9-dataset_contract_v1.json",
+      "exists": true,
+      "size": 5389,
+      "sha256": "d63ab9aee52c7f931f14b7ad2c9a601b670ba5cd739494b2c4d2e4005430ea6d"
+    }
+  }
+}

syntheticSuccess/c9/bayesnet/bayesnet-c9-20260321_062102/public_gate/normalized_schema_snapshot.json

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+{
+  "dataset_id": "c9",
+  "target_column": "ROLE_FAMILY",
+  "task_type": "regression",
+  "columns": [
+    {
+      "name": "ACTION",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
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+      "impute_strategy": "mode",
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+        "unique_ratio": 7.6e-05,
+        "example_values": [
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+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
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+    },
+    {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
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+    },
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+      "role": "feature",
+      "semantic_type": "numeric",
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+          "118201"
+        ]
+      }
+    },
+    {
+      "name": "ROLE_DEPTNAME",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.016861,
+        "example_values": [
+          "117912",
+          "119092",
+          "81476",
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+          "117941"
+        ]
+      }
+    },
+    {
+      "name": "ROLE_TITLE",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 334,
+        "unique_ratio": 0.012741,
+        "example_values": [
+          "118568",
+          "122849",
+          "118054",
+          "119962",
+          "117885"
+        ]
+      }
+    },
+    {
+      "name": "ROLE_FAMILY_DESC",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2205,
+        "unique_ratio": 0.084112,
+        "example_values": [
+          "198040",
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+          "120238",
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+          "117913"
+        ]
+      }
+    },
+    {
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+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "median",
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+        "unique_ratio": 0.002556,
+        "example_values": [
+          "19721",
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+        ]
+      }
+    },
+    {
+      "name": "ROLE_CODE",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.012741,
+        "example_values": [
+          "118570",
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+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c9/bayesnet/bayesnet-c9-20260321_062102/public_gate/public_gate_report.json

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+{
+  "dataset_id": "c9",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
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