Add syntheticSuccess c20

syntheticSuccess/c20/arf/arf-c20-20260324_073259/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=35855)
+syn.to_csv("/work/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/arf-c20-35855-20260330_065506.csv", index=False)
+print(f"[ARF] Generated 35855 rows -> /work/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/arf-c20-35855-20260330_065506.csv")

syntheticSuccess/c20/arf/arf-c20-20260324_073259/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/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/c20/arf/arf-c20-20260324_073259/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/arf_model.pkl")

syntheticSuccess/c20/arf/arf-c20-20260324_073259/arf-c20-1000-20260324_073801.csv

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+size 66568

syntheticSuccess/c20/arf/arf-c20-20260324_073259/arf-c20-35855-20260330_065506.csv

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+size 2395733

syntheticSuccess/c20/arf/arf-c20-20260324_073259/arf_model.pkl

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+size 116149296

syntheticSuccess/c20/arf/arf-c20-20260324_073259/gen_20260324_073801.log

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+size 441

syntheticSuccess/c20/arf/arf-c20-20260324_073259/gen_20260330_065506.log

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+size 443

syntheticSuccess/c20/arf/arf-c20-20260324_073259/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c20",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+      "exists": true,
+      "size": 537948,
+      "sha256": "7b491c9989a22d06db0c24422e76bf1d73173a7ba1760d5a3c4e5768c868ac21"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-val.csv",
+      "exists": true,
+      "size": 67338,
+      "sha256": "82d69efb2fbd8ddf6e8d77d9070b3d299f619fded74e2956d3af4b34a9c95a75"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv",
+      "exists": true,
+      "size": 67368,
+      "sha256": "ab311b2ccc419e2b1423bd27d4079912f464405ef8d37cd87e73ad147ed787ec"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_profile.json",
+      "exists": true,
+      "size": 3686,
+      "sha256": "64a935250a31399f41982c61828d40ab90791bab84e8dc6201dbdfa4508092b8"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3867,
+      "sha256": "78c743a19951022e3cbae86814578ff8723146c3d216423edc4b94e1b233ad27"
+    }
+  }
+}

syntheticSuccess/c20/arf/arf-c20-20260324_073259/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,152 @@
+{
+  "dataset_id": "c20",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "white_piece0_strength",
+      "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,
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+      "nullable": false,
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+      "role": "feature",
+      "semantic_type": "numeric",
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+          "6"
+        ]
+      }
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+    {
+      "name": "black_piece0_rank",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.000251,
+        "example_values": [
+          "7",
+          "2",
+          "5",
+          "1",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 8.4e-05,
+        "example_values": [
+          "w",
+          "b",
+          "d"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c20/arf/arf-c20-20260324_073259/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c20",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG003_profile_header_match",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG004_missing_token_normalized",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
+  "target_column": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv"
+  }
+}

syntheticSuccess/c20/arf/arf-c20-20260324_073259/public_gate/staged_input_manifest.json

@@ -0,0 +1,157 @@
+{
+  "dataset_id": "c20",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "white_piece0_strength",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.000139,
+        "example_values": [
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+      }
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
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+      "role": "feature",
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+      "nullable": false,
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+      "impute_strategy": "median",
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
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+      "parse_format": null,
+      "impute_strategy": "median",
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+          "1",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+          "b",
+          "d"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c20/arf/arf-c20-20260324_073259/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c20",
+  "model": "arf",
+  "run_id": "arf-c20-20260324_073259",
+  "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/c20/arf/arf-c20-20260324_073259/arf-c20-35855-20260330_065506.csv"
+  }
+}

syntheticSuccess/c20/arf/arf-c20-20260324_073259/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/c20/arf/arf-c20-20260324_073259/staged/arf/model_input_manifest.json"
+}

syntheticSuccess/c20/arf/arf-c20-20260324_073259/staged/arf/adapter_transforms_applied.json

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

syntheticSuccess/c20/arf/arf-c20-20260324_073259/staged/arf/model_input_manifest.json

@@ -0,0 +1,159 @@
+{
+  "dataset_id": "c20",
+  "model": "arf",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "white_piece0_strength",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.000139,
+        "example_values": [
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+          "0",
+          "4",
+          "5",
+          "6"
+        ]
+      }
+    },
+    {
+      "name": "white_piece0_file",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.000195,
+        "example_values": [
+          "5",
+          "6",
+          "3",
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+          "1"
+        ]
+      }
+    },
+    {
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+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
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+        "unique_ratio": 0.000251,
+        "example_values": [
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+          "1",
+          "3",
+          "7"
+        ]
+      }
+    },
+    {
+      "name": "black_piece0_strength",
+      "role": "feature",
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+        ]
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+        "example_values": [
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+          "b",
+          "d"
+        ]
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+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/arf/arf-c20-20260324_073259/public_gate/public_gate_report.json"
+}

syntheticSuccess/c20/arf/arf-c20-20260324_073259/staged/public/staged_features.json

@@ -0,0 +1,37 @@
+[
+  {
+    "feature_name": "white_piece0_strength",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "white_piece0_file",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "white_piece0_rank",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "black_piece0_strength",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "black_piece0_file",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "black_piece0_rank",
+    "data_type": "continuous",
+    "is_target": false
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+    "data_type": "categorical",
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syntheticSuccess/c20/bayesnet/bayesnet-c20-20260321_075513/_bayesnet_generate.py

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+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/c20/bayesnet/bayesnet-c20-20260321_075513/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=35855).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c20/bayesnet/bayesnet-c20-20260321_075513/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/c20/bayesnet/bayesnet-c20-20260321_075513/bayesnet-c20-35855-20260330_065507.csv", index=False)
+print(f"[BayesNet] Generated 35855 rows -> /work/output-SpecializedModels/c20/bayesnet/bayesnet-c20-20260321_075513/bayesnet-c20-35855-20260330_065507.csv")

syntheticSuccess/c20/bayesnet/bayesnet-c20-20260321_075513/_bayesnet_train.py

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+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/c20/bayesnet/bayesnet-c20-20260321_075513/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/c20/bayesnet/bayesnet-c20-20260321_075513/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/c20/bayesnet/bayesnet-c20-20260321_075513/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c20/bayesnet/bayesnet-c20-20260321_075513/bayesnet_model.pkl")

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+size 14122

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+{
+  "dataset_id": "c20",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+      "exists": true,
+      "size": 537948,
+      "sha256": "7b491c9989a22d06db0c24422e76bf1d73173a7ba1760d5a3c4e5768c868ac21"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-val.csv",
+      "exists": true,
+      "size": 67338,
+      "sha256": "82d69efb2fbd8ddf6e8d77d9070b3d299f619fded74e2956d3af4b34a9c95a75"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv",
+      "exists": true,
+      "size": 67368,
+      "sha256": "ab311b2ccc419e2b1423bd27d4079912f464405ef8d37cd87e73ad147ed787ec"
+    },
+    "profile_json": {
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+      "exists": true,
+      "size": 3686,
+      "sha256": "64a935250a31399f41982c61828d40ab90791bab84e8dc6201dbdfa4508092b8"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c20/c20-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3867,
+      "sha256": "78c743a19951022e3cbae86814578ff8723146c3d216423edc4b94e1b233ad27"
+    }
+  }
+}

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+{
+  "dataset_id": "c20",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
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+      "role": "feature",
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+          "6"
+        ]
+      }
+    },
+    {
+      "name": "black_piece0_rank",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_ratio": 0.000251,
+        "example_values": [
+          "7",
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+          "5",
+          "1",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
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+        "unique_ratio": 8.4e-05,
+        "example_values": [
+          "w",
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+    }
+  ]
+}

syntheticSuccess/c20/bayesnet/bayesnet-c20-20260321_075513/public_gate/public_gate_report.json

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+{
+  "dataset_id": "c20",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG003_profile_header_match",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG004_missing_token_normalized",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
+  "target_column": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c20/c20-test.csv"
+  }
+}

syntheticSuccess/c20/bayesnet/bayesnet-c20-20260321_075513/public_gate/staged_input_manifest.json

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+{
+  "dataset_id": "c20",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/bayesnet/bayesnet-c20-20260321_075513/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/bayesnet/bayesnet-c20-20260321_075513/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/bayesnet/bayesnet-c20-20260321_075513/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/bayesnet/bayesnet-c20-20260321_075513/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c20/bayesnet/bayesnet-c20-20260321_075513/public_gate/public_gate_report.json",
+  "column_schema": [
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+        ]
+      }
+    },
+    {
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+      "impute_strategy": "median",
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+        "example_values": [
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+          "1",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+        "example_values": [
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+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c20/bayesnet/bayesnet-c20-20260321_075513/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c20",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c20-20260321_075513",
+  "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/c20/bayesnet/bayesnet-c20-20260321_075513/bayesnet-c20-35855-20260330_065507.csv"
+  }
+}

syntheticSuccess/c20/bayesnet/bayesnet-c20-20260321_075513/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/c20/bayesnet/bayesnet-c20-20260321_075513/staged/bayesnet/model_input_manifest.json"
+}

syntheticSuccess/c20/bayesnet/bayesnet-c20-20260321_075513/staged/bayesnet/adapter_transforms_applied.json

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syntheticSuccess/c20/bayesnet/bayesnet-c20-20260321_075513/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,159 @@
+{
+  "dataset_id": "c20",
+  "model": "bayesnet",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
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