Add syntheticSuccess c7

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/_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/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=10368).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/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/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv", index=False)
+print(f"[BayesNet] Generated 10368 rows -> /work/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv")

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/_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/c7/bayesnet/bayesnet-c7-20260321_061816/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/c7/bayesnet/bayesnet-c7-20260321_061816/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/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl")

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-1000-20260321_061903.csv

@@ -0,0 +1,3 @@
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+oid sha256:a082b4d0235d47db0678b4525f42d70a94759dbd763c798f07df7236a908e891
+size 81759

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv

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+size 847021

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f4d7e04ef9de203ea1e07b25a9caac563c8bf34884957dbac67a8ddd149ab7e2
+size 1180522

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/const_cols.json

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

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260321_061903.log

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+size 480

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260330_065316.log

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8a9f2d9052018cdd13942a9a359eb52d98f22fdc71cb2df8060f7653b8337b5
+size 482

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c7",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv",
+      "exists": true,
+      "size": 857718,
+      "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv",
+      "exists": true,
+      "size": 107489,
+      "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv",
+      "exists": true,
+      "size": 107327,
+      "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json",
+      "exists": true,
+      "size": 4014,
+      "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json",
+      "exists": true,
+      "size": 4759,
+      "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd"
+    }
+  }
+}

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,183 @@
+{
+  "dataset_id": "c7",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "parents",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "usual",
+          "pretentious",
+          "great_pret"
+        ]
+      }
+    },
+    {
+      "name": "has_nurs",
+      "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": 0.000482,
+        "example_values": [
+          "very_crit",
+          "critical",
+          "improper",
+          "less_proper",
+          "proper"
+        ]
+      }
+    },
+    {
+      "name": "form",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "complete",
+          "completed",
+          "incomplete",
+          "foster"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "1",
+          "3",
+          "2",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "housing",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "less_conv",
+          "convenient",
+          "critical"
+        ]
+      }
+    },
+    {
+      "name": "finance",
+      "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": 0.000193,
+        "example_values": [
+          "convenient",
+          "inconv"
+        ]
+      }
+    },
+    {
+      "name": "social",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "slightly_prob",
+          "nonprob",
+          "problematic"
+        ]
+      }
+    },
+    {
+      "name": "health",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "recommended",
+          "priority",
+          "not_recom"
+        ]
+      }
+    },
+    {
+      "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": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "priority",
+          "spec_prior",
+          "not_recom",
+          "very_recom",
+          "recommend"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c7",
+  "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/c7/c7-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv"
+  }
+}

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/staged_input_manifest.json

@@ -0,0 +1,188 @@
+{
+  "dataset_id": "c7",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "parents",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "usual",
+          "pretentious",
+          "great_pret"
+        ]
+      }
+    },
+    {
+      "name": "has_nurs",
+      "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": 0.000482,
+        "example_values": [
+          "very_crit",
+          "critical",
+          "improper",
+          "less_proper",
+          "proper"
+        ]
+      }
+    },
+    {
+      "name": "form",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "complete",
+          "completed",
+          "incomplete",
+          "foster"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "1",
+          "3",
+          "2",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "housing",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "less_conv",
+          "convenient",
+          "critical"
+        ]
+      }
+    },
+    {
+      "name": "finance",
+      "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": 0.000193,
+        "example_values": [
+          "convenient",
+          "inconv"
+        ]
+      }
+    },
+    {
+      "name": "social",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "slightly_prob",
+          "nonprob",
+          "problematic"
+        ]
+      }
+    },
+    {
+      "name": "health",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "recommended",
+          "priority",
+          "not_recom"
+        ]
+      }
+    },
+    {
+      "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": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "priority",
+          "spec_prior",
+          "not_recom",
+          "very_recom",
+          "recommend"
+        ]
+      }
+    }
+  ]
+}

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c7",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c7-20260321_061816",
+  "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/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-20260330_065316.csv"
+  }
+}

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/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/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/model_input_manifest.json"
+}

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/adapter_transforms_applied.json

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

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,190 @@
+{
+  "dataset_id": "c7",
+  "model": "bayesnet",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "parents",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "usual",
+          "pretentious",
+          "great_pret"
+        ]
+      }
+    },
+    {
+      "name": "has_nurs",
+      "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": 0.000482,
+        "example_values": [
+          "very_crit",
+          "critical",
+          "improper",
+          "less_proper",
+          "proper"
+        ]
+      }
+    },
+    {
+      "name": "form",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "complete",
+          "completed",
+          "incomplete",
+          "foster"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.000386,
+        "example_values": [
+          "1",
+          "3",
+          "2",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "housing",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
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+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "less_conv",
+          "convenient",
+          "critical"
+        ]
+      }
+    },
+    {
+      "name": "finance",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.000193,
+        "example_values": [
+          "convenient",
+          "inconv"
+        ]
+      }
+    },
+    {
+      "name": "social",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "slightly_prob",
+          "nonprob",
+          "problematic"
+        ]
+      }
+    },
+    {
+      "name": "health",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "recommended",
+          "priority",
+          "not_recom"
+        ]
+      }
+    },
+    {
+      "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": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "priority",
+          "spec_prior",
+          "not_recom",
+          "very_recom",
+          "recommend"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/bayesnet/bayesnet-c7-20260321_061816/public_gate/public_gate_report.json"
+}

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/staged_features.json

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+[
+  {
+    "feature_name": "parents",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "has_nurs",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "form",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "children",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "housing",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "finance",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "social",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "health",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv

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+size 106030

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv

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+size 847349

syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/val.csv

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syntheticSuccess/c7/bayesnet/bayesnet-c7-20260321_061816/train_20260321_061816.log

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syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/_tabddpm_sample.py

@@ -0,0 +1,67 @@
+import os, sys, subprocess, json
+import numpy as np
+import pandas as pd
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Reuse the compat wrapper (patches collections.Sequence for skorch)
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+if not os.path.exists(wrapper):
+    with open(wrapper, "w") as f:
+        f.write(
+            "import collections, collections.abc\n"
+            "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+            "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+            "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+            "import sys, runpy\n"
+            "sys.argv = sys.argv[1:]\n"
+            "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+        )
+
+print(f"[TabDDPM] Sampling 10368 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/config_sample_20260422_211650.toml",
+     "--sample"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+
+# 将 .npy 输出转为 CSV
+work_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246"
+info_path = os.path.join(work_dir, "data", "info.json")
+with open(info_path) as f:
+    info = json.load(f)
+
+output_dir = os.path.join(work_dir, "output")
+col_names = info.get("column_names", [])
+
+parts = []
+x_num_path = os.path.join(output_dir, "X_num_train.npy")
+x_cat_path = os.path.join(output_dir, "X_cat_train.npy")
+y_path = os.path.join(output_dir, "y_train.npy")
+
+if os.path.exists(x_num_path):
+    parts.append(np.load(x_num_path, allow_pickle=True))
+if os.path.exists(x_cat_path):
+    parts.append(np.load(x_cat_path, allow_pickle=True).astype(float))
+if os.path.exists(y_path):
+    y = np.load(y_path, allow_pickle=True)
+    parts.append(y.reshape(-1, 1) if y.ndim == 1 else y)
+
+if parts:
+    combined = np.concatenate(parts, axis=1)
+    if col_names and len(col_names) == combined.shape[1]:
+        df = pd.DataFrame(combined, columns=col_names)
+    else:
+        df = pd.DataFrame(combined)
+    df.to_csv("/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/_tabddpm_train.py

@@ -0,0 +1,32 @@
+import os, sys, subprocess
+
+tabddpm_root = "/workspace/tabddpm/code"
+assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}"
+env = os.environ.copy()
+env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", ""))
+
+# Write a wrapper that patches collections.Sequence (removed in Python 3.10+)
+# before running pipeline.py - needed because skorch uses old API
+wrapper = os.path.join(tabddpm_root, "_compat_run.py")
+with open(wrapper, "w") as f:
+    f.write(
+        "import collections, collections.abc\n"
+        "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping',"
+        "'MutableSet','Set','Callable','Iterable','Iterator'):\n"
+        "    if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n"
+        "import sys, runpy\n"
+        "sys.argv = sys.argv[1:]\n"
+        "runpy.run_path(sys.argv[0], run_name='__main__')\n"
+    )
+
+print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 8
+num_classes = 5
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 1000
+batch_size = 1000
+seed = 0

syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/config_sample_20260422_211650.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 8
+num_classes = 5
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 10368
+batch_size = 1000
+seed = 0

syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/data/X_cat_train.npy

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+version https://git-lfs.github.com/spec/v1
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syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/data/info.json

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+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 8,
+  "train_size": 10368,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7
+  ],
+  "target_col_idx": [
+    8
+  ],
+  "column_names": [
+    "parents",
+    "has_nurs",
+    "form",
+    "children",
+    "housing",
+    "finance",
+    "social",
+    "health",
+    "class"
+  ],
+  "num_classes": 5
+}

syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/data/y_train.npy

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+version https://git-lfs.github.com/spec/v1
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syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/gen_20260422_211650.log

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syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/input_snapshot.json

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+{
+  "dataset_id": "c7",
+  "model": "tabddpm",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv",
+      "exists": true,
+      "size": 857718,
+      "sha256": "0ec97b49cecfd452f07551a63db7b812b5998a1e37101eae82255d00aa6a6243"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv",
+      "exists": true,
+      "size": 107489,
+      "sha256": "4501bb2be19f7e13b7ff5e9dedd74e3dd42f2cafc8cefd5435bda61fc974a769"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv",
+      "exists": true,
+      "size": 107327,
+      "sha256": "f9e808033a07feabb980addcf8c5f75111189ac2fb70993b8ad0f5ca3d5cfbae"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_profile.json",
+      "exists": true,
+      "size": 4014,
+      "sha256": "60424c615b91a26cf02d9bc1d7f91caa0ceb95bab39eb7cff6f9edea3ca0600e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c7/c7-dataset_contract_v1.json",
+      "exists": true,
+      "size": 4759,
+      "sha256": "79a434a1e2553b14b9f2e98c1adfc32a71aaa0d6cd49234f3f8a5603efca4ebd"
+    }
+  }
+}

syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/output/X_cat_train.npy

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syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/output/X_cat_unnorm.npy

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syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/output/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/output"
+real_data_path = "/work/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 8
+num_classes = 5
+is_y_cond = true
+
+[model_params.rtdl_params]
+d_layers = [256, 256]
+dropout = 0.0
+
+[diffusion_params]
+num_timesteps = 1000
+gaussian_loss_type = "mse"
+
+[train.main]
+steps = 5000
+lr = 0.001
+weight_decay = 0.0
+batch_size = 256
+
+[train.T]
+seed = 0
+normalization = "quantile"
+num_nan_policy = "__none__"
+cat_nan_policy = "__none__"
+cat_min_frequency = "__none__"
+cat_encoding = "__none__"
+y_policy = "default"
+
+[sample]
+num_samples = 10368
+batch_size = 1000
+seed = 0

syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/output/info.json

@@ -0,0 +1,33 @@
+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 8,
+  "train_size": 10368,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7
+  ],
+  "target_col_idx": [
+    8
+  ],
+  "column_names": [
+    "parents",
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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c7/tabddpm/tabddpm-c7-20260422_211246/public_gate/public_gate_report.json"
+}

syntheticSuccess/c7/tabddpm/tabddpm-c7-20260422_211246/tabddpm-c7-10368-20260422_211650.csv

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