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SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/_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/c5/bayesnet/bayesnet-c5-20260318_033000/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=6732).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/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/c5/bayesnet/bayesnet-c5-20260318_033000/bayesnet-c5-6732-20260318_033044.csv", index=False)
+print(f"[BayesNet] Generated 6732 rows -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/bayesnet-c5-6732-20260318_033044.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/_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/DatasetNew/c5/c5-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/c5/bayesnet/bayesnet-c5-20260318_033000/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/c5/bayesnet/bayesnet-c5-20260318_033000/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/gen_20260318_033044.log

@@ -0,0 +1,11 @@
+03/17/2026 19:31:15:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:31:15:WARNING:Probability values don't exactly sum to 1. Differ by: 1.1102230246251565e-16. Adjusting values.
+03/17/2026 19:31:15:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:31:15:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:31:15:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:31:15:WARNING:Probability values don't exactly sum to 1. Differ by: 1.1102230246251565e-16. Adjusting values.
+03/17/2026 19:31:15:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 19:31:15:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Restored constant column 'veil-type' = PARTIAL
+[BayesNet] Generated 6732 rows -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/bayesnet-c5-6732-20260318_033044.csv

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/train_20260318_033000.log

@@ -0,0 +1,6 @@
+[2026-03-17T19:30:26.510434+0000][1][CRITICAL] Error importing TabularGoggle: No module named 'dgl'
+[2026-03-17T19:30:26.521567+0000][1][CRITICAL] module disabled: /pip_libs/synthcity/plugins/generic/plugin_goggle.py
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Dropped zero-variance column 'veil-type' (value=PARTIAL)
+[BayesNet] Training on 6732 rows, 22 cols
+[BayesNet] Model saved -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260318_033000/bayesnet_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/_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/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=6732).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/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/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-6732-20260330_065301.csv", index=False)
+print(f"[BayesNet] Generated 6732 rows -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-6732-20260330_065301.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/_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/c5/bayesnet/bayesnet-c5-20260321_061655/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/c5/bayesnet/bayesnet-c5-20260321_061655/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/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/gen_20260321_061740.log

@@ -0,0 +1,11 @@
+03/20/2026 22:18:08:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:18:08:WARNING:Probability values don't exactly sum to 1. Differ by: 1.1102230246251565e-16. Adjusting values.
+03/20/2026 22:18:08:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:18:08:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:18:09:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:18:09:WARNING:Probability values don't exactly sum to 1. Differ by: 1.1102230246251565e-16. Adjusting values.
+03/20/2026 22:18:09:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 22:18:09:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Restored constant column 'veil-type' = PARTIAL
+[BayesNet] Generated 1000 rows -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-1000-20260321_061740.csv

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/gen_20260330_065301.log

@@ -0,0 +1,11 @@
+03/29/2026 22:53:30:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:53:30:WARNING:Probability values don't exactly sum to 1. Differ by: 1.1102230246251565e-16. Adjusting values.
+03/29/2026 22:53:30:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:53:30:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:53:30:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:53:30:WARNING:Probability values don't exactly sum to 1. Differ by: 1.1102230246251565e-16. Adjusting values.
+03/29/2026 22:53:30:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:53:30:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Restored constant column 'veil-type' = PARTIAL
+[BayesNet] Generated 6732 rows -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet-c5-6732-20260330_065301.csv

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c5",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-train.csv",
+      "exists": true,
+      "size": 1004346,
+      "sha256": "47ca8fcb0dce8411cee7c20652d6bf10a48a4c284cef58267e775b807c625180"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-val.csv",
+      "exists": true,
+      "size": 125666,
+      "sha256": "599dbe0d059984263e88e20a64ae75c0f9795a6ee662c7ae3d13fe4db35753c2"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-test.csv",
+      "exists": true,
+      "size": 126062,
+      "sha256": "f4eab85438337cfa2fd60a783388c9bb9f2a67a8c077d09f929fe34ee2895c28"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c5/c5-dataset_profile.json",
+      "exists": true,
+      "size": 8949,
+      "sha256": "74d201cb6f2a25d865c87f0421b9f9c5969d2edfd8fbae898594ff626b33e393"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c5/c5-dataset_contract_v1.json",
+      "exists": true,
+      "size": 11183,
+      "sha256": "df30b6c2fb9044e5c99b3257357a58462928289a836a9cf591e2f754f49bf729"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,467 @@
+{
+  "dataset_id": "c5",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "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": 2,
+        "unique_ratio": 0.000297,
+        "example_values": [
+          "EDIBLE",
+          "POISONOUS"
+        ]
+      }
+    },
+    {
+      "name": "cap-shape",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 6,
+        "unique_ratio": 0.000891,
+        "example_values": [
+          "CONVEX",
+          "BELL",
+          "FLAT",
+          "KNOBBED",
+          "CONICAL"
+        ]
+      }
+    },
+    {
+      "name": "cap-surface",
+      "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": 0.000594,
+        "example_values": [
+          "SCALY",
+          "SMOOTH",
+          "FIBROUS",
+          "GROOVES"
+        ]
+      }
+    },
+    {
+      "name": "cap-color",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 10,
+        "unique_ratio": 0.001485,
+        "example_values": [
+          "YELLOW",
+          "GRAY",
+          "BUFF",
+          "WHITE",
+          "BROWN"
+        ]
+      }
+    },
+    {
+      "name": "bruises?",
+      "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": 0.000297,
+        "example_values": [
+          "BRUISES",
+          "NO"
+        ]
+      }
+    },
+    {
+      "name": "odor",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "NONE"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.453209,
+        "unique_count": 8,
+        "unique_ratio": 0.002173,
+        "example_values": [
+          "ALMOND",
+          "FOUL",
+          "FISHY",
+          "SPICY",
+          "ANISE"
+        ]
+      }
+    },
+    {
+      "name": "gill-attachment",
+      "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": 0.000297,
+        "example_values": [
+          "FREE",
+          "ATTACHED"
+        ]
+      }
+    },
+    {
+      "name": "gill-spacing",
+      "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": 0.000297,
+        "example_values": [
+          "CLOSE",
+          "CROWDED"
+        ]
+      }
+    },
+    {
+      "name": "gill-size",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 2,
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SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
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+  "task_type": "classification",
+  "input_splits": {
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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c5/c5-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/public_gate/staged_input_manifest.json

@@ -0,0 +1,472 @@
+{
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+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/runtime_result.json

@@ -0,0 +1,14 @@
+{
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+  "model": "bayesnet",
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+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/staged/bayesnet/adapter_report.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/staged/bayesnet/adapter_transforms_applied.json

@@ -0,0 +1 @@
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SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/staged/bayesnet/model_input_manifest.json

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+    },
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+    {
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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/staged/public/train.csv",
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+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/staged/public/staged_features.json

@@ -0,0 +1,117 @@
+[
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+  {
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+  },
+  {
+    "feature_name": "bruises?",
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+  },
+  {
+    "feature_name": "odor",
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+    "is_target": false
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+    "feature_name": "gill-attachment",
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+    "is_target": false
+  },
+  {
+    "feature_name": "gill-spacing",
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+    "is_target": false
+  },
+  {
+    "feature_name": "gill-size",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "gill-color",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-shape",
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+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-root",
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+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-surface-above-ring",
+    "data_type": "categorical",
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+  },
+  {
+    "feature_name": "stalk-surface-below-ring",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-color-above-ring",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-color-below-ring",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "veil-type",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "veil-color",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ring-number",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "ring-type",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "spore-print-color",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "population",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "habitat",
+    "data_type": "categorical",
+    "is_target": false
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/train_20260321_061655.log

@@ -0,0 +1,6 @@
+[2026-03-20T22:17:21.393925+0000][1][CRITICAL] Error importing TabularGoggle: No module named 'dgl'
+[2026-03-20T22:17:21.405095+0000][1][CRITICAL] module disabled: /pip_libs/synthcity/plugins/generic/plugin_goggle.py
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Dropped zero-variance column 'veil-type' (value=PARTIAL)
+[BayesNet] Training on 6732 rows, 22 cols
+[BayesNet] Model saved -> /work/output-SpecializedModels/c5/bayesnet/bayesnet-c5-20260321_061655/bayesnet_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/staged/public/staged_features.json

@@ -0,0 +1,117 @@
+[
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+  {
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+    "is_target": false
+  },
+  {
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+  },
+  {
+    "feature_name": "cap-color",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "bruises?",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "odor",
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+    "is_target": false
+  },
+  {
+    "feature_name": "gill-attachment",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "gill-spacing",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "gill-size",
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+    "is_target": false
+  },
+  {
+    "feature_name": "gill-color",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
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+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-root",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-surface-above-ring",
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+  },
+  {
+    "feature_name": "stalk-surface-below-ring",
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+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-color-above-ring",
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+    "is_target": false
+  },
+  {
+    "feature_name": "stalk-color-below-ring",
+    "data_type": "categorical",
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+  {
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+    "is_target": false
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+  {
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+  },
+  {
+    "feature_name": "spore-print-color",
+    "data_type": "categorical",
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+  },
+  {
+    "feature_name": "population",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "habitat",
+    "data_type": "categorical",
+    "is_target": false
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/staged/realtabformer/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/c5/realtabformer/rtf-c5-20260321_191627/staged/realtabformer/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/staged/realtabformer/adapter_transforms_applied.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/staged/realtabformer/model_input_manifest.json

@@ -0,0 +1,474 @@
+{
+  "dataset_id": "c5",
+  "model": "realtabformer",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "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": 2,
+        "unique_ratio": 0.000297,
+        "example_values": [
+          "EDIBLE",
+          "POISONOUS"
+        ]
+      }
+    },
+    {
+      "name": "cap-shape",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 6,
+        "unique_ratio": 0.000891,
+        "example_values": [
+          "CONVEX",
+          "BELL",
+          "FLAT",
+          "KNOBBED",
+          "CONICAL"
+        ]
+      }
+    },
+    {
+      "name": "cap-surface",
+      "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": 0.000594,
+        "example_values": [
+          "SCALY",
+          "SMOOTH",
+          "FIBROUS",
+          "GROOVES"
+        ]
+      }
+    },
+    {
+      "name": "cap-color",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 10,
+        "unique_ratio": 0.001485,
+        "example_values": [
+          "YELLOW",
+          "GRAY",
+          "BUFF",
+          "WHITE",
+          "BROWN"
+        ]
+      }
+    },
+    {
+      "name": "bruises?",
+      "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": 0.000297,
+        "example_values": [
+          "BRUISES",
+          "NO"
+        ]
+      }
+    },
+    {
+      "name": "odor",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "NONE"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_count": 8,
+        "unique_ratio": 0.002173,
+        "example_values": [
+          "ALMOND",
+          "FOUL",
+          "FISHY",
+          "SPICY",
+          "ANISE"
+        ]
+      }
+    },
+    {
+      "name": "gill-attachment",
+      "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": 0.000297,
+        "example_values": [
+          "FREE",
+          "ATTACHED"
+        ]
+      }
+    },
+    {
+      "name": "gill-spacing",
+      "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": 0.000297,
+        "example_values": [
+          "CLOSE",
+          "CROWDED"
+        ]
+      }
+    },
+    {
+      "name": "gill-size",
+      "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": 0.000297,
+        "example_values": [
+          "BROAD",
+          "NARROW"
+        ]
+      }
+    },
+    {
+      "name": "gill-color",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 12,
+        "unique_ratio": 0.001783,
+        "example_values": [
+          "BROWN",
+          "BLACK",
+          "GRAY",
+          "PINK",
+          "CHOCOLATE"
+        ]
+      }
+    },
+    {
+      "name": "stalk-shape",
+      "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": 0.000297,
+        "example_values": [
+          "ENLARGING",
+          "TAPERING"
+        ]
+      }
+    },
+    {
+      "name": "stalk-root",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "?"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.295009,
+        "unique_count": 4,
+        "unique_ratio": 0.000843,
+        "example_values": [
+          "CLUB",
+          "BULBOUS",
+          "EQUAL",
+          "ROOTED"
+        ]
+      }
+    },
+    {
+      "name": "stalk-surface-above-ring",
+      "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": 0.000594,
+        "example_values": [
+          "SMOOTH",
+          "SILKY",
+          "FIBROUS",
+          "SCALY"
+        ]
+      }
+    },
+    {
+      "name": "stalk-surface-below-ring",
+      "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": 0.000594,
+        "example_values": [
+          "SMOOTH",
+          "SILKY",
+          "FIBROUS",
+          "SCALY"
+        ]
+      }
+    },
+    {
+      "name": "stalk-color-above-ring",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 9,
+        "unique_ratio": 0.001337,
+        "example_values": [
+          "WHITE",
+          "BROWN",
+          "PINK",
+          "BUFF",
+          "GRAY"
+        ]
+      }
+    },
+    {
+      "name": "stalk-color-below-ring",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 9,
+        "unique_ratio": 0.001337,
+        "example_values": [
+          "WHITE",
+          "BUFF",
+          "PINK",
+          "BROWN",
+          "GRAY"
+        ]
+      }
+    },
+    {
+      "name": "veil-type",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 1,
+        "unique_ratio": 0.000149,
+        "example_values": [
+          "PARTIAL"
+        ]
+      }
+    },
+    {
+      "name": "veil-color",
+      "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": 0.000594,
+        "example_values": [
+          "WHITE",
+          "BROWN",
+          "ORANGE",
+          "YELLOW"
+        ]
+      }
+    },
+    {
+      "name": "ring-number",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "NONE"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.005496,
+        "unique_count": 2,
+        "unique_ratio": 0.000299,
+        "example_values": [
+          "ONE",
+          "TWO"
+        ]
+      }
+    },
+    {
+      "name": "ring-type",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [
+        "NONE"
+      ],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.005496,
+        "unique_count": 4,
+        "unique_ratio": 0.000597,
+        "example_values": [
+          "PENDANT",
+          "LARGE",
+          "EVANESCENT",
+          "FLARING"
+        ]
+      }
+    },
+    {
+      "name": "spore-print-color",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 9,
+        "unique_ratio": 0.001337,
+        "example_values": [
+          "BLACK",
+          "CHOCOLATE",
+          "BROWN",
+          "WHITE",
+          "YELLOW"
+        ]
+      }
+    },
+    {
+      "name": "population",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 6,
+        "unique_ratio": 0.000891,
+        "example_values": [
+          "NUMEROUS",
+          "SEVERAL",
+          "SOLITARY",
+          "SCATTERED",
+          "ABUNDANT"
+        ]
+      }
+    },
+    {
+      "name": "habitat",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7,
+        "unique_ratio": 0.00104,
+        "example_values": [
+          "MEADOWS",
+          "GRASSES",
+          "WOODS",
+          "URBAN",
+          "LEAVES"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/realtabformer/rtf-c5-20260321_191627/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_065749/_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/c5/tabddpm/tabddpm-c5-20260319_065749/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_065749/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_065749/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_065749/output"
+real_data_path = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_065749/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 20
+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

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_065749/data/info.json

@@ -0,0 +1,57 @@
+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 20,
+  "train_size": 6732,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19
+  ],
+  "target_col_idx": [
+    20
+  ],
+  "column_names": [
+    "cap-shape",
+    "cap-surface",
+    "cap-color",
+    "bruises?",
+    "odor",
+    "gill-attachment",
+    "gill-spacing",
+    "gill-size",
+    "gill-color",
+    "stalk-shape",
+    "stalk-root",
+    "stalk-surface-above-ring",
+    "stalk-surface-below-ring",
+    "stalk-color-above-ring",
+    "stalk-color-below-ring",
+    "veil-color",
+    "ring-number",
+    "spore-print-color",
+    "population",
+    "habitat",
+    "ring-type"
+  ],
+  "num_classes": 5
+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_065749/train_20260319_065752.log

@@ -0,0 +1,26 @@
+/opt/conda/lib/python3.11/site-packages/skorch/__init__.py:6: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
+  import pkg_resources
+[ 6  4 10  2  9  2  2  2 12  2  5  4  4  9  9  4  3  9  6  7]
+111
+{'d_in': 111, 'num_classes': 5, 'is_y_cond': True, 'rtdl_params': {'d_layers': [256, 256], 'dropout': 0.0}}
+mlp
+Traceback (most recent call last):
+  File "/workspace/tabddpm/code/_compat_run.py", line 6, in <module>
+    runpy.run_path(sys.argv[0], run_name='__main__')
+  File "<frozen runpy>", line 291, in run_path
+  File "<frozen runpy>", line 98, in _run_module_code
+  File "<frozen runpy>", line 88, in _run_code
+  File "scripts/pipeline.py", line 112, in <module>
+    main()
+  File "scripts/pipeline.py", line 48, in main
+    train(
+  File "/workspace/tabddpm/code/scripts/train.py", line 154, in train
+    trainer.run_loop()
+  File "/workspace/tabddpm/code/scripts/train.py", line 53, in run_loop
+    x, out_dict = next(self.train_iter)
+                  ^^^^^^^^^^^^^^^^^^^^^
+  File "/workspace/tabddpm/code/lib/data.py", line 605, in prepare_fast_dataloader
+    y = torch.from_numpy(D.y[split])
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
+[TabDDPM] Training, config=/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_065749/config.toml

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_223500/_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/c5/tabddpm/tabddpm-c5-20260319_223500/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_223500/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_223500/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_223500/output"
+real_data_path = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_223500/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 20
+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

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_223500/data/info.json

@@ -0,0 +1,57 @@
+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 20,
+  "train_size": 6732,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19
+  ],
+  "target_col_idx": [
+    20
+  ],
+  "column_names": [
+    "cap-shape",
+    "cap-surface",
+    "cap-color",
+    "bruises?",
+    "odor",
+    "gill-attachment",
+    "gill-spacing",
+    "gill-size",
+    "gill-color",
+    "stalk-shape",
+    "stalk-root",
+    "stalk-surface-above-ring",
+    "stalk-surface-below-ring",
+    "stalk-color-above-ring",
+    "stalk-color-below-ring",
+    "veil-color",
+    "ring-number",
+    "spore-print-color",
+    "population",
+    "habitat",
+    "ring-type"
+  ],
+  "num_classes": 5
+}

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_223500/train_20260319_223503.log

@@ -0,0 +1,26 @@
+/opt/conda/lib/python3.11/site-packages/skorch/__init__.py:6: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
+  import pkg_resources
+[ 6  4 10  2  9  2  2  2 12  2  5  4  4  9  9  4  3  9  6  7]
+111
+{'d_in': 111, 'num_classes': 5, 'is_y_cond': True, 'rtdl_params': {'d_layers': [256, 256], 'dropout': 0.0}}
+mlp
+Traceback (most recent call last):
+  File "/workspace/tabddpm/code/_compat_run.py", line 6, in <module>
+    runpy.run_path(sys.argv[0], run_name='__main__')
+  File "<frozen runpy>", line 291, in run_path
+  File "<frozen runpy>", line 98, in _run_module_code
+  File "<frozen runpy>", line 88, in _run_code
+  File "scripts/pipeline.py", line 112, in <module>
+    main()
+  File "scripts/pipeline.py", line 48, in main
+    train(
+  File "/workspace/tabddpm/code/scripts/train.py", line 154, in train
+    trainer.run_loop()
+  File "/workspace/tabddpm/code/scripts/train.py", line 53, in run_loop
+    x, out_dict = next(self.train_iter)
+                  ^^^^^^^^^^^^^^^^^^^^^
+  File "/workspace/tabddpm/code/lib/data.py", line 605, in prepare_fast_dataloader
+    y = torch.from_numpy(D.y[split])
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+TypeError: can't convert np.ndarray of type numpy.object_. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint64, uint32, uint16, uint8, and bool.
+[TabDDPM] Training, config=/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260319_223500/config.toml

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/_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 1000 rows")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/config.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/c5/tabddpm/tabddpm-c5-20260321_071003"
+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/c5/tabddpm/tabddpm-c5-20260321_071003/tabddpm-c5-1000-20260321_071812.csv", index=False)
+    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/tabddpm-c5-1000-20260321_071812.csv")
+else:
+    print("[TabDDPM] WARNING: No output .npy files found")
+    sys.exit(1)

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/_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/c5/tabddpm/tabddpm-c5-20260321_071003/config.toml")
+ret = subprocess.run(
+    [sys.executable, wrapper, "scripts/pipeline.py",
+     "--config", "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/config.toml",
+     "--train"],
+    cwd=tabddpm_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print("[TabDDPM] Training complete")

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/config.toml

@@ -0,0 +1,39 @@
+seed = 0
+parent_dir = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/output"
+real_data_path = "/work/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/data"
+model_type = "mlp"
+num_numerical_features = 0
+device = "cuda:0"
+
+[model_params]
+d_in = 22
+num_classes = 2
+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

SynthesizePipeline_Archive/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260321_071003/data/info.json

@@ -0,0 +1,61 @@
+{
+  "name": "benchmark_dataset",
+  "task_type": "multiclass",
+  "n_num_features": 0,
+  "n_cat_features": 22,
+  "train_size": 6732,
+  "num_col_idx": [],
+  "cat_col_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    21
+  ],
+  "target_col_idx": [
+    22
+  ],
+  "column_names": [
+    "cap-shape",
+    "cap-surface",
+    "cap-color",
+    "bruises?",
+    "odor",
+    "gill-attachment",
+    "gill-spacing",
+    "gill-size",
+    "gill-color",
+    "stalk-shape",
+    "stalk-root",
+    "stalk-surface-above-ring",
+    "stalk-surface-below-ring",
+    "stalk-color-above-ring",
+    "stalk-color-below-ring",
+    "veil-type",
+    "veil-color",
+    "ring-number",
+    "ring-type",
+    "spore-print-color",
+    "population",
+    "habitat",
+    "class"
+  ],
+  "num_classes": 2
+}