Add files using upload-large-folder tool

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=1000)
+syn.to_csv("/work/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/arf-c12-1000-20260320_070726.csv", index=False)
+print(f"[ARF] Generated 1000 rows -> /work/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/arf-c12-1000-20260320_070726.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/DatasetNew/c12/c12-train.csv")
+df = df.dropna(axis=1, how="all")
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/train_20260320_042602.log

@@ -0,0 +1,7 @@
+[ARF] Training on 2623 rows, 1559 cols
+Initial accuracy is 0.9584445291650782
+Iteration number 1 reached accuracy of 0.5886389630194434.
+Iteration number 2 reached accuracy of 0.5848265345024781.
+Iteration number 3 reached accuracy of 0.5802516202821197.
+Iteration number 4 reached accuracy of 0.5922607701105604.
+[ARF] Model saved -> /work/output-SpecializedModels/c12/arf/arf-c12-20260320_042602/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/_arf_generate.py

@@ -0,0 +1,6 @@
+import pickle
+with open("/work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=2623)
+syn.to_csv("/work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf-c12-2623-20260330_065419.csv", index=False)
+print(f"[ARF] Generated 2623 rows -> /work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf-c12-2623-20260330_065419.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/_arf_train.py

@@ -0,0 +1,19 @@
+import pickle
+import pandas as pd
+from arfpy import arf
+
+df = pd.read_csv("/work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c12",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-train.csv",
+      "exists": true,
+      "size": 32714405,
+      "sha256": "33bb117347198c71163d5f3264264432732b63d98589da2700f184e3a81f4456"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-val.csv",
+      "exists": true,
+      "size": 4101144,
+      "sha256": "378b56871d42f5b6a36b250994ed5784943d07f1e3d5b07ec1e3703bcb712c82"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-test.csv",
+      "exists": true,
+      "size": 4125941,
+      "sha256": "f92d9e4c6743fbcff792d530af9fc93e6107201cc0e1fabf4c41efa8b51a609e"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c12/c12-dataset_profile.json",
+      "exists": true,
+      "size": 503575,
+      "sha256": "e655d13d30e13553c67ee13ddee5a9bdf512fbeb7d7b9d213f59d67b44e4d5d9"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c12/c12-dataset_contract_v1.json",
+      "exists": true,
+      "size": 658208,
+      "sha256": "2ec2c7c7dbaf914604b2d22474b10d3d2ec64e0a521b78930c5a87a4e5a4e0fb"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c12",
+  "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/c12/c12-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c12",
+  "model": "arf",
+  "run_id": "arf-c12-20260321_133736",
+  "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/c12/arf/arf-c12-20260321_133736/arf-c12-2623-20260330_065419.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/staged/arf/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/staged/arf/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/staged/arf/adapter_transforms_applied.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/train_20260321_133751.log

@@ -0,0 +1,7 @@
+[ARF] Training on 2623 rows, 1559 cols
+Initial accuracy is 0.9563476934807472
+Iteration number 1 reached accuracy of 0.6147540983606558.
+Iteration number 2 reached accuracy of 0.5834921845215402.
+Iteration number 3 reached accuracy of 0.5684330918795273.
+Iteration number 4 reached accuracy of 0.5831109416698437.
+[ARF] Model saved -> /work/output-SpecializedModels/c12/arf/arf-c12-20260321_133736/arf_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260318_034213/_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/c12/bayesnet/bayesnet-c12-20260318_034213/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=2623).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260318_034213/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/c12/bayesnet/bayesnet-c12-20260318_034213/bayesnet-c12-2623-20260318_043436.csv", index=False)
+print(f"[BayesNet] Generated 2623 rows -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260318_034213/bayesnet-c12-2623-20260318_043436.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260318_034213/_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/c12/c12-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/c12/bayesnet/bayesnet-c12-20260318_034213/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/c12/bayesnet/bayesnet-c12-20260318_034213/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260318_034213/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260318_034213/gen_20260318_043436.log

@@ -0,0 +1,12 @@
+03/17/2026 20:35:07:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:08:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:08:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:08:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:08:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:35:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:35:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:35:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:35:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/17/2026 20:35:35: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] Generated 2623 rows -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260318_034213/bayesnet-c12-2623-20260318_043436.csv

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/_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/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=2623).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/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/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet-c12-2623-20260330_065421.csv", index=False)
+print(f"[BayesNet] Generated 2623 rows -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet-c12-2623-20260330_065421.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/_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/c12/bayesnet/bayesnet-c12-20260321_062527/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/c12/bayesnet/bayesnet-c12-20260321_062527/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/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/gen_20260321_071907.log

@@ -0,0 +1,12 @@
+03/20/2026 23:19:38:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:38:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:38:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:38:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:38:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:58:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:58:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:58:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:58:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/20/2026 23:19:58: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] Generated 1000 rows -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet-c12-1000-20260321_071907.csv

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/gen_20260330_065421.log

@@ -0,0 +1,12 @@
+03/29/2026 22:54:53:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:53:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:53:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:53:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:54:53:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:55:20:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:55:20:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:55:20:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:55:20:WARNING:Probability values don't exactly sum to 1. Differ by: -2.220446049250313e-16. Adjusting values.
+03/29/2026 22:55:20: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] Generated 2623 rows -> /work/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet-c12-2623-20260330_065421.csv

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c12",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-train.csv",
+      "exists": true,
+      "size": 32714405,
+      "sha256": "33bb117347198c71163d5f3264264432732b63d98589da2700f184e3a81f4456"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-val.csv",
+      "exists": true,
+      "size": 4101144,
+      "sha256": "378b56871d42f5b6a36b250994ed5784943d07f1e3d5b07ec1e3703bcb712c82"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-test.csv",
+      "exists": true,
+      "size": 4125941,
+      "sha256": "f92d9e4c6743fbcff792d530af9fc93e6107201cc0e1fabf4c41efa8b51a609e"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c12/c12-dataset_profile.json",
+      "exists": true,
+      "size": 503575,
+      "sha256": "e655d13d30e13553c67ee13ddee5a9bdf512fbeb7d7b9d213f59d67b44e4d5d9"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c12/c12-dataset_contract_v1.json",
+      "exists": true,
+      "size": 658208,
+      "sha256": "2ec2c7c7dbaf914604b2d22474b10d3d2ec64e0a521b78930c5a87a4e5a4e0fb"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c12",
+  "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/c12/c12-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c12",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c12-20260321_062527",
+  "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/c12/bayesnet/bayesnet-c12-20260321_062527/bayesnet-c12-2623-20260330_065421.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/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/c12/bayesnet/bayesnet-c12-20260321_062527/staged/bayesnet/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/bayesnet/bayesnet-c12-20260321_062527/staged/bayesnet/adapter_transforms_applied.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c12/ctgan/ctgan-c12-20260320_060824/models_300epochs/train_20260320_060824.log

@@ -0,0 +1,4 @@
+/opt/conda/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:782: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py:841: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:270.)
+  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass

SynthesizePipeline_Archive/output-SpecializedModels/c12/ctgan/ctgan-c12-20260321_202808/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c12",
+  "model": "ctgan",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-train.csv",
+      "exists": true,
+      "size": 32714405,
+      "sha256": "33bb117347198c71163d5f3264264432732b63d98589da2700f184e3a81f4456"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-val.csv",
+      "exists": true,
+      "size": 4101144,
+      "sha256": "378b56871d42f5b6a36b250994ed5784943d07f1e3d5b07ec1e3703bcb712c82"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-test.csv",
+      "exists": true,
+      "size": 4125941,
+      "sha256": "f92d9e4c6743fbcff792d530af9fc93e6107201cc0e1fabf4c41efa8b51a609e"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c12/c12-dataset_profile.json",
+      "exists": true,
+      "size": 503575,
+      "sha256": "e655d13d30e13553c67ee13ddee5a9bdf512fbeb7d7b9d213f59d67b44e4d5d9"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c12/c12-dataset_contract_v1.json",
+      "exists": true,
+      "size": 658208,
+      "sha256": "2ec2c7c7dbaf914604b2d22474b10d3d2ec64e0a521b78930c5a87a4e5a4e0fb"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c12/ctgan/ctgan-c12-20260321_202808/models_300epochs/train_20260321_202823.log

@@ -0,0 +1,4 @@
+/opt/conda/lib/python3.10/site-packages/joblib/externals/loky/process_executor.py:782: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/torch/autograd/graph.py:841: UserWarning: Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at /pytorch/aten/src/ATen/cuda/CublasHandlePool.cpp:270.)
+  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass

SynthesizePipeline_Archive/output-SpecializedModels/c12/ctgan/ctgan-c12-20260321_202808/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c12",
+  "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/c12/c12-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c12/c12-test.csv"
+  }
+}