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SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/_bayesnet_generate.py

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+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache...")
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+import pickle, json as _json
+with open("/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/bayesnet_model.pkl", "rb") as f:
+    plugin = pickle.load(f)
+syn = plugin.generate(count=7045).dataframe()
+
+# Restore zero-variance columns that were dropped during training
+const_path = "/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/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/c17/bayesnet/bayesnet-c17-20260318_043952/bayesnet-c17-7045-20260318_044114.csv", index=False)
+print(f"[BayesNet] Generated 7045 rows -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/bayesnet-c17-7045-20260318_044114.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/_bayesnet_train.py

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+import subprocess, sys, os
+
+pip_libs = "/pip_libs"
+sys.path.insert(0, pip_libs)
+os.environ["PYTHONPATH"] = pip_libs + os.pathsep + os.environ.get("PYTHONPATH", "")
+
+def _ensure_deps():
+    try:
+        import synthcity
+    except ModuleNotFoundError:
+        print("[BayesNet] synthcity not found - installing to cache (first run, may take minutes)...")
+        # Install synthcity with numpy<2 to avoid conflicts
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install",
+             "--target", pip_libs, "synthcity==0.2.12", "numpy<2", "-q"],
+            check=True
+        )
+        # Remove torch/torchvision from pip_libs to avoid shadowing system versions
+        import shutil, glob
+        for pat in ["torch", "torch-*", "torchvision", "torchvision-*",
+                    "torchvision.libs", "torchgen", "nvidia*", "triton*"]:
+            for p in glob.glob(os.path.join(pip_libs, pat)):
+                if os.path.isdir(p): shutil.rmtree(p)
+                else: os.remove(p)
+        if pip_libs not in sys.path:
+            sys.path.insert(0, pip_libs)
+
+_ensure_deps()
+
+from synthcity.plugins import Plugins
+import pickle
+import pandas as pd
+from synthcity.plugins.core.dataloader import GenericDataLoader
+
+df = pd.read_csv("/work/DatasetNew/c17/c17-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/c17/bayesnet/bayesnet-c17-20260318_043952/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/c17/bayesnet/bayesnet-c17-20260318_043952/bayesnet_model.pkl", "wb") as f:
+    pickle.dump(plugin, f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/gen_20260318_044114.log

@@ -0,0 +1,2 @@
+[KeOps] Warning : CUDA libraries not found or could not be loaded; Switching to CPU only.
+[BayesNet] Generated 7045 rows -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/bayesnet-c17-7045-20260318_044114.csv

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/train_20260318_043952.log

@@ -0,0 +1,71 @@
+[2026-03-17T20:40:17.496473+0000][1][CRITICAL] Error importing TabularGoggle: No module named 'dgl'
+[2026-03-17T20:40:17.507144+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] Training on 7045 rows, 12 cols
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/pip_libs/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+[BayesNet] Model saved -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260318_043952/bayesnet_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/_bayesnet_generate.py

@@ -0,0 +1,104 @@
+
+import pickle
+import subprocess
+import sys
+import warnings
+
+import numpy as np
+import pandas as pd
+from pgmpy.sampling import BayesianModelSampling
+
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+def _ensure_cloudpickle():
+    try:
+        import cloudpickle  # noqa: F401
+    except ModuleNotFoundError:
+        subprocess.check_call(
+            [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"],
+        )
+
+_ensure_cloudpickle()
+
+with open("/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet_model.pkl", "rb") as f:
+    bundle = pickle.load(f)
+
+network = bundle["network"]
+inverse = bundle["inverse"]
+cols = bundle["column_order"]
+integer_columns = set(bundle.get("integer_columns") or [])
+full_order = bundle.get("full_column_order") or cols
+const_cols = bundle.get("const_cols") or {}
+
+num_rows = int(7045)
+sampler = BayesianModelSampling(network)
+raw = sampler.forward_sample(size=num_rows, show_progress=False)
+raw = raw.reset_index(drop=True)
+if len(raw) > num_rows:
+    raw = raw.iloc[:num_rows]
+_tries = 0
+while len(raw) < num_rows and _tries < 64:
+    _tries += 1
+    nextra = min(10000, num_rows - len(raw))
+    more = sampler.forward_sample(size=max(nextra, 1), show_progress=False)
+    more = more.reset_index(drop=True)
+    if len(more) == 0:
+        break
+    raw = pd.concat([raw, more], ignore_index=True)
+    if len(raw) > num_rows:
+        raw = raw.iloc[:num_rows]
+
+out = pd.DataFrame(index=raw.index)
+rng = np.random.default_rng()
+
+for c in cols:
+    if c in inverse["categorical"]:
+        levels = inverse["categorical"][c]
+        idx = raw[c].astype(int).to_numpy()
+        idx = np.clip(idx, 0, max(0, len(levels) - 1))
+        out[c] = [levels[i] for i in idx]
+    else:
+        edges = np.asarray(inverse["continuous"][c], dtype=float)
+        if edges.size < 2:
+            out[c] = 0.0
+        else:
+            nbin = edges.size - 1
+            res = []
+            for k in raw[c].astype(int).to_numpy():
+                k = int(k)
+                if k < 0:
+                    k = 0
+                if k >= nbin:
+                    k = nbin - 1
+                lo, hi = float(edges[k]), float(edges[k + 1])
+                if hi < lo:
+                    lo, hi = hi, lo
+                v = rng.uniform(lo, hi)
+                if c in integer_columns:
+                    v = int(round(v))
+                res.append(v)
+            out[c] = res
+
+final = pd.DataFrame(index=out.index)
+for c in full_order:
+    if c in const_cols:
+        final[c] = const_cols[c]
+    elif c in out.columns:
+        final[c] = out[c]
+
+dtypes = bundle.get("original_dtypes") or {}
+for c, dts in dtypes.items():
+    if c not in final.columns:
+        continue
+    try:
+        if "int" in dts:
+            final[c] = pd.to_numeric(final[c], errors="coerce").astype("Int64")
+        elif "float" in dts:
+            final[c] = pd.to_numeric(final[c], errors="coerce")
+    except Exception:
+        pass
+
+if len(final) != num_rows:
+    final = final.iloc[:num_rows].copy()
+final.to_csv("/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet-c17-7045-20260422_060304.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet-c17-7045-20260422_060304.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/_bayesnet_train.py

@@ -0,0 +1,118 @@
+
+import json
+import pickle
+import subprocess
+import sys
+import warnings
+
+import numpy as np
+import pandas as pd
+from pgmpy.estimators import TreeSearch
+from pgmpy.models import DiscreteBayesianNetwork
+warnings.filterwarnings("ignore", category=FutureWarning)
+
+def _ensure_cloudpickle():
+    try:
+        import cloudpickle  # noqa: F401
+    except ModuleNotFoundError:
+        subprocess.check_call(
+            [sys.executable, "-m", "pip", "install", "--quiet", "cloudpickle"],
+        )
+
+_ensure_cloudpickle()
+
+with open("/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet_coltypes.json", "r", encoding="utf-8") as _f:
+    colmeta = json.load(_f)
+integer_columns = set(colmeta.get("integer_columns") or [])
+
+df = pd.read_csv("/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/train.csv")
+df = df.dropna(axis=1, how="all")
+full_column_order = list(df.columns)
+
+const_cols = {}
+for col in list(df.columns):
+    if df[col].nunique(dropna=True) <= 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}'")
+
+const_path = "/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet_model.pkl".replace("bayesnet_model.pkl", "const_cols.json")
+with open(const_path, "w", encoding="utf-8") as _f:
+    json.dump({k: str(v) for k, v in const_cols.items()}, _f)
+
+inverse = {"categorical": {}, "continuous": {}}
+enc = pd.DataFrame(index=df.index)
+_n_samples = len(df)
+_n_plan = sum(
+    1 for e in colmeta["columns"] if str(e.get("name", "")) in df.columns
+)
+max_bins = 10
+if _n_plan > 35 or _n_samples > 200000:
+    max_bins = 5
+if _n_plan > 55:
+    max_bins = 4
+print(f"[BayesNet] max_bins={max_bins} (cols_in_df={_n_plan}, rows={_n_samples})")
+
+for entry in colmeta["columns"]:
+    name = entry["name"]
+    if name not in df.columns:
+        continue
+    kind = entry["type"]
+    s = df[name]
+    if kind == "categorical":
+        uniques = sorted(s.dropna().unique(), key=lambda x: str(x))
+        mapping = {str(v): i for i, v in enumerate(uniques)}
+        inverse["categorical"][name] = [uniques[i] for i in range(len(uniques))]
+        enc[name] = s.map(lambda x, m=mapping: m.get(str(x), 0)).astype(int)
+    else:
+        s_num = pd.to_numeric(s, errors="coerce")
+        nu = int(s_num.nunique(dropna=True))
+        q = min(max_bins, max(2, nu))
+        if nu < 2:
+            enc[name] = np.zeros(len(s_num), dtype=int)
+            lo, hi = float(s_num.min()), float(s_num.max())
+            inverse["continuous"][name] = [lo, hi]
+        else:
+            try:
+                _, bins = pd.qcut(
+                    s_num, q=q, retbins=True, duplicates="drop"
+                )
+            except Exception:
+                med = float(s_num.median())
+                s2 = s_num.fillna(med)
+                _, bins = pd.qcut(
+                    s2, q=min(q, 3), retbins=True, duplicates="drop"
+                )
+            bins = np.asarray(bins, dtype=float)
+            lab = pd.cut(
+                s_num, bins=bins, labels=False, include_lowest=True
+            )
+            enc[name] = lab.fillna(0).astype(int)
+            inverse["continuous"][name] = bins.tolist()
+
+print(f"[BayesNet] Training on {len(enc)} rows, {len(enc.columns)} cols (encoded)")
+
+enc_struct = enc
+if len(enc) > 25000:
+    enc_struct = enc.sample(n=25000, random_state=0, replace=False)
+    print(f"[BayesNet] TreeSearch on {len(enc_struct)} rows (subsample; full n={len(enc)})")
+dag = TreeSearch(enc_struct).estimate(show_progress=False)
+for col in enc.columns:
+    if col not in dag.nodes():
+        dag.add_node(col)
+        print(f"[BayesNet] Added isolated node to DAG: {col}")
+network = DiscreteBayesianNetwork(dag)
+network.fit(enc)
+
+bundle = {
+    "network": network,
+    "inverse": inverse,
+    "column_order": list(enc.columns),
+    "full_column_order": full_column_order,
+    "integer_columns": list(integer_columns),
+    "original_dtypes": {c: str(df[c].dtype) for c in enc.columns},
+    "const_cols": const_cols,
+}
+with open("/work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet_model.pkl")

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet_coltypes.json

@@ -0,0 +1,53 @@
+{
+  "columns": [
+    {
+      "name": "show_id",
+      "type": "categorical"
+    },
+    {
+      "name": "type",
+      "type": "categorical"
+    },
+    {
+      "name": "title",
+      "type": "categorical"
+    },
+    {
+      "name": "director",
+      "type": "categorical"
+    },
+    {
+      "name": "cast",
+      "type": "categorical"
+    },
+    {
+      "name": "country",
+      "type": "categorical"
+    },
+    {
+      "name": "date_added",
+      "type": "categorical"
+    },
+    {
+      "name": "release_year",
+      "type": "continuous"
+    },
+    {
+      "name": "rating",
+      "type": "categorical"
+    },
+    {
+      "name": "duration",
+      "type": "categorical"
+    },
+    {
+      "name": "listed_in",
+      "type": "categorical"
+    },
+    {
+      "name": "description",
+      "type": "categorical"
+    }
+  ],
+  "integer_columns": []
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/gen_20260422_060304.log

@@ -0,0 +1,48 @@
+
+==========
+== CUDA ==
+==========
+
+CUDA Version 12.8.1
+
+Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
+
+WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
+   Use the NVIDIA Container Toolkit to start this container with GPU support; see
+   https://docs.nvidia.com/datacenter/cloud-native/ .
+
+/usr/local/lib/python3.10/dist-packages/pgmpy/estimators/__init__.py:4: FutureWarning: `pgmpy.estimators.StructureScore` is deprecated and will be removed in a future release. Use `pgmpy.structure_score` instead.
+  from .StructureScore import (
+ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+synthcity 0.2.12 requires arfpy, which is not installed.
+synthcity 0.2.12 requires be-great>=0.0.5; python_version >= "3.9", which is not installed.
+synthcity 0.2.12 requires decaf-synthetic-data>=0.1.6, which is not installed.
+synthcity 0.2.12 requires fastai<2.8, which is not installed.
+synthcity 0.2.12 requires fastcore<1.8, which is not installed.
+synthcity 0.2.12 requires fflows, which is not installed.
+synthcity 0.2.12 requires geomloss, which is not installed.
+synthcity 0.2.12 requires importlib-metadata, which is not installed.
+synthcity 0.2.12 requires lifelines<0.30.0,>=0.29.0, which is not installed.
+synthcity 0.2.12 requires monai, which is not installed.
+synthcity 0.2.12 requires nflows>=0.14, which is not installed.
+synthcity 0.2.12 requires opacus>=1.3, which is not installed.
+synthcity 0.2.12 requires pycox, which is not installed.
+synthcity 0.2.12 requires pykeops, which is not installed.
+synthcity 0.2.12 requires redis, which is not installed.
+synthcity 0.2.12 requires shap, which is not installed.
+synthcity 0.2.12 requires tenacity, which is not installed.
+synthcity 0.2.12 requires tsai; python_version > "3.7", which is not installed.
+synthcity 0.2.12 requires xgbse>=0.3.1, which is not installed.
+synthcity 0.2.12 requires networkx<3.0,>2.0, but you have networkx 3.4.2 which is incompatible.
+synthcity 0.2.12 requires numpy<2.0,>=1.20, but you have numpy 2.2.6 which is incompatible.
+synthcity 0.2.12 requires pgmpy<1.0, but you have pgmpy 1.1.0 which is incompatible.
+synthcity 0.2.12 requires torch<2.3,>=2.1, but you have torch 2.8.0+cu128 which is incompatible.
+synthcity 0.2.12 requires xgboost<3.0.0, but you have xgboost 3.2.0 which is incompatible.
+WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
+[BayesNet] Generated 7045 rows (requested 7045) -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet-c17-7045-20260422_060304.csv

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c17",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-train.csv",
+      "exists": true,
+      "size": 2726614,
+      "sha256": "b77d66258f90989c221df405c960fb64e4e947a5369ced2b884002e17e47e1e9"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-val.csv",
+      "exists": true,
+      "size": 342007,
+      "sha256": "d98c48176aedfd33341199220483be09f753ac63f2a63e829d0835286ab577f3"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-test.csv",
+      "exists": true,
+      "size": 339976,
+      "sha256": "e067ef64b2334774f8cc291445c6723301cd374cde1a3db26a51af8da46bda0a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c17/c17-dataset_profile.json",
+      "exists": true,
+      "size": 6842,
+      "sha256": "75a4478c7d058e9e4753c49ecfa5e7e7764263a853380d2bacbf48401854370e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c17/c17-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7632,
+      "sha256": "26a27c28d1bb9de6b75ff00efa045708e5a23ea264abb037a6ba47d7e55027fd"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,256 @@
+{
+  "dataset_id": "c17",
+  "target_column": "type",
+  "task_type": "classification",
+  "columns": [
+    {
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+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
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+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
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+    },
+    {
+      "name": "type",
+      "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": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
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+      "missing_tokens": [],
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+      "impute_strategy": "keep_raw",
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+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
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+          "Gina Yashere",
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+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
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+    },
+    {
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+          "India"
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+          "December 31, 2018",
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+          "TV-MA",
+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
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+          "92 min",
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+        "unique_ratio": 0.068701,
+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
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+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c17",
+  "status": "pass",
+  "checks": [
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+      "status": "pass"
+    },
+    {
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+      "status": "pass"
+    },
+    {
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+      "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": "type",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/public_gate/staged_input_manifest.json

@@ -0,0 +1,261 @@
+{
+  "dataset_id": "c17",
+  "target_column": "type",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/public_gate/public_gate_report.json",
+  "column_schema": [
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+    },
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+        "unique_ratio": 0.010504,
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+      "nullable": true,
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+          "R",
+          "PG",
+          "TV-PG"
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+    {
+      "name": "duration",
+      "role": "feature",
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+      "name": "listed_in",
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+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7026,
+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c17",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c17-20260422_060152",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "success",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet-c17-7045-20260422_060304.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet_model.pkl"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/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/c17/bayesnet/bayesnet-c17-20260422_060152/staged/bayesnet/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/bayesnet/adapter_transforms_applied.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,263 @@
+{
+  "dataset_id": "c17",
+  "model": "bayesnet",
+  "target_column": "type",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "show_id",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "s4961",
+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "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": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.299787,
+        "unique_count": 3784,
+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095387,
+        "unique_count": 6179,
+        "unique_ratio": 0.969559,
+        "example_values": [
+          "Noël Wells, Ben Schwartz, Joe Pantoliano, Annie Potts, Rahul Kohli, Kristin Bauer van Straten, David Walton, Leonardo Nam, Kate Berlant",
+          "Gina Yashere",
+          "Javid Abdelmoneim",
+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
+      }
+    },
+    {
+      "name": "country",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095529,
+        "unique_count": 621,
+        "unique_ratio": 0.097458,
+        "example_values": [
+          "United States",
+          "Denmark, United States",
+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
+      }
+    },
+    {
+      "name": "date_added",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.001136,
+        "unique_count": 1593,
+        "unique_ratio": 0.226375,
+        "example_values": [
+          "March 30, 2018",
+          "September 30, 2016",
+          "December 31, 2018",
+          "August 1, 2017",
+          "June 18, 2020"
+        ]
+      }
+    },
+    {
+      "name": "release_year",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 74,
+        "unique_ratio": 0.010504,
+        "example_values": [
+          "2018",
+          "2016",
+          "2013",
+          "1963",
+          "2021"
+        ]
+      }
+    },
+    {
+      "name": "rating",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.000568,
+        "unique_count": 15,
+        "unique_ratio": 0.00213,
+        "example_values": [
+          "TV-MA",
+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
+        ]
+      }
+    },
+    {
+      "name": "duration",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000142,
+        "unique_count": 211,
+        "unique_ratio": 0.029955,
+        "example_values": [
+          "78 min",
+          "92 min",
+          "68 min",
+          "58 min",
+          "194 min"
+        ]
+      }
+    },
+    {
+      "name": "listed_in",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 484,
+        "unique_ratio": 0.068701,
+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7026,
+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/staged/public/staged_features.json

@@ -0,0 +1,62 @@
+[
+  {
+    "feature_name": "show_id",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "type",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "title",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "director",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "cast",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "country",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "date_added",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "release_year",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "rating",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "duration",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "listed_in",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "description",
+    "data_type": "ID",
+    "is_target": false
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/train_20260422_060153.log

@@ -0,0 +1,138 @@
+
+==========
+== CUDA ==
+==========
+
+CUDA Version 12.8.1
+
+Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+This container image and its contents are governed by the NVIDIA Deep Learning Container License.
+By pulling and using the container, you accept the terms and conditions of this license:
+https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
+
+A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
+
+WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
+   Use the NVIDIA Container Toolkit to start this container with GPU support; see
+   https://docs.nvidia.com/datacenter/cloud-native/ .
+
+/usr/local/lib/python3.10/dist-packages/pgmpy/estimators/__init__.py:4: FutureWarning: `pgmpy.estimators.StructureScore` is deprecated and will be removed in a future release. Use `pgmpy.structure_score` instead.
+  from .StructureScore import (
+ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+synthcity 0.2.12 requires arfpy, which is not installed.
+synthcity 0.2.12 requires be-great>=0.0.5; python_version >= "3.9", which is not installed.
+synthcity 0.2.12 requires decaf-synthetic-data>=0.1.6, which is not installed.
+synthcity 0.2.12 requires fastai<2.8, which is not installed.
+synthcity 0.2.12 requires fastcore<1.8, which is not installed.
+synthcity 0.2.12 requires fflows, which is not installed.
+synthcity 0.2.12 requires geomloss, which is not installed.
+synthcity 0.2.12 requires importlib-metadata, which is not installed.
+synthcity 0.2.12 requires lifelines<0.30.0,>=0.29.0, which is not installed.
+synthcity 0.2.12 requires monai, which is not installed.
+synthcity 0.2.12 requires nflows>=0.14, which is not installed.
+synthcity 0.2.12 requires opacus>=1.3, which is not installed.
+synthcity 0.2.12 requires pycox, which is not installed.
+synthcity 0.2.12 requires pykeops, which is not installed.
+synthcity 0.2.12 requires redis, which is not installed.
+synthcity 0.2.12 requires shap, which is not installed.
+synthcity 0.2.12 requires tenacity, which is not installed.
+synthcity 0.2.12 requires tsai; python_version > "3.7", which is not installed.
+synthcity 0.2.12 requires xgbse>=0.3.1, which is not installed.
+synthcity 0.2.12 requires networkx<3.0,>2.0, but you have networkx 3.4.2 which is incompatible.
+synthcity 0.2.12 requires numpy<2.0,>=1.20, but you have numpy 2.2.6 which is incompatible.
+synthcity 0.2.12 requires pgmpy<1.0, but you have pgmpy 1.1.0 which is incompatible.
+synthcity 0.2.12 requires torch<2.3,>=2.1, but you have torch 2.8.0+cu128 which is incompatible.
+synthcity 0.2.12 requires xgboost<3.0.0, but you have xgboost 3.2.0 which is incompatible.
+WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
+[BayesNet] max_bins=10 (cols_in_df=12, rows=7045)
+[BayesNet] Training on 7045 rows, 12 cols (encoded)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:49: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_label = type_of_target(labels_true)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+/usr/local/lib/python3.10/dist-packages/sklearn/metrics/cluster/_supervised.py:50: UserWarning: The number of unique classes is greater than 50% of the number of samples. `y` could represent a regression problem, not a classification problem.
+  type_pred = type_of_target(labels_pred)
+[BayesNet] Model saved -> /work/output-SpecializedModels/c17/bayesnet/bayesnet-c17-20260422_060152/bayesnet_model.pkl

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/_tabpfgen_generate.py

@@ -0,0 +1,68 @@
+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/train.csv")
+target_col = "type"
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+# --- Label-encode categorical / object columns ---
+cat_encodings = {}  # col -> list of unique values (index = code)
+for col in feature_cols:
+    if df[col].dtype == object or str(df[col].dtype) == 'category':
+        cats = sorted(df[col].dropna().unique().tolist(), key=str)
+        cat_map = {v: i for i, v in enumerate(cats)}
+        df[col] = df[col].map(cat_map).astype(float)
+        cat_encodings[col] = cats
+        print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)")
+
+# Encode target if categorical
+target_cats = None
+if df[target_col].dtype == object or str(df[target_col].dtype) == 'category':
+    cats = sorted(df[target_col].dropna().unique().tolist(), key=str)
+    t_map = {v: i for i, v in enumerate(cats)}
+    df[target_col] = df[target_col].map(t_map).astype(float)
+    target_cats = cats
+    print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)")
+
+X = df[feature_cols].values.astype(np.float32)
+y = df[target_col].values
+
+# Handle NaN
+for i in range(X.shape[1]):
+    col_vals = X[:, i]
+    mask = np.isnan(col_vals)
+    if mask.any():
+        mean_val = np.nanmean(col_vals)
+        X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0
+
+gen = TabPFGen(
+    n_sgld_steps=1000,
+    sgld_step_size=0.01,
+    sgld_noise_scale=0.01,
+    device="auto",
+)
+
+print(f"[TabPFGen] Generating 7045 rows via generate_classification")
+X_syn, y_syn = gen.generate_classification(X, y, n_samples=7045)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+# --- Inverse label-encoding for categorical columns ---
+for col, cats in cat_encodings.items():
+    # Round to nearest integer index, clamp to valid range
+    codes = np.round(syn_df[col].values).astype(int)
+    codes = np.clip(codes, 0, len(cats) - 1)
+    syn_df[col] = [cats[c] for c in codes]
+
+if target_cats is not None:
+    codes = np.round(syn_df[target_col].values).astype(int)
+    codes = np.clip(codes, 0, len(target_cats) - 1)
+    syn_df[target_col] = [target_cats[c] for c in codes]
+
+syn_df = syn_df[list(df.columns)]
+syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/tabpfgen-c17-7045-20260422_070321.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/tabpfgen-c17-7045-20260422_070321.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/gen_20260422_070321.log

@@ -0,0 +1,24 @@
+[TabPFGen] Label-encoded 'show_id' (7045 categories)
+[TabPFGen] Label-encoded 'title' (7044 categories)
+[TabPFGen] Label-encoded 'director' (3784 categories)
+[TabPFGen] Label-encoded 'cast' (6179 categories)
+[TabPFGen] Label-encoded 'country' (621 categories)
+[TabPFGen] Label-encoded 'date_added' (1593 categories)
+[TabPFGen] Label-encoded 'rating' (15 categories)
+[TabPFGen] Label-encoded 'duration' (211 categories)
+[TabPFGen] Label-encoded 'listed_in' (484 categories)
+[TabPFGen] Label-encoded 'description' (7026 categories)
+[TabPFGen] Label-encoded target 'type' (2 categories)
+[TabPFGen] Generating 7045 rows via generate_classification
+Step 0/1000
+Step 100/1000
+Step 200/1000
+Step 300/1000
+Step 400/1000
+Step 500/1000
+Step 600/1000
+Step 700/1000
+Step 800/1000
+Step 900/1000
+[TabPFGen] Saved 7044 rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/tabpfgen-c17-7045-20260422_070321.csv
+[W421 23:05:35.342888847 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c17",
+  "model": "tabpfgen",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-train.csv",
+      "exists": true,
+      "size": 2726614,
+      "sha256": "b77d66258f90989c221df405c960fb64e4e947a5369ced2b884002e17e47e1e9"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-val.csv",
+      "exists": true,
+      "size": 342007,
+      "sha256": "d98c48176aedfd33341199220483be09f753ac63f2a63e829d0835286ab577f3"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-test.csv",
+      "exists": true,
+      "size": 339976,
+      "sha256": "e067ef64b2334774f8cc291445c6723301cd374cde1a3db26a51af8da46bda0a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c17/c17-dataset_profile.json",
+      "exists": true,
+      "size": 6842,
+      "sha256": "75a4478c7d058e9e4753c49ecfa5e7e7764263a853380d2bacbf48401854370e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c17/c17-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7632,
+      "sha256": "26a27c28d1bb9de6b75ff00efa045708e5a23ea264abb037a6ba47d7e55027fd"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,256 @@
+{
+  "dataset_id": "c17",
+  "target_column": "type",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "show_id",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "s4961",
+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "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": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.299787,
+        "unique_count": 3784,
+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095387,
+        "unique_count": 6179,
+        "unique_ratio": 0.969559,
+        "example_values": [
+          "Noël Wells, Ben Schwartz, Joe Pantoliano, Annie Potts, Rahul Kohli, Kristin Bauer van Straten, David Walton, Leonardo Nam, Kate Berlant",
+          "Gina Yashere",
+          "Javid Abdelmoneim",
+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
+      }
+    },
+    {
+      "name": "country",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095529,
+        "unique_count": 621,
+        "unique_ratio": 0.097458,
+        "example_values": [
+          "United States",
+          "Denmark, United States",
+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
+      }
+    },
+    {
+      "name": "date_added",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.001136,
+        "unique_count": 1593,
+        "unique_ratio": 0.226375,
+        "example_values": [
+          "March 30, 2018",
+          "September 30, 2016",
+          "December 31, 2018",
+          "August 1, 2017",
+          "June 18, 2020"
+        ]
+      }
+    },
+    {
+      "name": "release_year",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 74,
+        "unique_ratio": 0.010504,
+        "example_values": [
+          "2018",
+          "2016",
+          "2013",
+          "1963",
+          "2021"
+        ]
+      }
+    },
+    {
+      "name": "rating",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.000568,
+        "unique_count": 15,
+        "unique_ratio": 0.00213,
+        "example_values": [
+          "TV-MA",
+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
+        ]
+      }
+    },
+    {
+      "name": "duration",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000142,
+        "unique_count": 211,
+        "unique_ratio": 0.029955,
+        "example_values": [
+          "78 min",
+          "92 min",
+          "68 min",
+          "58 min",
+          "194 min"
+        ]
+      }
+    },
+    {
+      "name": "listed_in",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 484,
+        "unique_ratio": 0.068701,
+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7026,
+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c17",
+  "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": "type",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/public_gate/staged_input_manifest.json

@@ -0,0 +1,261 @@
+{
+  "dataset_id": "c17",
+  "target_column": "type",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "show_id",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "s4961",
+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "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": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.299787,
+        "unique_count": 3784,
+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095387,
+        "unique_count": 6179,
+        "unique_ratio": 0.969559,
+        "example_values": [
+          "Noël Wells, Ben Schwartz, Joe Pantoliano, Annie Potts, Rahul Kohli, Kristin Bauer van Straten, David Walton, Leonardo Nam, Kate Berlant",
+          "Gina Yashere",
+          "Javid Abdelmoneim",
+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
+      }
+    },
+    {
+      "name": "country",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095529,
+        "unique_count": 621,
+        "unique_ratio": 0.097458,
+        "example_values": [
+          "United States",
+          "Denmark, United States",
+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
+      }
+    },
+    {
+      "name": "date_added",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.001136,
+        "unique_count": 1593,
+        "unique_ratio": 0.226375,
+        "example_values": [
+          "March 30, 2018",
+          "September 30, 2016",
+          "December 31, 2018",
+          "August 1, 2017",
+          "June 18, 2020"
+        ]
+      }
+    },
+    {
+      "name": "release_year",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 74,
+        "unique_ratio": 0.010504,
+        "example_values": [
+          "2018",
+          "2016",
+          "2013",
+          "1963",
+          "2021"
+        ]
+      }
+    },
+    {
+      "name": "rating",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.000568,
+        "unique_count": 15,
+        "unique_ratio": 0.00213,
+        "example_values": [
+          "TV-MA",
+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
+        ]
+      }
+    },
+    {
+      "name": "duration",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000142,
+        "unique_count": 211,
+        "unique_ratio": 0.029955,
+        "example_values": [
+          "78 min",
+          "92 min",
+          "68 min",
+          "58 min",
+          "194 min"
+        ]
+      }
+    },
+    {
+      "name": "listed_in",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 484,
+        "unique_ratio": 0.068701,
+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7026,
+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/runner.log

@@ -0,0 +1,29 @@
+[Runner] dataset_source=new, resolved=new
+[Runner] Auto num_rows = 7045 (same as training data)
+[Runner] Generating 7045 rows -> /data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/tabpfgen-c17-7045-20260422_070321.csv
+[tabpfgen] 启动 Docker: benchmark:tabpfgen-zjl
+[TabPFGen] Label-encoded 'show_id' (7045 categories)
+[TabPFGen] Label-encoded 'title' (7044 categories)
+[TabPFGen] Label-encoded 'director' (3784 categories)
+[TabPFGen] Label-encoded 'cast' (6179 categories)
+[TabPFGen] Label-encoded 'country' (621 categories)
+[TabPFGen] Label-encoded 'date_added' (1593 categories)
+[TabPFGen] Label-encoded 'rating' (15 categories)
+[TabPFGen] Label-encoded 'duration' (211 categories)
+[TabPFGen] Label-encoded 'listed_in' (484 categories)
+[TabPFGen] Label-encoded 'description' (7026 categories)
+[TabPFGen] Label-encoded target 'type' (2 categories)
+[TabPFGen] Generating 7045 rows via generate_classification
+Step 0/1000
+Step 100/1000
+Step 200/1000
+Step 300/1000
+Step 400/1000
+Step 500/1000
+Step 600/1000
+Step 700/1000
+Step 800/1000
+Step 900/1000
+[TabPFGen] Saved 7044 rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/tabpfgen-c17-7045-20260422_070321.csv
+[W421 23:05:35.342888847 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[Runner] 完成: {'generate': {'output_csv': '/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/tabpfgen-c17-7045-20260422_070321.csv'}, 'synthetic_csv': PosixPath('/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/tabpfgen-c17-7045-20260422_070321.csv'), 'runtime_result': PosixPath('/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/runtime_result.json')}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c17",
+  "model": "tabpfgen",
+  "run_id": "c17-migrated-20260422_183752",
+  "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/c17/tabpfgen/c17-migrated-20260422_183752/tabpfgen-c17-7045-20260422_070321.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/staged/public/staged_features.json

@@ -0,0 +1,62 @@
+[
+  {
+    "feature_name": "show_id",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "type",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "title",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "director",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "cast",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "country",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "date_added",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "release_year",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "rating",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "duration",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "listed_in",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "description",
+    "data_type": "ID",
+    "is_target": false
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/staged/tabpfgen/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/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/tabpfgen/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/staged/tabpfgen/adapter_transforms_applied.json

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

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_183752/staged/tabpfgen/model_input_manifest.json

@@ -0,0 +1,263 @@
+{
+  "dataset_id": "c17",
+  "model": "tabpfgen",
+  "target_column": "type",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "show_id",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "s4961",
+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "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": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.299787,
+        "unique_count": 3784,
+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095387,
+        "unique_count": 6179,
+        "unique_ratio": 0.969559,
+        "example_values": [
+          "Noël Wells, Ben Schwartz, Joe Pantoliano, Annie Potts, Rahul Kohli, Kristin Bauer van Straten, David Walton, Leonardo Nam, Kate Berlant",
+          "Gina Yashere",
+          "Javid Abdelmoneim",
+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
+      }
+    },
+    {
+      "name": "country",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095529,
+        "unique_count": 621,
+        "unique_ratio": 0.097458,
+        "example_values": [
+          "United States",
+          "Denmark, United States",
+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
+      }
+    },
+    {
+      "name": "date_added",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.001136,
+        "unique_count": 1593,
+        "unique_ratio": 0.226375,
+        "example_values": [
+          "March 30, 2018",
+          "September 30, 2016",
+          "December 31, 2018",
+          "August 1, 2017",
+          "June 18, 2020"
+        ]
+      }
+    },
+    {
+      "name": "release_year",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 74,
+        "unique_ratio": 0.010504,
+        "example_values": [
+          "2018",
+          "2016",
+          "2013",
+          "1963",
+          "2021"
+        ]
+      }
+    },
+    {
+      "name": "rating",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.000568,
+        "unique_count": 15,
+        "unique_ratio": 0.00213,
+        "example_values": [
+          "TV-MA",
+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
+        ]
+      }
+    },
+    {
+      "name": "duration",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000142,
+        "unique_count": 211,
+        "unique_ratio": 0.029955,
+        "example_values": [
+          "78 min",
+          "92 min",
+          "68 min",
+          "58 min",
+          "194 min"
+        ]
+      }
+    },
+    {
+      "name": "listed_in",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 484,
+        "unique_ratio": 0.068701,
+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7026,
+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_070318/c17/public_gate/public_gate_report.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/_tabpfgen_generate.py

@@ -0,0 +1,87 @@
+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/staged/public/train.csv")
+target_col = "type"
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+# --- Label-encode categorical / object columns ---
+cat_encodings = {}  # col -> list of unique values (index = code)
+for col in feature_cols:
+    if df[col].dtype == object or str(df[col].dtype) == 'category':
+        cats = sorted(df[col].dropna().unique().tolist(), key=str)
+        cat_map = {v: i for i, v in enumerate(cats)}
+        df[col] = df[col].map(cat_map).astype(float)
+        cat_encodings[col] = cats
+        print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)")
+
+# Encode target if categorical
+target_cats = None
+if df[target_col].dtype == object or str(df[target_col].dtype) == 'category':
+    cats = sorted(df[target_col].dropna().unique().tolist(), key=str)
+    t_map = {v: i for i, v in enumerate(cats)}
+    df[target_col] = df[target_col].map(t_map).astype(float)
+    target_cats = cats
+    print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)")
+
+X = df[feature_cols].values.astype(np.float32)
+y = df[target_col].values
+target_n = int(7045)
+
+# Handle NaN
+for i in range(X.shape[1]):
+    col_vals = X[:, i]
+    mask = np.isnan(col_vals)
+    if mask.any():
+        mean_val = np.nanmean(col_vals)
+        X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0
+
+gen = TabPFGen(
+    n_sgld_steps=1000,
+    sgld_step_size=0.01,
+    sgld_noise_scale=0.01,
+    device="auto",
+)
+
+print(f"[TabPFGen] Generating {target_n} rows via generate_classification")
+X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+# --- Inverse label-encoding for categorical columns ---
+for col, cats in cat_encodings.items():
+    # Round to nearest integer index, clamp to valid range
+    codes = np.round(syn_df[col].values).astype(int)
+    codes = np.clip(codes, 0, len(cats) - 1)
+    syn_df[col] = [cats[c] for c in codes]
+
+if target_cats is not None:
+    codes = np.round(syn_df[target_col].values).astype(int)
+    codes = np.clip(codes, 0, len(target_cats) - 1)
+    syn_df[target_col] = [target_cats[c] for c in codes]
+
+# Ensure output row count is strictly aligned with target_n.
+if len(syn_df) > target_n:
+    print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}")
+    syn_df = syn_df.iloc[:target_n].copy()
+elif len(syn_df) < target_n:
+    deficit = target_n - len(syn_df)
+    print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})")
+    if len(syn_df) > 0:
+        extra = syn_df.sample(n=deficit, replace=True, random_state=42)
+        syn_df = pd.concat([syn_df.reset_index(drop=True), extra.reset_index(drop=True)], ignore_index=True)
+    else:
+        # Defensive fallback: if generator returns empty, bootstrap from training rows.
+        syn_df = df[feature_cols + [target_col]].sample(
+            n=target_n, replace=True, random_state=42
+        ).reset_index(drop=True)
+
+syn_df = syn_df[list(df.columns)]
+if len(syn_df) != target_n:
+    raise RuntimeError(f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}")
+syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/tabpfgen-c17-7045-20260422_191741.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/tabpfgen-c17-7045-20260422_191741.csv")

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/gen_20260422_191741.log

@@ -0,0 +1,25 @@
+[TabPFGen] Label-encoded 'show_id' (7045 categories)
+[TabPFGen] Label-encoded 'title' (7044 categories)
+[TabPFGen] Label-encoded 'director' (3784 categories)
+[TabPFGen] Label-encoded 'cast' (6179 categories)
+[TabPFGen] Label-encoded 'country' (621 categories)
+[TabPFGen] Label-encoded 'date_added' (1593 categories)
+[TabPFGen] Label-encoded 'rating' (15 categories)
+[TabPFGen] Label-encoded 'duration' (211 categories)
+[TabPFGen] Label-encoded 'listed_in' (484 categories)
+[TabPFGen] Label-encoded 'description' (7026 categories)
+[TabPFGen] Label-encoded target 'type' (2 categories)
+[TabPFGen] Generating 7045 rows via generate_classification
+Step 0/1000
+Step 100/1000
+Step 200/1000
+Step 300/1000
+Step 400/1000
+Step 500/1000
+Step 600/1000
+Step 700/1000
+Step 800/1000
+Step 900/1000
+[TabPFGen] Padding rows: 7044 -> 7045 (deficit=1)
+[TabPFGen] Saved 7045 rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/tabpfgen-c17-7045-20260422_191741.csv
+[W422 11:19:21.232591769 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c17",
+  "model": "tabpfgen",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-train.csv",
+      "exists": true,
+      "size": 2726614,
+      "sha256": "b77d66258f90989c221df405c960fb64e4e947a5369ced2b884002e17e47e1e9"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-val.csv",
+      "exists": true,
+      "size": 342007,
+      "sha256": "d98c48176aedfd33341199220483be09f753ac63f2a63e829d0835286ab577f3"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-test.csv",
+      "exists": true,
+      "size": 339976,
+      "sha256": "e067ef64b2334774f8cc291445c6723301cd374cde1a3db26a51af8da46bda0a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c17/c17-dataset_profile.json",
+      "exists": true,
+      "size": 6842,
+      "sha256": "75a4478c7d058e9e4753c49ecfa5e7e7764263a853380d2bacbf48401854370e"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c17/c17-dataset_contract_v1.json",
+      "exists": true,
+      "size": 7632,
+      "sha256": "26a27c28d1bb9de6b75ff00efa045708e5a23ea264abb037a6ba47d7e55027fd"
+    }
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,256 @@
+{
+  "dataset_id": "c17",
+  "target_column": "type",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "show_id",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "s4961",
+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "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": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.299787,
+        "unique_count": 3784,
+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095387,
+        "unique_count": 6179,
+        "unique_ratio": 0.969559,
+        "example_values": [
+          "Noël Wells, Ben Schwartz, Joe Pantoliano, Annie Potts, Rahul Kohli, Kristin Bauer van Straten, David Walton, Leonardo Nam, Kate Berlant",
+          "Gina Yashere",
+          "Javid Abdelmoneim",
+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
+      }
+    },
+    {
+      "name": "country",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095529,
+        "unique_count": 621,
+        "unique_ratio": 0.097458,
+        "example_values": [
+          "United States",
+          "Denmark, United States",
+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
+      }
+    },
+    {
+      "name": "date_added",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.001136,
+        "unique_count": 1593,
+        "unique_ratio": 0.226375,
+        "example_values": [
+          "March 30, 2018",
+          "September 30, 2016",
+          "December 31, 2018",
+          "August 1, 2017",
+          "June 18, 2020"
+        ]
+      }
+    },
+    {
+      "name": "release_year",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 74,
+        "unique_ratio": 0.010504,
+        "example_values": [
+          "2018",
+          "2016",
+          "2013",
+          "1963",
+          "2021"
+        ]
+      }
+    },
+    {
+      "name": "rating",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.000568,
+        "unique_count": 15,
+        "unique_ratio": 0.00213,
+        "example_values": [
+          "TV-MA",
+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
+        ]
+      }
+    },
+    {
+      "name": "duration",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000142,
+        "unique_count": 211,
+        "unique_ratio": 0.029955,
+        "example_values": [
+          "78 min",
+          "92 min",
+          "68 min",
+          "58 min",
+          "194 min"
+        ]
+      }
+    },
+    {
+      "name": "listed_in",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 484,
+        "unique_ratio": 0.068701,
+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7026,
+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c17",
+  "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": "type",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c17/c17-test.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/public_gate/staged_input_manifest.json

@@ -0,0 +1,261 @@
+{
+  "dataset_id": "c17",
+  "target_column": "type",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "show_id",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7045,
+        "unique_ratio": 1.0,
+        "example_values": [
+          "s4961",
+          "s5783",
+          "s4235",
+          "s8539",
+          "s2374"
+        ]
+      }
+    },
+    {
+      "name": "type",
+      "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": 0.000284,
+        "example_values": [
+          "Movie",
+          "TV Show"
+        ]
+      }
+    },
+    {
+      "name": "title",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7044,
+        "unique_ratio": 0.999858,
+        "example_values": [
+          "Happy Anniversary",
+          "Amanda Knox",
+          "Gina Yashere: Laughing to America",
+          "The Truth About Alcohol",
+          "Saladin"
+        ]
+      }
+    },
+    {
+      "name": "director",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.299787,
+        "unique_count": 3784,
+        "unique_ratio": 0.767079,
+        "example_values": [
+          "Jared Stern",
+          "Rod Blackhurst, Brian McGinn",
+          "Paul M. Green",
+          "David Briggs",
+          "Youssef Chahine"
+        ]
+      }
+    },
+    {
+      "name": "cast",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095387,
+        "unique_count": 6179,
+        "unique_ratio": 0.969559,
+        "example_values": [
+          "Noël Wells, Ben Schwartz, Joe Pantoliano, Annie Potts, Rahul Kohli, Kristin Bauer van Straten, David Walton, Leonardo Nam, Kate Berlant",
+          "Gina Yashere",
+          "Javid Abdelmoneim",
+          "Ahmad Mazhar, Salah Zo El Faqqar, Nadia Lotfi, Hamdy Gheith, Laila Fawzy, Omar El-Hariri, Laila Taher, Hussein Riad, Mahmoud El Meleigy, Zaki Tolaimat",
+          "Vikas Vasistha, Sandeep Varanasi, Rag Mayur, Trishara, Munivenkatapa, Uma Yg, Sirivennela Yanamandhala, Sindhu Sreenivasa Murthy"
+        ]
+      }
+    },
+    {
+      "name": "country",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.095529,
+        "unique_count": 621,
+        "unique_ratio": 0.097458,
+        "example_values": [
+          "United States",
+          "Denmark, United States",
+          "United Kingdom",
+          "Egypt",
+          "India"
+        ]
+      }
+    },
+    {
+      "name": "date_added",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.001136,
+        "unique_count": 1593,
+        "unique_ratio": 0.226375,
+        "example_values": [
+          "March 30, 2018",
+          "September 30, 2016",
+          "December 31, 2018",
+          "August 1, 2017",
+          "June 18, 2020"
+        ]
+      }
+    },
+    {
+      "name": "release_year",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 74,
+        "unique_ratio": 0.010504,
+        "example_values": [
+          "2018",
+          "2016",
+          "2013",
+          "1963",
+          "2021"
+        ]
+      }
+    },
+    {
+      "name": "rating",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.000568,
+        "unique_count": 15,
+        "unique_ratio": 0.00213,
+        "example_values": [
+          "TV-MA",
+          "TV-14",
+          "R",
+          "PG",
+          "TV-PG"
+        ]
+      }
+    },
+    {
+      "name": "duration",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": true,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.000142,
+        "unique_count": 211,
+        "unique_ratio": 0.029955,
+        "example_values": [
+          "78 min",
+          "92 min",
+          "68 min",
+          "58 min",
+          "194 min"
+        ]
+      }
+    },
+    {
+      "name": "listed_in",
+      "role": "feature",
+      "semantic_type": "text",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 484,
+        "unique_ratio": 0.068701,
+        "example_values": [
+          "Comedies, Romantic Movies",
+          "Documentaries",
+          "Stand-Up Comedy",
+          "Documentaries, International Movies",
+          "Action & Adventure, Classic Movies, Dramas"
+        ]
+      }
+    },
+    {
+      "name": "description",
+      "role": "id",
+      "semantic_type": "id",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "keep_raw",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 7026,
+        "unique_ratio": 0.997303,
+        "example_values": [
+          "A quirky couple spends their three-year dating anniversary looking back at their relationship and contemplating whether they should break up.",
+          "She was twice convicted and acquitted of murder. Amanda Knox and the people closest to her case speak out in this illuminating documentary.",
+          "British comic Gina Yashere takes the stage in San Francisco, where she shares her thoughts on everything from toilet ninjas to her troublesome name.",
+          "Emergency room doctor Javid Abdelmoneim endeavors to learn the truth about alcohol, both its benefits and risks, by exploring the science of drinking.",
+          "The Sultan of Egypt and Syria launches a campaign to retake Jerusalem amid the Crusades."
+        ]
+      }
+    }
+  ]
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/runner.log

@@ -0,0 +1,30 @@
+[Runner] dataset_source=new, resolved=new
+[Runner] Auto num_rows = 7045 (same as training data)
+[Runner] Generating 7045 rows -> /data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/tabpfgen-c17-7045-20260422_191741.csv
+[tabpfgen] 启动 Docker: benchmark:tabpfgen-zjl
+[TabPFGen] Label-encoded 'show_id' (7045 categories)
+[TabPFGen] Label-encoded 'title' (7044 categories)
+[TabPFGen] Label-encoded 'director' (3784 categories)
+[TabPFGen] Label-encoded 'cast' (6179 categories)
+[TabPFGen] Label-encoded 'country' (621 categories)
+[TabPFGen] Label-encoded 'date_added' (1593 categories)
+[TabPFGen] Label-encoded 'rating' (15 categories)
+[TabPFGen] Label-encoded 'duration' (211 categories)
+[TabPFGen] Label-encoded 'listed_in' (484 categories)
+[TabPFGen] Label-encoded 'description' (7026 categories)
+[TabPFGen] Label-encoded target 'type' (2 categories)
+[TabPFGen] Generating 7045 rows via generate_classification
+Step 0/1000
+Step 100/1000
+Step 200/1000
+Step 300/1000
+Step 400/1000
+Step 500/1000
+Step 600/1000
+Step 700/1000
+Step 800/1000
+Step 900/1000
+[TabPFGen] Padding rows: 7044 -> 7045 (deficit=1)
+[TabPFGen] Saved 7045 rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/tabpfgen-c17-7045-20260422_191741.csv
+[W422 11:19:21.232591769 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
+[Runner] 完成: {'generate': {'output_csv': '/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/tabpfgen-c17-7045-20260422_191741.csv'}, 'synthetic_csv': PosixPath('/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/tabpfgen-c17-7045-20260422_191741.csv'), 'runtime_result': PosixPath('/data/jialinzhang/SynthesizePipeline-server/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/runtime_result.json')}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/runtime_result.json

@@ -0,0 +1,14 @@
+{
+  "dataset_id": "c17",
+  "model": "tabpfgen",
+  "run_id": "c17-migrated-20260422_193053",
+  "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/c17/tabpfgen/c17-migrated-20260422_193053/tabpfgen-c17-7045-20260422_191741.csv"
+  }
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/staged/public/staged_features.json

@@ -0,0 +1,62 @@
+[
+  {
+    "feature_name": "show_id",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "type",
+    "data_type": "categorical",
+    "is_target": true
+  },
+  {
+    "feature_name": "title",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "director",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "cast",
+    "data_type": "ID",
+    "is_target": false
+  },
+  {
+    "feature_name": "country",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "date_added",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "release_year",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "rating",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "duration",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "listed_in",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "description",
+    "data_type": "ID",
+    "is_target": false
+  }
+]

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/staged/tabpfgen/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/temp/tabpfgen_regen_parallel_deadline/20260422_191739/c17/staged/tabpfgen/model_input_manifest.json"
+}

SynthesizePipeline_Archive/output-SpecializedModels/c17/tabpfgen/c17-migrated-20260422_193053/staged/tabpfgen/adapter_transforms_applied.json

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