Resume SynthData0523 main/c7 batch 2

.gitattributes

@@ -5420,3 +5420,34 @@ SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet-c7-10368-202
 SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/bayesnet_model.pkl filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260321_061903.log filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/gen_20260330_065316.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/train_20260321_061816.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_coltypes.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/const_cols.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/gen_20260429_031053.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/input_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/public_gate/normalized_schema_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/public_gate/public_gate_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/public_gate/staged_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/runtime_result.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/bayesnet/adapter_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/bayesnet/adapter_transforms_applied.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/bayesnet/model_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/staged_features.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/train_20260429_031038.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/gen_20260429_031608.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/train_20260429_031537.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan_metadata.json filter=lfs diff=lfs merge=lfs -text

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/adapter_transforms_applied.json

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

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/bayesnet/model_input_manifest.json

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

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/staged_features.json

@@ -0,0 +1,47 @@
+[
+  {
+    "feature_name": "parents",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "has_nurs",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "form",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "children",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "housing",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "finance",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "social",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "health",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058
+size 106030

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a
+size 847349

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780
+size 106192

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260321_061816/train_20260321_061816.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ac5d75bda1da96923ed90d790fc4743707eff8968b962898056f8d855f54505
+size 465

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_generate.py

@@ -0,0 +1,105 @@
+
+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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/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(10368)
+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 = final.reset_index(drop=True)
+final.to_csv("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv")

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/_bayesnet_train.py

@@ -0,0 +1,133 @@
+
+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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/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
+max_cat_levels = 256
+if _n_plan > 35 or _n_samples > 200000:
+    max_bins = 5
+    max_cat_levels = 64
+if _n_plan > 55:
+    max_bins = 4
+    max_cat_levels = 32
+print(
+    f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} "
+    f"(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":
+        s2 = s.astype(str).fillna("__NA__")
+        counts = s2.value_counts(dropna=False)
+        if len(counts) > max_cat_levels:
+            keep = set(counts.index[: max_cat_levels - 1].tolist())
+            s2 = s2.map(lambda x: x if x in keep else "__OTHER__")
+        uniques = sorted(s2.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] = s2.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)
+enc_fit = enc
+if len(enc) > 120000:
+    enc_fit = enc.sample(n=120000, random_state=1, replace=False)
+    print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})")
+network.fit(enc_fit)
+
+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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl")

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet-c7-10368-20260429_031053.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ec483f732565f4c875574544cd638c236741ab73b64670c524b307e81d3762a
+size 847720

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_coltypes.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1cde99b1974184c5df0d3fccfade8f11ca79c02b92a5c659dc5ba44c4aefe1ab
+size 635

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/bayesnet_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:342563385d822291efea03ef34858a3fa7a9c37695d1d9aa761ad616bb14bd4f
+size 5680

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/const_cols.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44136fa355b3678a1146ad16f7e8649e94fb4fc21fe77e8310c060f61caaff8a
+size 2

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/gen_20260429_031053.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e814c1f07c97634a205072c6af264b79105ca899b88cdfec9e2a14198170e22e
+size 3664

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f14cece186149619179cfb8958e71a76b2fbcabb5fa6692b86991573a8d3b9d4
+size 1349

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5dd68094a5d8fc40d291695c5aa12bc01cfe8d3a62848b4bcfd82c2e88ed19c9
+size 4177

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f08566bcd78a9005059bf27f5f9ecf74954e2be289410d19100e900577f651ea
+size 912

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:10fcaae37cee3c1cecb035645a99b986db3843874586926587633a65d1d6d835
+size 4993

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a94883ffb73e985e889ae5e8791d48ba9a58afdf509bbb913053485e0feb65f2
+size 616

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/bayesnet/adapter_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:906add5afc393cc78cc1424a22b842aec8297d88dc80cb495fe3c24ed7249e55
+size 324

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/bayesnet/adapter_transforms_applied.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945
+size 2

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b718a92aed504ba5d07eb0fb848edd8d33a916bc26a39393436602dc0e3f5f7
+size 5193

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/staged_features.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07596ae0689482090a05749dba332eca061d863cde5d8bf7a83e8c2c92abb330
+size 851

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058
+size 106030

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a
+size 847349

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780
+size 106192

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031038/train_20260429_031038.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4111c669d5532e874b7f7b68b102dfe387746d6756cffa5d3ad8d7cb0ea7f147
+size 3741

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_generate.py

@@ -0,0 +1,105 @@
+
+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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/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(10368)
+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 = final.reset_index(drop=True)
+final.to_csv("/work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv")

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/_bayesnet_train.py

@@ -0,0 +1,133 @@
+
+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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/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
+max_cat_levels = 256
+if _n_plan > 35 or _n_samples > 200000:
+    max_bins = 5
+    max_cat_levels = 64
+if _n_plan > 55:
+    max_bins = 4
+    max_cat_levels = 32
+print(
+    f"[BayesNet] max_bins={max_bins}, max_cat_levels={max_cat_levels} "
+    f"(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":
+        s2 = s.astype(str).fillna("__NA__")
+        counts = s2.value_counts(dropna=False)
+        if len(counts) > max_cat_levels:
+            keep = set(counts.index[: max_cat_levels - 1].tolist())
+            s2 = s2.map(lambda x: x if x in keep else "__OTHER__")
+        uniques = sorted(s2.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] = s2.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)
+enc_fit = enc
+if len(enc) > 120000:
+    enc_fit = enc.sample(n=120000, random_state=1, replace=False)
+    print(f"[BayesNet] fit() on {len(enc_fit)} rows (full n={len(enc)})")
+network.fit(enc_fit)
+
+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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl")

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39ab8a39e164ff03b6176d8e2ecb2cd4bcaad07a36ece74636a690a5b5fc0128
+size 848576

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_coltypes.json

@@ -0,0 +1,41 @@
+{
+  "columns": [
+    {
+      "name": "parents",
+      "type": "categorical"
+    },
+    {
+      "name": "has_nurs",
+      "type": "categorical"
+    },
+    {
+      "name": "form",
+      "type": "categorical"
+    },
+    {
+      "name": "children",
+      "type": "categorical"
+    },
+    {
+      "name": "housing",
+      "type": "categorical"
+    },
+    {
+      "name": "finance",
+      "type": "categorical"
+    },
+    {
+      "name": "social",
+      "type": "categorical"
+    },
+    {
+      "name": "health",
+      "type": "categorical"
+    },
+    {
+      "name": "class",
+      "type": "categorical"
+    }
+  ],
+  "integer_columns": []
+}

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:342563385d822291efea03ef34858a3fa7a9c37695d1d9aa761ad616bb14bd4f
+size 5680

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/const_cols.json

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

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/gen_20260429_031608.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29ad479e9d4fb60e922c5012efa4a0a98dbd7d317e09898bbbf71679ac4fc146
+size 3664

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/input_snapshot.json

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

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/normalized_schema_snapshot.json

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

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c7",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG003_profile_header_match",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG004_missing_token_normalized",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
+  "target_column": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c7/c7-test.csv"
+  }
+}

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/staged_input_manifest.json

@@ -0,0 +1,188 @@
+{
+  "dataset_id": "c7",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "parents",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "usual",
+          "pretentious",
+          "great_pret"
+        ]
+      }
+    },
+    {
+      "name": "has_nurs",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "very_crit",
+          "critical",
+          "improper",
+          "less_proper",
+          "proper"
+        ]
+      }
+    },
+    {
+      "name": "form",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "complete",
+          "completed",
+          "incomplete",
+          "foster"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "1",
+          "3",
+          "2",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "housing",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "less_conv",
+          "convenient",
+          "critical"
+        ]
+      }
+    },
+    {
+      "name": "finance",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000193,
+        "example_values": [
+          "convenient",
+          "inconv"
+        ]
+      }
+    },
+    {
+      "name": "social",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "slightly_prob",
+          "nonprob",
+          "problematic"
+        ]
+      }
+    },
+    {
+      "name": "health",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "recommended",
+          "priority",
+          "not_recom"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "priority",
+          "spec_prior",
+          "not_recom",
+          "very_recom",
+          "recommend"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/runtime_result.json

@@ -0,0 +1,15 @@
+{
+  "dataset_id": "c7",
+  "model": "bayesnet",
+  "run_id": "bayesnet-c7-20260429_031537",
+  "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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet-c7-10368-20260429_031608.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/bayesnet_model.pkl"
+  }
+}

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/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-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json"
+}

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/adapter_transforms_applied.json

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

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,190 @@
+{
+  "dataset_id": "c7",
+  "model": "bayesnet",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "parents",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "usual",
+          "pretentious",
+          "great_pret"
+        ]
+      }
+    },
+    {
+      "name": "has_nurs",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "very_crit",
+          "critical",
+          "improper",
+          "less_proper",
+          "proper"
+        ]
+      }
+    },
+    {
+      "name": "form",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "complete",
+          "completed",
+          "incomplete",
+          "foster"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.000386,
+        "example_values": [
+          "1",
+          "3",
+          "2",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "housing",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "less_conv",
+          "convenient",
+          "critical"
+        ]
+      }
+    },
+    {
+      "name": "finance",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000193,
+        "example_values": [
+          "convenient",
+          "inconv"
+        ]
+      }
+    },
+    {
+      "name": "social",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "slightly_prob",
+          "nonprob",
+          "problematic"
+        ]
+      }
+    },
+    {
+      "name": "health",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.000289,
+        "example_values": [
+          "recommended",
+          "priority",
+          "not_recom"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 5,
+        "unique_ratio": 0.000482,
+        "example_values": [
+          "priority",
+          "spec_prior",
+          "not_recom",
+          "very_recom",
+          "recommend"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c7/bayesnet/bayesnet-c7-20260429_031537/public_gate/public_gate_report.json"
+}

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/staged_features.json

@@ -0,0 +1,47 @@
+[
+  {
+    "feature_name": "parents",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "has_nurs",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "form",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "children",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "housing",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "finance",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "social",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "health",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2042076337d5c37c6476e6bca2bd33cb5a171450c27894534ef50ac223256058
+size 106030

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b37f6b2ef5257f40bd826ac956749881f0f474362bdb56e8c5728ad629242e3a
+size 847349

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eff6dec27c3740661a1ae84dea391d690dfb60342bfd5d7527b903fdd6009780
+size 106192

SynthData0523/main/c7/bayesnet/bayesnet-c7-20260429_031537/train_20260429_031537.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1788e94eba6e542f6afdf6c0054a1d521deae3fba20e72212282c5bd06f20d84
+size 3741

SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/_ctgan_generate.py

@@ -0,0 +1,18 @@
+import sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
+apply_ctgan_inverse_fix()
+import pandas as pd
+from ctgan.synthesizers.ctgan import CTGAN
+model = CTGAN.load("/work/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/models_300epochs/ctgan_300epochs.pt")
+total = 10368
+chunk = min(50000, total) if total > 50000 else total
+parts = []
+left = total
+while left > 0:
+    take = min(chunk, left)
+    parts.append(model.sample(take))
+    left -= take
+sampled = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
+sampled.to_csv("/work/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-Benchmark-trainonly-v1/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv")

SynthData0523/main/c7/ctgan/ctgan-c7-20260429_032054/ctgan-c7-10368-20260429_032437.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6292ae1576f8682712eb05a628de61384dab5cf211e620852f27aaea02e87599
+size 840588