Add syntheticSuccess n1

syntheticSuccess/n1/arf/arf-n1-20260422_055912/_arf_generate.py

@@ -0,0 +1,23 @@
+import pickle
+import pandas as pd
+
+n_target = int(3680)
+with open("/work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+syn = model.forge(n=n_target)
+syn = syn.reset_index(drop=True)
+if len(syn) > n_target:
+    syn = syn.iloc[:n_target]
+elif len(syn) < n_target:
+    parts = [syn]
+    tries = 0
+    while sum(len(p) for p in parts) < n_target and tries < 64:
+        tries += 1
+        need = n_target - sum(len(p) for p in parts)
+        chunk = model.forge(n=max(need, 1)).reset_index(drop=True)
+        if len(chunk) == 0:
+            break
+        parts.append(chunk)
+    syn = pd.concat(parts, ignore_index=True).iloc[:n_target]
+syn.to_csv("/work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf-n1-3680-20260422_061320.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf-n1-3680-20260422_061320.csv")

syntheticSuccess/n1/arf/arf-n1-20260422_055912/_arf_train.py

@@ -0,0 +1,37 @@
+import pickle
+import numpy as np
+import pandas as pd
+from arfpy import arf
+
+def _sanitize_for_arf(df: pd.DataFrame) -> pd.DataFrame:
+    """缓解 forge 阶段 scipy.stats.truncnorm / 除零：处理 inf、NaN 与极端尾部。"""
+    df = df.replace([np.inf, -np.inf], np.nan)
+    df = df.dropna(axis=1, how="all")
+    for col in df.select_dtypes(include=[np.number]).columns:
+        med = df[col].median()
+        if pd.isna(med):
+            med = 0.0
+        df[col] = df[col].fillna(med)
+        nu = int(df[col].nunique(dropna=True))
+        if nu <= 1:
+            continue
+        lo, hi = df[col].quantile(0.001), df[col].quantile(0.999)
+        if pd.notna(lo) and pd.notna(hi) and lo < hi:
+            df[col] = df[col].clip(lo, hi)
+    return df
+
+df = pd.read_csv("/work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/staged/public/train.csv")
+df = _sanitize_for_arf(df)
+print(f"[ARF] Training on {len(df)} rows, {len(df.columns)} cols")
+
+model = arf.arf(x=df)
+if hasattr(model, "fit"):
+    model.fit()
+elif hasattr(model, "forde"):
+    model.forde()
+else:
+    raise RuntimeError("arfpy API: no fit() / forde()")
+
+with open("/work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf_model.pkl")

syntheticSuccess/n1/arf/arf-n1-20260422_055912/arf-n1-3680-20260422_061320.csv

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+oid sha256:912e4f20ac476ce74eab18fb0a4da53ed19e5876b74df5483385b6bdde862ff0
+size 2692127

syntheticSuccess/n1/arf/arf-n1-20260422_055912/arf_model.pkl

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+oid sha256:f02f7a2a0691c9c1374e859eb1279110c3714119df2baef75683d3cb41029b82
+size 70017137

syntheticSuccess/n1/arf/arf-n1-20260422_055912/gen_20260422_061320.log

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+oid sha256:e9bbc194d6449ff602a1300843158a3d5162f44a8f5c9250dda06cdde5f9daab
+size 455

syntheticSuccess/n1/arf/arf-n1-20260422_055912/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "n1",
+  "model": "arf",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-train.csv",
+      "exists": true,
+      "size": 563021,
+      "sha256": "e603fe5195edac0e926f63b398fd1af936766c1b502b300341a478e70aef4df5"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-val.csv",
+      "exists": true,
+      "size": 71100,
+      "sha256": "29ec664376ba652ee157545c9afe81d1afa054e30a554bc50ab2ebf1c52cef83"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-test.csv",
+      "exists": true,
+      "size": 71605,
+      "sha256": "2696873d18a2f693dc4b08f1c30f34a30cf96f1195790479ab8947fee8bc35ef"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n1/n1-dataset_profile.json",
+      "exists": true,
+      "size": 21967,
+      "sha256": "62128c1a7c75e9b7c23136d477b5795a9477bc710734776bd4a1378d3d81dc33"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/n1/n1-dataset_contract_v1.json",
+      "exists": true,
+      "size": 27866,
+      "sha256": "cf9ef940957593e898ef55899808bb02267775fdad024ec6bf75793ef28b7ba1"
+    }
+  }
+}

syntheticSuccess/n1/arf/arf-n1-20260422_055912/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,1222 @@
+{
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+  "target_column": "class",
+  "task_type": "classification",
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+      "role": "feature",
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syntheticSuccess/n1/arf/arf-n1-20260422_055912/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
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+  "task_type": "classification",
+  "input_splits": {
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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-test.csv"
+  }
+}

syntheticSuccess/n1/arf/arf-n1-20260422_055912/public_gate/staged_input_manifest.json

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syntheticSuccess/n1/arf/arf-n1-20260422_055912/runtime_result.json

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+{
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+  "model": "arf",
+  "run_id": "arf-n1-20260422_055912",
+  "public_gate_status": "pass",
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+  "reason_code": null,
+  "reason_detail": null,
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+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/arf_model.pkl"
+  }
+}

syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/arf/adapter_report.json

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+}

syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/arf/adapter_transforms_applied.json

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syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/arf/model_input_manifest.json

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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/staged/public/test.csv",
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+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/n1/arf/arf-n1-20260422_055912/public_gate/public_gate_report.json"
+}

syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/staged_features.json

@@ -0,0 +1,292 @@
+[
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+  {
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+    "is_target": false
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+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
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+    "is_target": false
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+  {
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+    "is_target": false
+  },
+  {
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+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
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+    "data_type": "continuous",
+    "is_target": false
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+  {
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+  },
+  {
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+  {
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+  {
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+    "data_type": "continuous",
+    "is_target": false
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+  },
+  {
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+  },
+  {
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+    "data_type": "continuous",
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+  {
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+  },
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+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_650",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_lab",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_labs",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_telnet",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_857",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_data",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_415",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_85",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_technology",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_1999",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_parts",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_pm",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_direct",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_cs",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_meeting",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_original",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_project",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_re",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_edu",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_table",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "word_freq_conference",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "char_freq_%3B",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "char_freq_%28",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "char_freq_%5B",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "char_freq_%21",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "char_freq_%24",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "char_freq_%23",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "capital_run_length_average",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "capital_run_length_longest",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "capital_run_length_total",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "binary",
+    "is_target": true
+  }
+]

syntheticSuccess/n1/arf/arf-n1-20260422_055912/staged/public/test.csv

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syntheticSuccess/n1/arf/arf-n1-20260422_055912/train_20260422_055912.log

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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_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/n1/bayesnet/bayesnet-n1-20260422_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(3680)
+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/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet-n1-3680-20260422_060304.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet-n1-3680-20260422_060304.csv")

syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_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/n1/bayesnet/bayesnet-n1-20260422_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/n1/bayesnet/bayesnet-n1-20260422_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/n1/bayesnet/bayesnet-n1-20260422_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/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl")

syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet-n1-3680-20260422_060304.csv

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+size 3770782

syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_coltypes.json

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+{
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_address",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_all",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "categorical"
+    },
+    {
+      "name": "word_freq_our",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_over",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_order",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_will",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_people",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_report",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_free",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_business",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_email",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_you",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_your",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_font",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_000",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_money",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_george",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_650",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_857",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_data",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_415",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_85",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "categorical"
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_original",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_project",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_re",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "continuous"
+    },
+    {
+      "name": "word_freq_table",
+      "type": "categorical"
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "continuous"
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "continuous"
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "continuous"
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "continuous"
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "continuous"
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "continuous"
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "continuous"
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "continuous"
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "continuous"
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "continuous"
+    },
+    {
+      "name": "class",
+      "type": "categorical"
+    }
+  ],
+  "integer_columns": []
+}

syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/bayesnet_model.pkl

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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/const_cols.json

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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/gen_20260422_060304.log

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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/input_snapshot.json

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+{
+  "dataset_id": "n1",
+  "model": "bayesnet",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-train.csv",
+      "exists": true,
+      "size": 563021,
+      "sha256": "e603fe5195edac0e926f63b398fd1af936766c1b502b300341a478e70aef4df5"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-val.csv",
+      "exists": true,
+      "size": 71100,
+      "sha256": "29ec664376ba652ee157545c9afe81d1afa054e30a554bc50ab2ebf1c52cef83"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-test.csv",
+      "exists": true,
+      "size": 71605,
+      "sha256": "2696873d18a2f693dc4b08f1c30f34a30cf96f1195790479ab8947fee8bc35ef"
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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/public_gate/normalized_schema_snapshot.json

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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/public_gate/public_gate_report.json

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+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-test.csv"
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+}

syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/public_gate/staged_input_manifest.json

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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/runtime_result.json

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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/staged/bayesnet/adapter_report.json

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syntheticSuccess/n1/bayesnet/bayesnet-n1-20260422_060152/staged/bayesnet/adapter_transforms_applied.json

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syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/_ctgan_generate.py

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+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-SpecializedModels/n1/ctgan/ctgan-n1-20260413_043449/models_1epochs/ctgan_1epochs.pt")
+total = 3680
+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-SpecializedModels/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-3680-20260420_030338.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/n1/ctgan/ctgan-n1-20260413_043449/ctgan-n1-3680-20260420_030338.csv")

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syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
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+      "status": "pass"
+    }
+  ],
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+  "task_type": "classification",
+  "input_splits": {
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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n1/n1-test.csv"
+  }
+}

syntheticSuccess/n1/ctgan/ctgan-n1-20260413_043449/public_gate/staged_input_manifest.json

@@ -0,0 +1,1227 @@
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