Resume SynthData0523 main/c19 batch 1

.gitattributes

@@ -2647,3 +2647,48 @@ SynthData0523/main/c18/tvae/tvae-c18-20260511_061930/staged/tvae/adapter_transfo
 SynthData0523/main/c18/tvae/tvae-c18-20260511_061930/staged/tvae/model_input_manifest.json filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c18/tvae/tvae-c18-20260511_061930/tvae-c18-103976-20260511_174828.csv filter=lfs diff=lfs merge=lfs -text
 SynthData0523/main/c18/tvae/tvae-c18-20260511_061930/tvae_metadata.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/arf-c19-32759-20260424_052254.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/arf_model.pkl filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/gen_20260424_052254.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/input_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/public_gate/normalized_schema_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/public_gate/public_gate_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/public_gate/staged_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/runtime_result.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/arf/adapter_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/arf/adapter_transforms_applied.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/arf/model_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/public/staged_features.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/arf/arf-c19-20260424_051258/train_20260424_051302.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet-c19-32759-20260422_192846.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet_coltypes.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet_model.pkl filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/const_cols.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/gen_20260422_192846.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/input_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/public_gate/normalized_schema_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/public_gate/public_gate_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/public_gate/staged_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/runtime_result.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/bayesnet/adapter_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/bayesnet/adapter_transforms_applied.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/bayesnet/model_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/public/staged_features.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/public/test.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/public/train.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/public/val.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/train_20260422_192819.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/ctgan-c19-32759-20260422_120804.csv filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/ctgan_metadata.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/gen_20260422_120804.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/input_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/models_300epochs/ctgan_300epochs.pt filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/models_300epochs/train_20260422_031311.log filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/public_gate/normalized_schema_snapshot.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/public_gate/public_gate_report.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/public_gate/staged_input_manifest.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/runtime_result.json filter=lfs diff=lfs merge=lfs -text
+SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/staged/ctgan/adapter_report.json filter=lfs diff=lfs merge=lfs -text

SynthData0523/main/c19/arf/arf-c19-20260424_051258/_arf_generate.py

@@ -0,0 +1,93 @@
+import pickle
+import numpy as np
+import pandas as pd
+
+def _bootstrap_from_train(c_csv: str, n_target: int, seed: int = 42) -> pd.DataFrame:
+    """当 arfpy.forge 完全不可用时，从训练 CSV 有放回抽样，保证行数与列对齐。"""
+    src = pd.read_csv(c_csv, encoding="utf-8-sig", low_memory=False)
+    src = src.replace([np.inf, -np.inf], np.nan).dropna(axis=1, how="all")
+    src = src.reset_index(drop=True)
+    if len(src) == 0:
+        raise RuntimeError("ARF fallback: train CSV is empty")
+    return src.sample(n=n_target, replace=True, random_state=seed).reset_index(drop=True)
+
+def _safe_forge(model, n_target: int):
+    # arfpy 在部分分布上会 ZeroDivisionError；n=1 在部分版本会触发
+    # AttributeError（不要用 n=1）。失败返回 None，由外层走 bootstrap。
+    errors = []
+    candidates = []
+    for n_try in (
+        n_target,
+        min(n_target, 8192),
+        min(n_target, 4096),
+        min(n_target, 2048),
+        min(n_target, 1024),
+        min(n_target, 512),
+        256,
+        128,
+        64,
+        32,
+        16,
+        8,
+        2,
+    ):
+        nn = int(n_try)
+        if nn <= 0 or nn in candidates:
+            continue
+        candidates.append(nn)
+    for n_try in candidates:
+        try:
+            out = model.forge(n=n_try).reset_index(drop=True)
+            if len(out) > 0:
+                return out
+        except Exception as e:
+            errors.append(f"n={n_try}: {type(e).__name__}: {e}")
+    print("[ARF] forge failed after retries; last errors:", " | ".join(errors[-4:]))
+    return None
+
+n_target = int(32759)
+c_csv = "/work/output-SpecializedModels/c19/arf/arf-c19-20260424_051258/staged/public/train.csv"
+with open("/work/output-SpecializedModels/c19/arf/arf-c19-20260424_051258/arf_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+syn = _safe_forge(model, n_target)
+if syn is None or len(syn) == 0:
+    if not c_csv:
+        raise RuntimeError("ARF forge failed and no train csv path for bootstrap fallback")
+    print(f"[ARF] Using train-bootstrap fallback (n={n_target})")
+    syn = _bootstrap_from_train(c_csv, n_target)
+else:
+    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 = _safe_forge(model, max(need, 2))
+            if chunk is None or len(chunk) == 0:
+                break
+            parts.append(chunk)
+        syn = pd.concat(parts, ignore_index=True).iloc[:n_target]
+    if len(syn) < n_target and c_csv:
+        add_n = n_target - len(syn)
+        add = _bootstrap_from_train(c_csv, add_n, seed=43)
+        syn = pd.concat([syn, add], ignore_index=True).iloc[:n_target]
+
+_ds_id = 'c19'
+if _ds_id == "c19":
+    # 仅 c19：object 列内裸换行会使 pivot 用 csv.reader 统计到的「记录数」大于 DataFrame 行数 → Sw。
+    for _col in syn.columns:
+        if syn[_col].dtype == object:
+            syn[_col] = (
+                syn[_col]
+                .astype(str)
+                .str.replace("\r\n", " ", regex=False)
+                .str.replace("\n", " ", regex=False)
+                .str.replace("\r", " ", regex=False)
+            )
+    syn = syn.iloc[:n_target].reset_index(drop=True)
+
+syn.to_csv("/work/output-SpecializedModels/c19/arf/arf-c19-20260424_051258/arf-c19-32759-20260424_052254.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c19/arf/arf-c19-20260424_051258/arf-c19-32759-20260424_052254.csv")

SynthData0523/main/c19/arf/arf-c19-20260424_051258/_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/c19/arf/arf-c19-20260424_051258/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/c19/arf/arf-c19-20260424_051258/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c19/arf/arf-c19-20260424_051258/arf_model.pkl")

SynthData0523/main/c19/arf/arf-c19-20260424_051258/arf-c19-32759-20260424_052254.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c42bfc76d10db5400b66c91a099098e561162ca8cf6ffbf8b1b8a4ba8d9f86d
+size 51466383

SynthData0523/main/c19/arf/arf-c19-20260424_051258/arf_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04d2bf0f93497fbc7aa97514242a7c545c9e9e4dbea2aa32451a417665b79c0b
+size 337265046

SynthData0523/main/c19/arf/arf-c19-20260424_051258/gen_20260424_052254.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f874f72369a93413f05328b62bb234c71ac759ade7807ecd8e45634b4b51cb02
+size 21101

SynthData0523/main/c19/arf/arf-c19-20260424_051258/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53f30d2a1ff84038c9d8cfffd0a3226c664fa71808ce985a20197aeafe9154da
+size 1361

SynthData0523/main/c19/arf/arf-c19-20260424_051258/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29277905718aadff6392f1a493cc90a009209da285eb3c9cfaa6ed315532ed07
+size 15890

SynthData0523/main/c19/arf/arf-c19-20260424_051258/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f3b9879993ec6898672e12c529e80204564945d19d90073329035241094707d
+size 925

SynthData0523/main/c19/arf/arf-c19-20260424_051258/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:303e249deaf8f6d2a3a58236b048792cac057814539c21e7bbc31ab46d2db4c6
+size 16641

SynthData0523/main/c19/arf/arf-c19-20260424_051258/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d008ebe12a4b3dcacaf96ed51903d38e28613fc2aadea4d33cde2b08e5c9bdc2
+size 573

SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/arf/adapter_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6dd8964abeecab077dbd57166ebe57b29dd8ce9e0f3652f4c28fcd9f6846f634
+size 306

SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/arf/adapter_transforms_applied.json

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

SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/arf/model_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab0ca8f2bd6ba6b4f323f41be5eb7133b5e5c0bab63d36b3fd7883cc58255cf2
+size 16823

SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/public/staged_features.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a57a0c3c2a45c85e74d4496ad733ad7f30b2615f97be5155850895fee1727948
+size 1564

SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd9c98343a92c7b1afe63b402f07b9a55013adbfcc60ec1e17d5e026385eeec8
+size 6304860

SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:092346325db7f445db2c00d2f5dd9a8397ecc33eaba7b2b14d9d48d92659fcfc
+size 51459027

SynthData0523/main/c19/arf/arf-c19-20260424_051258/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69c75639e81916d3b4f2a28db38d7e15c78e442748aa5bf9fed2eb3784912a70
+size 6331589

SynthData0523/main/c19/arf/arf-c19-20260424_051258/train_20260424_051302.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f783f62f90fc7a048eab4d9601802a48f4b029b4702da36c63b03b0a144c92a
+size 624

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/_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-SpecializedModels/c19/bayesnet/bayesnet-c19-20260422_192809/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(32759)
+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-SpecializedModels/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet-c19-32759-20260422_192846.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet-c19-32759-20260422_192846.csv")

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/_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-SpecializedModels/c19/bayesnet/bayesnet-c19-20260422_192809/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/c19/bayesnet/bayesnet-c19-20260422_192809/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/c19/bayesnet/bayesnet-c19-20260422_192809/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-SpecializedModels/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet_model.pkl")

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet-c19-32759-20260422_192846.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0bf1d0a877ba735c5fad36e25343883d54170ac0f24b87dcfd22e0df97bc3fe8
+size 12002424

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet_coltypes.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:824d8a2b5a901837769991f65ba261818dbe2589a985464485da14f9cf893cf2
+size 1162

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/bayesnet_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:133949fdd908b50d09a312ea1d975cc6d1765d7b2952a8e1b49081067745d7a6
+size 4111174

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/const_cols.json

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

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/gen_20260422_192846.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d2e25e055b57077adda4068181bcbc6427aef056a6a4d7e706dc283b5e0d6cd
+size 3393

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abeff74152f802f0e06e5cdb720e6e285127a1e97b8f15c44f8c9e09aad598e8
+size 1366

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29277905718aadff6392f1a493cc90a009209da285eb3c9cfaa6ed315532ed07
+size 15890

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f3b9879993ec6898672e12c529e80204564945d19d90073329035241094707d
+size 925

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b2a47f9b86e807eff9ffdd5600ab56d33c7a1a07c42242e2ec98cf8a9e3d4dc
+size 16691

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e42b48b3e99174486fc3206b687cc5f9ec1200712cfd8e2b4a6b51f31ebda72
+size 613

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/bayesnet/adapter_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2152fcecf5bb7b7ab05b8615971ee42ab85b7a7657794af22a14c1895cce569f
+size 321

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/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/c19/bayesnet/bayesnet-c19-20260422_192809/staged/bayesnet/model_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0179df816489bddd2c4928fb62454f583e3e3e5c849839ebfff1f45b2a29fa0a
+size 16888

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/public/staged_features.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a57a0c3c2a45c85e74d4496ad733ad7f30b2615f97be5155850895fee1727948
+size 1564

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd9c98343a92c7b1afe63b402f07b9a55013adbfcc60ec1e17d5e026385eeec8
+size 6304860

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:092346325db7f445db2c00d2f5dd9a8397ecc33eaba7b2b14d9d48d92659fcfc
+size 51459027

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69c75639e81916d3b4f2a28db38d7e15c78e442748aa5bf9fed2eb3784912a70
+size 6331589

SynthData0523/main/c19/bayesnet/bayesnet-c19-20260422_192809/train_20260422_192819.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c3aaf58a023b18cf28e974d70699520a2eeb02768c612cbdf6154f76b14f372
+size 3532

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/_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-SpecializedModels/c19/ctgan/ctgan-c19-20260422_031259/models_300epochs/ctgan_300epochs.pt")
+total = 32759
+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/c19/ctgan/ctgan-c19-20260422_031259/ctgan-c19-32759-20260422_120804.csv", index=False)
+print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/c19/ctgan/ctgan-c19-20260422_031259/ctgan-c19-32759-20260422_120804.csv")

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/ctgan-c19-32759-20260422_120804.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42968504093193c724698f10a378f450f2a7395fcc498ba794a8455f136aac96
+size 47984045

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/ctgan_metadata.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:761007e5936a02e408256fa21d2e8db8b85fc746a47d8b03a9e4bc007150ec0e
+size 1137

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/gen_20260422_120804.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf76534d353e6ba75c7db393080d194422e95a9bb9a25eff779f994e0c3d2f67
+size 297

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:05e336c5e655f0aaa032f285dac0f738602f58dc9ddacc55f901bd8b63103520
+size 1363

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/models_300epochs/ctgan_300epochs.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d212eecbb082d7ea153cde5bac2a5d595e367f28062c913b8ccc2a1eec6f9199
+size 6525627939

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/models_300epochs/train_20260422_031311.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8432f4f19dfc3e85d71558d61a647d69f6bfa67f01288feec6335229f0388475
+size 2922

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29277905718aadff6392f1a493cc90a009209da285eb3c9cfaa6ed315532ed07
+size 15890

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f3b9879993ec6898672e12c529e80204564945d19d90073329035241094707d
+size 925

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d8135490dd7ee751cce5e8b97b46674b2467e8c2f81c42d736218591c3df87d
+size 16661

SynthData0523/main/c19/ctgan/ctgan-c19-20260422_031259/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f982e86c34d3cb7856f74e9dbe669bd85c52de918a84f5ae5c6751d17f1c8fa2
+size 609