Add files using upload-large-folder tool

syntheticSuccess/c15/arf/arf-c15-20260423_090001/_arf_generate.py

@@ -0,0 +1,79 @@
+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(480000)
+c_csv = "/work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/staged/public/train.csv"
+with open("/work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/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]
+
+syn.to_csv("/work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/arf-c15-480000-20260423_133619.csv", index=False)
+print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/arf-c15-480000-20260423_133619.csv")

syntheticSuccess/c15/arf/arf-c15-20260423_090001/_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/c15/arf/arf-c15-20260423_090001/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/c15/arf/arf-c15-20260423_090001/arf_model.pkl", "wb") as f:
+    pickle.dump(model, f)
+print(f"[ARF] Model saved -> /work/output-SpecializedModels/c15/arf/arf-c15-20260423_090001/arf_model.pkl")

syntheticSuccess/c15/arf/arf-c15-20260423_090001/gen_20260423_133619.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d238af22252391aa68468aedfbf5b3789b59d93ab15365400968cf999b695ab7
+size 5836

syntheticSuccess/c15/arf/arf-c15-20260423_090001/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1eb8e4ae781e14d13f0dac87d7aa4a8148cc69e1f703f65b3d5dbbe2a9f046c6
+size 1360

syntheticSuccess/c15/arf/arf-c15-20260423_090001/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e18dc098fd61958a51c24594cd5bad03aad3e07a097380b3aa9d6351ee18824a
+size 11438

syntheticSuccess/c15/arf/arf-c15-20260423_090001/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c491eb29211dbb52507826c49cabccf8fe3583c072f7f08822b86a2769181aad
+size 920

syntheticSuccess/c15/arf/arf-c15-20260423_090001/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:508e90cdd49cbc94d2fa7f696f2d54e36c2b486532a7beaa28171b6d4285ee3d
+size 12189

syntheticSuccess/c15/arf/arf-c15-20260423_090001/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8b48277a73ccc867becc1a1c03738751bd6ff90d46819531a0a8724a7f9a449
+size 574

syntheticSuccess/c15/arf/arf-c15-20260423_090001/staged/arf/adapter_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ab78b8f5e8824ce493c7a45b46db58ef9367bde54396a712f82895880acf0ce
+size 306

syntheticSuccess/c15/arf/arf-c15-20260423_090001/staged/arf/adapter_transforms_applied.json

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

syntheticSuccess/c15/arf/arf-c15-20260423_090001/staged/arf/model_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f315849823f124889be0dd78d99eff36352b68c328443df432abb9bcc092ca69
+size 12371

syntheticSuccess/c15/arf/arf-c15-20260423_090001/staged/public/staged_features.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d5884fb0f913ab783893461f45f8c28269069b45754d30e21de3ff7da579227
+size 2300

syntheticSuccess/c15/arf/arf-c15-20260423_090001/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e68fec0fb16fb89b5e58bbb7949b744ebd11f8bf7b1d0c7aad908b17a2afb72
+size 8530452

syntheticSuccess/c15/arf/arf-c15-20260423_090001/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db13b576ba5284f2712b174f1b4445147bcb12fa295a4e38a1dc269d999d09fa
+size 8528882

syntheticSuccess/c15/arf/arf-c15-20260423_090001/train_20260423_090029.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:747404cdc69e823dbf5dd18f2640a49c31b931c53422ceeef631f117d0f42c4f
+size 235

syntheticSuccess/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_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(480000)
+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/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet-c15-480000-20260422_060347.csv", index=False)
+print(f"[BayesNet] Generated {len(final)} rows (requested {num_rows}) -> /work/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet-c15-480000-20260422_060347.csv")

syntheticSuccess/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_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/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet_model.pkl", "wb") as _f:
+    pickle.dump(bundle, _f)
+print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet_model.pkl")

syntheticSuccess/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet-c15-480000-20260422_060347.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48028e7a5f58b2d8248ec06582ad605755c43e364d49725d90dd00fef8dba1c4
+size 118223581

syntheticSuccess/c15/bayesnet/bayesnet-c15-20260422_060152/bayesnet_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efbb176f729aaa284c2246b9242fc1f22beb85154506625ca24a5782cbcdf3c3
+size 62558615

syntheticSuccess/c15/bayesnet/bayesnet-c15-20260422_060152/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e60fda0bb5a782d4e6917157f5a204d44e8e15de208c863574afc98855561477
+size 68240502

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/_tabsyn_sample.py

@@ -0,0 +1,39 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c15/tabsyn/tabsyn-c15-20260426_203054"
+dataname = "tabsyn_c15"
+output_csv = "/work/output-SpecializedModels/c15/tabsyn/tabsyn-c15-20260426_203054/tabsyn-c15-480000-20260427_004432.csv"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Ensure data symlink exists
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+print(f"[TabSyn] Sampling 480000 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "sample",
+     "--method", "tabsyn",
+     "--gpu", "0",
+     "--save_path", output_csv],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print(f"[TabSyn] Saved -> {output_csv}")

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/_tabsyn_train.py

@@ -0,0 +1,63 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/c15/tabsyn/tabsyn-c15-20260426_203054"
+dataname = "tabsyn_c15"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Symlink data dir into TabSyn data/
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024")
+_te = 1000
+if _te is not None:
+    env["TABSYN_VAE_EPOCHS"] = str(_te)
+    env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2))
+
+# Data preprocessing is done on the host side (_prepare_data_dir)
+# which creates .npy files, train/test CSVs, and info.json
+
+# Step 1: Train VAE (produces latent embeddings)
+print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "vae",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] VAE training failed")
+    sys.exit(ret.returncode)
+
+# Step 2: Train diffusion model on latent space
+print(f"[TabSyn] Step 2/2: Training diffusion model")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "tabsyn",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] Diffusion training failed")
+    sys.exit(ret.returncode)
+print("[TabSyn] Training complete (VAE + Diffusion)")

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/gen_20260427_004432.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:24e340f5ad4084793aa4ebd32bf2039c406c7a69bf89010ebd3c58cb0c78c015
+size 679

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3519d1c1389441f2f9778424f485f92414110fe79fd872f8bd6e230dee10c87d
+size 1363

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e18dc098fd61958a51c24594cd5bad03aad3e07a097380b3aa9d6351ee18824a
+size 11438

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c491eb29211dbb52507826c49cabccf8fe3583c072f7f08822b86a2769181aad
+size 920

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dedf1274d80a62e937663a6e63320c5ae865d44b70d87cd5302feabab746f930
+size 12219

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdb2639e52e69303dfded36d3828c31af5980745c817ad29af94e94baf7a0c1e
+size 581

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db13b576ba5284f2712b174f1b4445147bcb12fa295a4e38a1dc269d999d09fa
+size 8528882

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/tabsyn-c15-480000-20260427_004432.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce1790f2b5f44d0f70021193548fceaf51297496647379ca5e0945f6a689b5de
+size 39501567

syntheticSuccess/c15/tabsyn/tabsyn-c15-20260426_203054/train_20260426_203129.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cafeb386341049771c2eb39fef3aa9adc8e95692a983a9e0dde3c25dfe120cf
+size 19257681

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/_tvae_generate.py

@@ -0,0 +1,9 @@
+import sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
+apply_ctgan_inverse_fix()
+from ctgan.synthesizers.tvae import TVAE
+model = TVAE.load("/work/output-SpecializedModels/c15/tvae/tvae-c15-20260419_073541/models_300epochs/tvae_300epochs.pt")
+samples = model.sample(480000)
+samples.to_csv("/work/output-SpecializedModels/c15/tvae/tvae-c15-20260419_073541/tvae-c15-480000-20260419_181017.csv", index=False)
+print(f"[TVAE] Generated 480000 rows -> /work/output-SpecializedModels/c15/tvae/tvae-c15-20260419_073541/tvae-c15-480000-20260419_181017.csv")

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/_tvae_train.py

@@ -0,0 +1,16 @@
+import json, sys
+import pandas as pd
+from ctgan.data import read_csv
+from ctgan.synthesizers.tvae import TVAE
+
+csv_path = "/work/output-SpecializedModels/c15/tvae/tvae-c15-20260419_073541/staged/public/train.csv"
+meta_path = "/work/output-SpecializedModels/c15/tvae/tvae-c15-20260419_073541/tvae_metadata.json"
+save_path = "/work/output-SpecializedModels/c15/tvae/tvae-c15-20260419_073541/models_300epochs/tvae_300epochs.pt"
+epochs = 300
+
+data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
+print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}")
+model = TVAE(epochs=epochs, batch_size=500)
+model.fit(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/gen_20260419_133821.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22602b513ac2753eadfeee4e57acbce4adcd05e99392272d3529ecf90be17258
+size 1702

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/gen_20260419_181017.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9223c3850a3d9ac88c352f97b578f341b3fcf27cebf8b6dd51829f61a25acaad
+size 133

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/input_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b95499c4583f2995874b49bb5f54d4dfe1596d5612689392c26969eed1d62f31
+size 1361

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/models_300epochs/train_20260419_073620.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3041cf26a2949d35e95d3dec72eda8c6bb9d0fd773432594d716545506e47d80
+size 174

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/models_300epochs/tvae_300epochs.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b280a4c429ca1a35cd6d64a48709fd219df2b0f5ca59cfed2c6b3ded9da46db1
+size 11796972

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e18dc098fd61958a51c24594cd5bad03aad3e07a097380b3aa9d6351ee18824a
+size 11438

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/public_gate/public_gate_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c491eb29211dbb52507826c49cabccf8fe3583c072f7f08822b86a2769181aad
+size 920

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/public_gate/staged_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3dec0124eb2ef791e1a59b07043a360242d45343883e27db4609c98ee6b6416
+size 12199

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/runtime_result.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:153268140c0ea5297b23c4e1c5c02a0eaf23c4b0e4d8a7eddd809f40c2c445bf
+size 443

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/staged/public/staged_features.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d5884fb0f913ab783893461f45f8c28269069b45754d30e21de3ff7da579227
+size 2300

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e68fec0fb16fb89b5e58bbb7949b744ebd11f8bf7b1d0c7aad908b17a2afb72
+size 8530452

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db13b576ba5284f2712b174f1b4445147bcb12fa295a4e38a1dc269d999d09fa
+size 8528882

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/staged/tvae/adapter_report.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed822b2c6619454395cecd02570a3ee74f389e3912687f800d7d5a4c86901c35
+size 309

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/staged/tvae/adapter_transforms_applied.json

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

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/staged/tvae/model_input_manifest.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab89d678d74aa7ff3d237e8d74be6bb09317d38af9cabce8aab7267837e89bd3
+size 12384

syntheticSuccess/c15/tvae/tvae-c15-20260419_073541/tvae_metadata.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd610e35b3b9ab1469d9890cb5ee11c7eacc5cf3ccda8f2374e53571cc824a76
+size 1610