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+with open("/work/output-SpecializedModels/n4/arf/arf-n4-20260422_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/n4/arf/arf-n4-20260422_055912/arf-n4-1595-20260422_060945.csv", index=False) +print(f"[ARF] Generated {len(syn)} rows (requested {n_target}) -> /work/output-SpecializedModels/n4/arf/arf-n4-20260422_055912/arf-n4-1595-20260422_060945.csv") diff --git a/SynthData0523/main/n4/arf/arf-n4-20260422_055912/_arf_train.py b/SynthData0523/main/n4/arf/arf-n4-20260422_055912/_arf_train.py new file mode 100644 index 0000000000000000000000000000000000000000..42c73a1f10f325900f7e5b07263f36a10f9b1678 --- /dev/null +++ b/SynthData0523/main/n4/arf/arf-n4-20260422_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/n4/arf/arf-n4-20260422_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/n4/arf/arf-n4-20260422_055912/arf_model.pkl", "wb") as f: + pickle.dump(model, f) +print(f"[ARF] Model saved -> /work/output-SpecializedModels/n4/arf/arf-n4-20260422_055912/arf_model.pkl") diff --git a/SynthData0523/main/n4/arf/arf-n4-20260422_055912/arf-n4-1595-20260422_060945.csv b/SynthData0523/main/n4/arf/arf-n4-20260422_055912/arf-n4-1595-20260422_060945.csv new file mode 100644 index 0000000000000000000000000000000000000000..690a767926ee280d8266bba26ea41c7eedfa64f5 --- /dev/null +++ b/SynthData0523/main/n4/arf/arf-n4-20260422_055912/arf-n4-1595-20260422_060945.csv @@ -0,0 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+1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:577f8277d538d1961df9ee134e06b1d4d9bcc32e92f6abf2adbf7837e4dbd7b6 +size 771 diff --git a/SynthData0523/main/n4/arf/arf-n4-20260422_055912/input_snapshot.json b/SynthData0523/main/n4/arf/arf-n4-20260422_055912/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..ec093cdcb3195e867fe208ecfbbf923c85b9262f --- /dev/null +++ b/SynthData0523/main/n4/arf/arf-n4-20260422_055912/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "n4", + "model": "arf", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n4/n4-train.csv", + "exists": true, + "size": 852257, + "sha256": "24c830718b1e4e6aaca9d053fc1a91c0ae5f1a593db2724f4764e43be56fd575" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/n4/n4-val.csv", + "exists": true, + "size": 107938, + "sha256": "e46401b5f2e6c641b35f87b4ba4677e1ad1f9113bfc1e15cf858db2a793f701f" + 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= 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 {} + +sampler = BayesianModelSampling(network) +raw = sampler.forward_sample(size=1595, show_progress=False) + +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 + +final.to_csv("/work/output-SpecializedModels/n4/bayesnet/bayesnet-n4-20260420_052235/bayesnet-n4-1595-20260420_052617.csv", index=False) +print(f"[BayesNet] Generated 1595 rows -> /work/output-SpecializedModels/n4/bayesnet/bayesnet-n4-20260420_052235/bayesnet-n4-1595-20260420_052617.csv") diff --git a/SynthData0523/main/n4/bayesnet/bayesnet-n4-20260420_052235/_bayesnet_train.py b/SynthData0523/main/n4/bayesnet/bayesnet-n4-20260420_052235/_bayesnet_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ff7cb92634e06f1653a86fa87dbe862af7eb6a2f --- /dev/null +++ b/SynthData0523/main/n4/bayesnet/bayesnet-n4-20260420_052235/_bayesnet_train.py @@ -0,0 +1,93 @@ + +import json +import pickle +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) + +with open("/work/output-SpecializedModels/n4/bayesnet/bayesnet-n4-20260420_052235/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/n4/bayesnet/bayesnet-n4-20260420_052235/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/n4/bayesnet/bayesnet-n4-20260420_052235/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) +max_bins = 10 + +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)") + +dag = TreeSearch(enc).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/n4/bayesnet/bayesnet-n4-20260420_052235/bayesnet_model.pkl", "wb") as _f: + pickle.dump(bundle, _f) +print(f"[BayesNet] Model saved -> /work/output-SpecializedModels/n4/bayesnet/bayesnet-n4-20260420_052235/bayesnet_model.pkl") diff --git a/SynthData0523/main/n4/bayesnet/bayesnet-n4-20260420_052235/bayesnet-n4-1595-20260420_052617.csv b/SynthData0523/main/n4/bayesnet/bayesnet-n4-20260420_052235/bayesnet-n4-1595-20260420_052617.csv new file mode 100644 index 0000000000000000000000000000000000000000..3c9ef7045e4475905ccbec5a1d43d6b96c43620f --- /dev/null +++ b/SynthData0523/main/n4/bayesnet/bayesnet-n4-20260420_052235/bayesnet-n4-1595-20260420_052617.csv @@ -0,0 +1,3 @@ +version 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--git a/SynthData0523/main/n4/ctgan/ctgan-n4-20260422_031259/_ctgan_generate.py b/SynthData0523/main/n4/ctgan/ctgan-n4-20260422_031259/_ctgan_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..8b5b85749787568c7e4ebc1687f777f6b09b403c --- /dev/null +++ b/SynthData0523/main/n4/ctgan/ctgan-n4-20260422_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/n4/ctgan/ctgan-n4-20260422_031259/models_300epochs/ctgan_300epochs.pt") +total = 1595 +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/n4/ctgan/ctgan-n4-20260422_031259/ctgan-n4-1595-20260422_032206.csv", index=False) +print("[CTGAN] Generated", total, "rows in", len(parts), "chunks ->", "/work/output-SpecializedModels/n4/ctgan/ctgan-n4-20260422_031259/ctgan-n4-1595-20260422_032206.csv") \ No newline at end of file diff --git a/SynthData0523/main/n4/ctgan/ctgan-n4-20260422_031259/ctgan-n4-1595-20260422_032206.csv b/SynthData0523/main/n4/ctgan/ctgan-n4-20260422_031259/ctgan-n4-1595-20260422_032206.csv new file mode 100644 index 0000000000000000000000000000000000000000..557fd7da435499b9e0caa90be92e125772097815 --- /dev/null +++ b/SynthData0523/main/n4/ctgan/ctgan-n4-20260422_031259/ctgan-n4-1595-20260422_032206.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8edcc7a4c5c2e02fe284ed9712bbf585df665126d8a1827bf0145322791806c5 +size 3738092 diff --git a/SynthData0523/main/n4/ctgan/ctgan-n4-20260422_031259/ctgan_metadata.json 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columns=meta["column_names"]) +df.to_csv(r'/work/output-Benchmark-trainonly-v1/n4/forestdiffusion/forest-n4-20260511_130614/forest-n4-1595-20260511_132012.csv', index=False) +print("saved", len(df)) diff --git a/SynthData0523/main/n4/forestdiffusion/forest-n4-20260511_130614/_fd_meta_host.json b/SynthData0523/main/n4/forestdiffusion/forest-n4-20260511_130614/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..f0cad1c685e21ea8f08281817e1228bdb29c33f2 --- /dev/null +++ b/SynthData0523/main/n4/forestdiffusion/forest-n4-20260511_130614/_fd_meta_host.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1167007c0f9ede6a9434998d12371707325b03cedcff6afdc8229faea798bafb +size 1845 diff --git a/SynthData0523/main/n4/forestdiffusion/forest-n4-20260511_130614/_fd_train.py b/SynthData0523/main/n4/forestdiffusion/forest-n4-20260511_130614/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..ba5de08f743f3d0555831e3fbd066915fd629be3 --- /dev/null +++ b/SynthData0523/main/n4/forestdiffusion/forest-n4-20260511_130614/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/n4/forestdiffusion/forest-n4-20260511_130614/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/n4/forestdiffusion/forest-n4-20260511_130614/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=2 " + f"n_estimators=1 duplicate_K=1 n_jobs=1 " + f"max_depth=2 xgb_verbosity=0 xgb_nthread=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=2, n_estimators=1, duplicate_K=1, n_jobs=1, + model="xgboost", max_depth=2, tree_method="hist", cat_indexes=cat_indexes, + verbosity=0, nthread=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/n4/forestdiffusion/forest-n4-20260511_130614/forestdiffusion_model.joblib') diff --git a/SynthData0523/main/n4/forestdiffusion/forest-n4-20260511_130614/forest-n4-1595-20260511_132012.csv b/SynthData0523/main/n4/forestdiffusion/forest-n4-20260511_130614/forest-n4-1595-20260511_132012.csv new file mode 100644 index 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100644 index 0000000000000000000000000000000000000000..f94776b2261e3e82b121221376416d42be61a245 --- /dev/null +++ b/SynthData0523/main/n4/realtabformer/rtf-n4-20260330_171755/train_20260330_171755.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfe6c1468fb305b1bf4e474a1088fd533e2a7f268aebb40a24f083be46a034e2 +size 372500 diff --git a/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/_tabbyflow_gen.py b/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/_tabbyflow_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..de81895297fe9f292a4e330aac820e07559af132 --- /dev/null +++ b/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/_tabbyflow_gen.py @@ -0,0 +1,33 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/n4/tabbyflow/tabbyflow-n4-20260424_004926/tabular_bundle/pipeline_ds" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_tabbyflow-n4-20260424_004926", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_ds/adapter_tabbyflow-n4-20260424_004926/model_500.pt", + "--num_samples_to_generate", str(int(1595)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_tabbyflow-n4-20260424_004926") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-SpecializedModels/n4/tabbyflow/tabbyflow-n4-20260424_004926/tabbyflow-n4-1595-20260424_005118.csv") diff --git a/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/_tabbyflow_train.py b/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..97df8e6a002a198dba5141c42b59224f6e5aa13f --- /dev/null +++ b/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_ds" +src = r"/work/output-SpecializedModels/n4/tabbyflow/tabbyflow-n4-20260424_004926/tabular_bundle/pipeline_ds" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_tabbyflow-n4-20260424_004926", +]) diff --git a/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/input_snapshot.json b/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..66c610fb42fdc48c931b2475c7792f93f2bc2d17 --- /dev/null +++ b/SynthData0523/main/n4/tabbyflow/tabbyflow-n4-20260424_004926/input_snapshot.json @@ -0,0 +1,3 @@ +version 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b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/_tabddpm_sample.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 1595 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260425_074426.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/tabddpm-n4-1595-20260425_074426.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/tabddpm-n4-1595-20260425_074426.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/_tabddpm_train.py b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7020dcf9d85d71365fd131626fdc96ccc691aa98 --- /dev/null +++ b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config.toml b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..87fb8eaa3ab8c245d4b53cc49d686aca2b08aab2 --- /dev/null +++ b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/output" +real_data_path = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/data" +model_type = "mlp" +num_numerical_features = 126 +device = "cuda:0" + +[model_params] +d_in = 127 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260424_210638.toml b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260424_210638.toml new file mode 100644 index 0000000000000000000000000000000000000000..2493f241d0c8b86904ebec87f230a7806c01f414 --- /dev/null +++ b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260424_210638.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/output" +real_data_path = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/data" +model_type = "mlp" +num_numerical_features = 126 +device = "cuda:0" + +[model_params] +d_in = 127 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1595 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260425_032135.toml b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260425_032135.toml new file mode 100644 index 0000000000000000000000000000000000000000..2493f241d0c8b86904ebec87f230a7806c01f414 --- /dev/null +++ b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260425_032135.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/output" +real_data_path = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/data" +model_type = "mlp" +num_numerical_features = 126 +device = "cuda:0" + +[model_params] +d_in = 127 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1595 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260425_074426.toml b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260425_074426.toml new file mode 100644 index 0000000000000000000000000000000000000000..2493f241d0c8b86904ebec87f230a7806c01f414 --- /dev/null +++ b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/config_sample_20260425_074426.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/output" +real_data_path = "/work/output-SpecializedModels/n4/tabddpm/tabddpm-n4-20260321_155313/data" +model_type = "mlp" +num_numerical_features = 126 +device = "cuda:0" + +[model_params] +d_in = 127 +num_classes = 0 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1595 +batch_size = 1000 +seed = 0 diff --git a/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/data/X_cat_test.npy b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/data/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..ed03801bbc77dbf0f721731653d1803ceaf56995 --- /dev/null +++ b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/data/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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b/SynthData0523/main/n4/tabddpm/tabddpm-n4-20260321_155313/train_20260321_155314.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dcdaf66385896f656f3dcdf870d7b639c850d28f5c3a50720971de5c0e89ae95 +size 9216 diff --git a/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/_tabdiff_gen.py b/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/_tabdiff_gen.py new file mode 100644 index 0000000000000000000000000000000000000000..4150ca05091da459c8f04db0b687634112c69a4c --- /dev/null +++ b/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/_tabdiff_gen.py @@ -0,0 +1,36 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n4" +src = r"/work/output-Benchmark-trainonly-v1/n4/tabdiff/tabdiff-n4-20260501_175728/tabular_bundle/pipeline_n4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", + "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_n4/adapter_learnable/model_500.pt", + "--num_samples_to_generate", str(int(1595)), +]) +# test() 写入 tabdiff/result////samples.csv +import glob as g +base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable") +best = None +best_t = -1.0 +for root, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(root, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t = t + best = p +if not best: + raise SystemExit("tabdiff: no samples.csv under " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/n4/tabdiff/tabdiff-n4-20260501_175728/tabdiff-n4-1595-20260501_180141.csv") diff --git a/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/_tabdiff_train.py b/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/_tabdiff_train.py new file mode 100644 index 0000000000000000000000000000000000000000..95a7b3c1b95e134a9270b45c8ba9d992cb073d10 --- /dev/null +++ b/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/_tabdiff_train.py @@ -0,0 +1,21 @@ + +import os, shutil, subprocess, sys +td = r"/workspace/TabDiff" +name = r"pipeline_n4" +src = r"/work/output-Benchmark-trainonly-v1/n4/tabdiff/tabdiff-n4-20260501_175728/tabular_bundle/pipeline_n4" +dst_data = os.path.join(td, "data", name) +dst_syn = os.path.join(td, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(td) +os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["TABDIFF_SMOKE_STEPS"] = "500" +os.environ["TABDIFF_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "-m", "tabdiff.main", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_learnable", +]) diff --git a/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/gen_20260501_180141.log b/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/gen_20260501_180141.log new file mode 100644 index 0000000000000000000000000000000000000000..cc50806e1f0c442852ea8c2dd8914ae5a0b9bcc9 --- /dev/null +++ b/SynthData0523/main/n4/tabdiff/tabdiff-n4-20260501_175728/gen_20260501_180141.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9464e6c95e314108b6e9fb239cd3fb498c365a5a370e259a4a89d7bd4bd68832 +size 5934 diff --git 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+size 328980 diff --git a/SynthData0523/main/n4/tabpfgen/n4-migrated-20260422_193053/_tabpfgen_generate.py b/SynthData0523/main/n4/tabpfgen/n4-migrated-20260422_193053/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..9838bac20e9b31aad7c2d1357bf432ff33b5e04b --- /dev/null +++ b/SynthData0523/main/n4/tabpfgen/n4-migrated-20260422_193053/_tabpfgen_generate.py @@ -0,0 +1,87 @@ +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n4/staged/public/train.csv") +target_col = "target" + +feature_cols = [c for c in df.columns if c != target_col] + +# --- Label-encode categorical / object columns --- +cat_encodings = {} # col -> list of unique values (index = code) +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +# Encode target if categorical +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(1595) + +# Handle NaN +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_regression") +X_syn, y_syn = gen.generate_regression(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +# --- Inverse label-encoding for categorical columns --- +for col, cats in cat_encodings.items(): + # Round to nearest integer index, clamp to valid range + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +# Ensure output row count is strictly aligned with target_n. +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat([syn_df.reset_index(drop=True), extra.reset_index(drop=True)], ignore_index=True) + else: + # Defensive fallback: if generator returns empty, bootstrap from training rows. + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError(f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}") +syn_df.to_csv("/work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n4/tabpfgen-n4-1595-20260422_191742.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/temp/tabpfgen_regen_parallel_deadline/20260422_191739/n4/tabpfgen-n4-1595-20260422_191742.csv") diff --git a/SynthData0523/main/n4/tabpfgen/n4-migrated-20260422_193053/gen_20260422_191742.log 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a/SynthData0523/main/n4/tabpfgen/n4-migrated-20260422_193053/tabpfgen-n4-1595-20260422_191742.csv b/SynthData0523/main/n4/tabpfgen/n4-migrated-20260422_193053/tabpfgen-n4-1595-20260422_191742.csv new file mode 100644 index 0000000000000000000000000000000000000000..19994855e2ebd231d461c2910366e53e19b2e056 --- /dev/null +++ b/SynthData0523/main/n4/tabpfgen/n4-migrated-20260422_193053/tabpfgen-n4-1595-20260422_191742.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05bce53e7789d4a1e50698c31aacc59069624f8397082ad5ffaab89a0f5e4329 +size 2194935 diff --git a/SynthData0523/main/n4/tvae/tvae-n4-20260328_053052/_tvae_generate.py b/SynthData0523/main/n4/tvae/tvae-n4-20260328_053052/_tvae_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..0e478e7101394e42ddb798399d7a804fe4540238 --- /dev/null +++ b/SynthData0523/main/n4/tvae/tvae-n4-20260328_053052/_tvae_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.tvae import TVAE +model = TVAE.load("/work/output-SpecializedModels/n4/tvae/tvae-n4-20260328_053052/models_300epochs/tvae_300epochs.pt") +total = 1595 +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 +samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0] +samples.to_csv("/work/output-SpecializedModels/n4/tvae/tvae-n4-20260328_053052/tvae-n4-1595-20260420_030435.csv", index=False) +print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-SpecializedModels/n4/tvae/tvae-n4-20260328_053052/tvae-n4-1595-20260420_030435.csv") diff --git a/SynthData0523/main/n4/tvae/tvae-n4-20260328_053052/_tvae_train.py b/SynthData0523/main/n4/tvae/tvae-n4-20260328_053052/_tvae_train.py new file mode 100644 index 0000000000000000000000000000000000000000..822218748af528d7b29ef9c2cbf7c2c257dd2355 --- /dev/null +++ b/SynthData0523/main/n4/tvae/tvae-n4-20260328_053052/_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/n4/tvae/tvae-n4-20260328_053052/staged/public/train.csv" +meta_path = "/work/output-SpecializedModels/n4/tvae/tvae-n4-20260328_053052/tvae_metadata.json" +save_path = "/work/output-SpecializedModels/n4/tvae/tvae-n4-20260328_053052/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}") diff --git 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