Resume SynthData0523 main/c5 batch 4

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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c5/tabddpm/tabddpm-c5-20260422_211246/staged/public/val.csv",
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+
+import os, shutil, subprocess, sys
+td = r"/workspace/TabDiff"
+name = r"pipeline_ds"
+src = r"/work/output-SpecializedModels/c5/tabdiff/tabdiff-c5-20260420_055925/tabular_bundle/pipeline_ds"
+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_ds/adapter_learnable/model_500.pt",
+    "--num_samples_to_generate", str(int(6732)),
+])
+# test() 写入 tabdiff/result/<dataname>/<exp>/<epoch>/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-SpecializedModels/c5/tabdiff/tabdiff-c5-20260420_055925/tabdiff-c5-6732-20260420_060648.csv")

SynthData0523/main/c5/tabdiff/tabdiff-c5-20260420_055925/_tabdiff_train.py

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+
+import os, shutil, subprocess, sys
+td = r"/workspace/TabDiff"
+name = r"pipeline_ds"
+src = r"/work/output-SpecializedModels/c5/tabdiff/tabdiff-c5-20260420_055925/tabular_bundle/pipeline_ds"
+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",
+])

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SynthData0523/main/c5/tabpfgen/tabpfgen-c5-20260511_061054/_tabpfgen_generate.py

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+import os
+import numpy as np
+import pandas as pd
+import json
+from tabpfgen import TabPFGen
+
+df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c5/tabpfgen/tabpfgen-c5-20260511_061054/staged/public/train.csv")
+target_col = "class"
+
+target_missing = df[target_col].isna()
+if target_missing.any():
+    dropped = int(target_missing.sum())
+    df = df.loc[~target_missing].copy()
+    print(
+        f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'"
+    )
+if df.empty:
+    raise ValueError(
+        f"[TabPFGen] No rows remain after dropping missing target '{target_col}'"
+    )
+
+feature_cols = [c for c in df.columns if c != target_col]
+
+cat_encodings = {}
+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)")
+
+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
+fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000")))
+if len(X) > fit_rows_cap:
+    rng = np.random.default_rng(42)
+    idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False))
+    X = X[idx]
+    y = y[idx]
+    print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})")
+target_n = int(6732)
+
+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
+
+chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256")))
+device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto"
+
+n_sgld_steps = max(1, int(os.environ.get("TABPFGEN_N_SGLD_STEPS", "1000")))
+sgld_step_size = float(os.environ.get("TABPFGEN_SGLD_STEP_SIZE", "0.01"))
+sgld_noise_scale = float(os.environ.get("TABPFGEN_SGLD_NOISE_SCALE", "0.01"))
+
+# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。
+# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。）
+gen = TabPFGen(
+    n_sgld_steps=n_sgld_steps,
+    sgld_step_size=sgld_step_size,
+    sgld_noise_scale=sgld_noise_scale,
+    device=device,
+)
+
+print(
+    f"[TabPFGen] Generating {target_n} rows via generate_classification "
+    f"(chunk_rows={chunk_rows}, device={device}, "
+    f"n_sgld_steps={n_sgld_steps}, sgld_step_size={sgld_step_size}, "
+    f"sgld_noise_scale={sgld_noise_scale})"
+)
+x_parts = []
+y_parts = []
+remaining = target_n
+while remaining > 0:
+    take = min(chunk_rows, remaining)
+    X_part, y_part = gen.generate_classification(X, y, n_samples=take)
+    x_parts.append(np.asarray(X_part))
+    y_parts.append(np.asarray(y_part))
+    remaining -= take
+    print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}")
+
+X_syn = np.concatenate(x_parts, axis=0)
+y_syn = np.concatenate(y_parts, axis=0)
+
+syn_df = pd.DataFrame(X_syn, columns=feature_cols)
+syn_df[target_col] = y_syn
+
+for col, cats in cat_encodings.items():
+    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]
+
+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:
+        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/output-Benchmark-trainonly-v1/c5/tabpfgen/tabpfgen-c5-20260511_061054/tabpfgen-c5-6732-20260511_061054.csv", index=False)
+print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c5/tabpfgen/tabpfgen-c5-20260511_061054/tabpfgen-c5-6732-20260511_061054.csv")