hyperparameter/c2/tabddpm/tabddpm-c2-20260504_181749/_tabddpm_sample_r0.py

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 1382 rows")
ret = subprocess.run(
    [sys.executable, wrapper, "scripts/pipeline.py",
     "--config", "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/config_sample_20260504_182010_r0.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-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/data/info.json"
with open(info_path) as f:
    info = json.load(f)

output_dir = "/work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/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-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/tabddpm-c2-1382-20260504_182010.csv", index=False)
    print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181749/tabddpm-c2-1382-20260504_182010.csv")
else:
    print("[TabDDPM] WARNING: No output .npy files found")
    sys.exit(1)