| 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", "")) |
|
|
| |
| 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) |
|
|
| |
| 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) |
|
|
