Hyper ParameterTuning/n11/tabdiff/runs/tabdiff-n11-20260520_234635/_tabdiff_train.py

import os, shutil, subprocess, sys
td = r"/workspace/TabDiff"
name = r"pipeline_n11"
src = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260520_234635/tabular_bundle/pipeline_n11"
rt = r"/work/output-Benchmark-trainonly-v1/n11/tabdiff/tabdiff-n11-20260520_234635/_tabdiff_runtime"
shutil.rmtree(rt, ignore_errors=True)

def _ignore(_, names):
    skip = {"__pycache__", "data", "synthetic", "result", "results", "ckpt"}
    return [n for n in names if n in skip or n.endswith(".pyc")]

shutil.copytree(td, rt, ignore=_ignore)

def _replace_once(path, old, new):
    text = open(path, "r", encoding="utf-8").read()
    if old not in text:
        raise RuntimeError(f"patch anchor not found in {path}")
    text = text.replace(old, new, 1)
    with open(path, "w", encoding="utf-8") as f:
        f.write(text)

_replace_once(
    os.path.join(rt, "utils_train.py"),
    "        X_train_num, X_test_num = X_num['train'], X_num['test']\n        X_train_cat, X_test_cat = X_cat['train'], X_cat['test']\n        \n        categories = src.get_categories(X_train_cat)\n",
    "        X_train_num, X_test_num = X_num['train'], X_num['test']\n        if X_cat is None:\n            X_train_cat = np.empty((X_train_num.shape[0], 0), dtype=np.int64)\n            X_test_cat = np.empty((X_test_num.shape[0], 0), dtype=np.int64)\n            categories = []\n        else:\n            X_train_cat, X_test_cat = X_cat['train'], X_cat['test']\n            categories = src.get_categories(X_train_cat)\n",
)
_replace_once(
    os.path.join(rt, "utils_train.py"),
    "        X_cat = {} if os.path.exists(os.path.join(data_path, 'X_cat_train.npy'))  else None\n        X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) else None\n        y = {} if os.path.exists(os.path.join(data_path, 'y_train.npy')) else None\n",
    "        has_cat = os.path.exists(os.path.join(data_path, 'X_cat_train.npy'))\n        X_cat = {} if (has_cat or concat) else None\n        X_num = {} if os.path.exists(os.path.join(data_path, 'X_num_train.npy')) else None\n        y = {} if os.path.exists(os.path.join(data_path, 'y_train.npy')) else None\n",
)
_replace_once(
    os.path.join(rt, "src", "data.py"),
    "        num_workers=1,\n",
    "        num_workers=int(os.environ.get('TABDIFF_NUM_WORKERS', '0')),\n",
)
_replace_once(
    os.path.join(rt, "src", "data.py"),
    "    loader = torch.utils.data.DataLoader(torch_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=1)\n",
    "    loader = torch.utils.data.DataLoader(torch_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=int(os.environ.get('TABDIFF_NUM_WORKERS', '0')))\n",
)
_replace_once(
    os.path.join(rt, "tabdiff", "main.py"),
    "    if os.environ.get(\"TABDIFF_ADAPTER_TRAIN\", \"\").strip() and args.mode == \"train\":\n        raw_config[\"train\"][\"main\"][\"check_val_every\"] = int(raw_config[\"train\"][\"main\"][\"steps\"])\n\n    ## Load training data\n",
    "    if os.environ.get(\"TABDIFF_ADAPTER_TRAIN\", \"\").strip() and args.mode == \"train\":\n        raw_config[\"train\"][\"main\"][\"check_val_every\"] = int(raw_config[\"train\"][\"main\"][\"steps\"])\n\n    _train_batch = os.environ.get(\"TABDIFF_BATCH_SIZE\", \"\").strip() or os.environ.get(\"TABDIFF_TRAIN_BATCH_SIZE\", \"\").strip()\n    if _train_batch:\n        raw_config[\"train\"][\"main\"][\"batch_size\"] = max(1, int(_train_batch))\n    _sample_batch = os.environ.get(\"TABDIFF_SAMPLE_BATCH_SIZE\", \"\").strip()\n    if _sample_batch:\n        raw_config[\"sample\"][\"batch_size\"] = max(1, int(_sample_batch))\n    _train_lr = os.environ.get(\"TABDIFF_LR\", \"\").strip() or os.environ.get(\"TABDIFF_LEARNING_RATE\", \"\").strip()\n    if _train_lr:\n        raw_config[\"train\"][\"main\"][\"lr\"] = float(_train_lr)\n    _num_timesteps = os.environ.get(\"TABDIFF_NUM_TIMESTEPS\", \"\").strip() or os.environ.get(\"TABDIFF_TIMESTEPS\", \"\").strip()\n    if _num_timesteps:\n        raw_config[\"diffusion_params\"][\"num_timesteps\"] = max(1, int(_num_timesteps))\n\n    ## Load training data\n",
)
_replace_once(
    os.path.join(rt, "tabdiff", "main.py"),
    "        num_workers = 4,\n",
    "        num_workers = int(os.environ.get('TABDIFF_NUM_WORKERS', '0')),\n",
)
dst_data = os.path.join(rt, "data", name)
dst_syn = os.path.join(rt, "synthetic", name)
shutil.rmtree(dst_data, ignore_errors=True)
os.makedirs(os.path.dirname(dst_data), exist_ok=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(rt)
os.environ["PYTHONPATH"] = rt + os.pathsep + os.environ.get("PYTHONPATH", "")
os.environ["TABDIFF_SMOKE_STEPS"] = "300"
os.environ["TABDIFF_STEPS"] = "300"
os.environ["TABDIFF_BATCH_SIZE"] = "256"
os.environ["TABDIFF_TRAIN_BATCH_SIZE"] = "256"
os.environ["TABDIFF_SAMPLE_BATCH_SIZE"] = "256"
os.environ["TABDIFF_LR"] = "0.0002"
os.environ["TABDIFF_LEARNING_RATE"] = "0.0002"
os.environ["TABDIFF_NUM_TIMESTEPS"] = "100"
os.environ["TABDIFF_TIMESTEPS"] = "100"
os.environ["TABDIFF_NUM_WORKERS"] = "0"
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",
])