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"""预处理原始 HDF5 数据集以供代理模型训练。
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脚本根据实验 tag 或显式路径读取原始曲线数据，调用统一预处理函数完成参数变换、
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曲线清洗/标准化、训练验证测试划分和 scaler 保存，输出后续训练脚本直接消费的
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processed 数据文件。
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"""
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# pylint: disable=import-error,wrong-import-position
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from __future__ import annotations
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import argparse
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.append(str(ROOT))
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from src.common.experiment_paths import normalize_tag, processed_path_for_tag
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from src.data.preprocess import preprocess_dataset
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def main() -> None:
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"""把原始 HDF5 曲线数据切分、标准化并保存为训练用 pkl。"""
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parser = argparse.ArgumentParser(
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description="Preprocess HDF5 dataset for forward surrogate"
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)
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parser.add_argument(
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"--input",
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type=str,
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required=True,
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help="Path to the generated .h5 dataset",
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)
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parser.add_argument(
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"--output",
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type=str,
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default=None,
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help="Optional output .pkl path",
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)
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parser.add_argument(
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"--tag",
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type=str,
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default=None,
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help="Experiment tag for auto naming",
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)
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parser.add_argument("--test-size", type=float, default=0.15)
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parser.add_argument("--val-size", type=float, default=0.15)
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parser.add_argument("--seed", type=int, default=42)
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parser.add_argument(
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"--no-param-feature-transform",
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action="store_true",
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help="Keep raw physical parameters before StandardScaler; default uses log/asinh features",
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)
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args = parser.parse_args()
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tag = normalize_tag(args.tag)
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output_path = (
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Path(args.output)
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if args.output is not None
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else processed_path_for_tag(tag)
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)
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preprocess_dataset(
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input_path=Path(args.input),
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output_path=output_path,
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test_size=args.test_size,
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val_size=args.val_size,
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random_seed=args.seed,
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use_param_feature_transform=not args.no_param_feature_transform,
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)
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if __name__ == "__main__":
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main()
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