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74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
"""在 chemeleon 环境（chemprop>=2.2）中运行：把数据集里所有唯一 SMILES 编码成 CheMeleon 指纹缓存。
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用法（工作目录 = lnp_ml 根，且同目录有 chemeleon_fingerprint.py）:
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conda activate chemeleon
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python scripts/precompute_chemeleon.py --out data/processed/chemeleon_embeddings.npz
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产物 .npz 含 "smiles" (N,) 与 "embeddings" (N, D)，供主项目查表使用。
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首次运行会自动把权重下载到 ~/.chemprop/chemeleon_mp.pt。
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"""
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import argparse
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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DATA_FILES = [
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"data/interim/internal.csv",
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"data/external/all_data_LiON.csv",
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]
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GLOB_FILES = [
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"data/external/all_amine_split_for_LiON/cv_*/train.csv",
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"data/external/all_amine_split_for_LiON/cv_*/test.csv",
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]
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def collect_smiles(root: Path) -> list:
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seen: set = set()
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files = [root / f for f in DATA_FILES]
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for pattern in GLOB_FILES:
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files += sorted(root.glob(pattern))
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for f in files:
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if not f.exists():
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print(f"跳过不存在的文件: {f}")
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continue
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df = pd.read_csv(f, low_memory=False)
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if "smiles" in df.columns:
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seen.update(df["smiles"].dropna().astype(str).tolist())
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return sorted(seen)
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--out", default="data/processed/chemeleon_embeddings.npz")
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ap.add_argument("--root", default=".", help="lnp_ml 仓库根目录")
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ap.add_argument("--batch-size", type=int, default=1024)
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ap.add_argument("--device", default=None, help="cpu / cuda，默认自动")
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args = ap.parse_args()
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# chemeleon_fingerprint.py 在仓库根，确保可 import
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sys.path.insert(0, str(Path(args.root).resolve()))
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from chemeleon_fingerprint import CheMeleonFingerprint
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fp = CheMeleonFingerprint(device=args.device)
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smiles = collect_smiles(Path(args.root))
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print(f"收集到 {len(smiles)} 个唯一 SMILES，开始编码...")
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chunks = []
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for i in range(0, len(smiles), args.batch_size):
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batch = smiles[i : i + args.batch_size]
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chunks.append(np.asarray(fp(batch), dtype=np.float32))
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print(f" {min(i + args.batch_size, len(smiles))}/{len(smiles)}")
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embeddings = np.vstack(chunks)
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out = Path(args.out)
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out.parent.mkdir(parents=True, exist_ok=True)
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np.savez_compressed(
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out, smiles=np.array(smiles), embeddings=embeddings
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)
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print(f"已保存 {embeddings.shape} 到 {out}")
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if __name__ == "__main__":
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main() |