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103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
"""在 unimol 环境（pip install unimol-tools）中运行：把数据集所有唯一 SMILES 编码成 UniMol 表征缓存。
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用法（工作目录 = lnp_ml 根）:
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conda activate unimol
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python scripts/precompute_unimol.py --out data/processed/unimol_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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首次运行会自动下载 UniMol 预训练权重（HF 不通时先 export HF_ENDPOINT=https://hf-mirror.com）。
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"""
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import argparse
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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/unimol_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("--model-name", default="unimolv1", help="unimolv1 / unimolv2")
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ap.add_argument("--model-size", default="84m", help="仅 unimolv2 生效")
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ap.add_argument("--batch-size", type=int, default=64)
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args = ap.parse_args()
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from unimol_tools import UniMolRepr
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clf = UniMolRepr(
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data_type="molecule", remove_hs=False,
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model_name=args.model_name, model_size=args.model_size,
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)
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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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def encode_one(s):
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try:
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rep = clf.get_repr([s], return_atomic_reprs=True)
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return np.asarray(rep["cls_repr"], dtype=np.float32).reshape(-1)
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except Exception as e:
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print(f" 跳过（编码失败）: {s[:40]}... ({e})")
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return None
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vecs: dict = {}
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dim = None
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bs = args.batch_size
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for i in range(0, len(smiles), bs):
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batch = smiles[i:i + bs]
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try:
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rep = clf.get_repr(batch, return_atomic_reprs=True)
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cls = np.asarray(rep["cls_repr"], dtype=np.float32)
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if cls.ndim != 2 or cls.shape[0] != len(batch):
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raise ValueError(f"返回形状 {cls.shape} 与输入 {len(batch)} 不对齐")
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for s, v in zip(batch, cls):
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vecs[s] = v
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dim = v.shape[0]
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except Exception as e:
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print(f" 批量失败，改逐条: {e}")
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for s in batch:
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v = encode_one(s)
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vecs[s] = v
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if v is not None:
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dim = v.shape[0]
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print(f" {min(i + bs, len(smiles))}/{len(smiles)}")
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if dim is None:
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raise RuntimeError("没有任何 SMILES 编码成功，请检查 unimol-tools 安装与权重。")
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n_fail = sum(1 for s in smiles if vecs.get(s) is None)
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embeddings = np.vstack([
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vecs[s] if vecs.get(s) is not None else np.zeros(dim, np.float32) for s in smiles
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])
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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(out, smiles=np.array(smiles), embeddings=embeddings)
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print(f"已保存 {embeddings.shape} 到 {out}（失败 {n_fail} 个已置零）")
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if __name__ == "__main__":
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main() |