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Add pretrained encoder baseline scripts and results

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Dicong Li 2026-08-14 17:37:31 +00:00
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# 离线预训练分子 embedding
三个预训练编码器作为额外的 chemical token 接入模型。embedding 离线生成、
存为 `.npy`（按 csv 行顺序对齐），训练时通过 `{smiles: vector}` 查表注入。
npy 文件与预训练权重不入库，运行下列脚本可复现。
## MolFormer (768d)
`ibm/MoLFormer-XL-both-10pct`，HuggingFace 直接加载。
注意：需 transformers>=4.57（依赖 `transformers.masking_utils`），
与项目 pin 的 4.45 冲突，建议装到独立目录后用 sys.path 隔离。
见 `encode_molformer.py`。
## GraphMVP (300d)
ICLR'22，5 层 GIN + OGB AtomEncoder/BondEncoder。
权重：https://github.com/chao1224/GraphMVP → Drive 文件夹
`GraphMVP_complate_features_for_regression/GraphMVP/pretraining_model.pth`
注意：新版 OGB 的 chirality 类别数为 5（checkpoint 为 4），加载时按形状逐层对齐。
见 `encode_graphmvp.py`。
## GROVER (3200d = atom 1600 + bond 1600)
NeurIPS'20，dualtrans (hidden 800, depth 6)。
代码：https://github.com/tencent-ailab/grover，权重 grover_base。
原项目基于 Python 3.6.8 / PyTorch 1.1，在现代环境需两处补丁：
1. `grover/util/utils.py` 的 `torch.load` 加 `weights_only=False`
2. `build_model` 前用 checkpoint 自带 args 补齐 current_args 缺失字段，
并为 dropout 等推理期字段提供默认值
见 `encode_grover.py`。
## 使用
```bash
python -m lnp_ml.modeling.pretrain main --device cuda \
--output-dir models/pretrain/<name> \
--<name>-emb data/interim/<name>_emb_external.npy \
--<name>-csv data/external/all_data_LiON.csv
python -m lnp_ml.modeling.nested_cv_optuna \
--device cuda --n-trials 15 --epochs-per-trial 30 --n-repeats 3 --seed 42 \
--<name>-emb data/interim/<name>_emb.npy \
--<name>-csv data/interim/internal.csv \
--init-from-pretrain models/pretrain/<name>/pretrain_delivery.pt \
--output-dir models/full/<name>_s42
```

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"""GraphMVP 离线 embedding (300d)
依赖：torch-geometric, ogb
权重：https://github.com/chao1224/GraphMVP → Drive 文件夹内
GraphMVP_complate_features_for_regression/GraphMVP/pretraining_model.pth
注意：新版 OGB 的 chirality 类别数为 5，而 checkpoint 为 4，
加载时按形状逐层对齐（前 4 行用预训练，新增类别保持随机初始化）。
用法：
python encode_graphmvp.py --ckpt pretraining_model.pth \
--csv data/interim/internal.csv --out data/interim/graphmvp_emb.npy
"""
import argparse
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
from ogb.utils.mol import smiles2graph
from torch_geometric.data import Batch, Data
from torch_geometric.nn import MessagePassing, global_mean_pool
class GINConv(MessagePassing):
def __init__(self, emb_dim: int) -> None:
super().__init__(aggr="add")
self.mlp = nn.Sequential(
nn.Linear(emb_dim, 2 * emb_dim), nn.BatchNorm1d(2 * emb_dim),
nn.ReLU(), nn.Linear(2 * emb_dim, emb_dim),
)
self.eps = nn.Parameter(torch.Tensor([0]))
self.bond_encoder = BondEncoder(emb_dim=emb_dim)
def forward(self, x, edge_index, edge_attr):
e = self.bond_encoder(edge_attr)
return self.mlp((1 + self.eps) * x + self.propagate(edge_index, x=x, edge_attr=e))
def message(self, x_j, edge_attr):
return F.relu(x_j + edge_attr)
def update(self, aggr_out):
return aggr_out
class GNNComplete(nn.Module):
def __init__(self, num_layer: int = 5, emb_dim: int = 300, drop_ratio: float = 0.0) -> None:
super().__init__()
self.num_layer, self.drop_ratio = num_layer, drop_ratio
self.atom_encoder = AtomEncoder(emb_dim)
self.gnns = nn.ModuleList([GINConv(emb_dim) for _ in range(num_layer)])
self.batch_norms = nn.ModuleList([nn.BatchNorm1d(emb_dim) for _ in range(num_layer)])
def forward(self, x, edge_index, edge_attr):
h = self.atom_encoder(x)
for layer in range(self.num_layer):
h = self.gnns[layer](h, edge_index, edge_attr)
h = self.batch_norms[layer](h)
h = F.dropout(h if layer == self.num_layer - 1 else F.relu(h),
self.drop_ratio, training=self.training)
return h
def load_weights(model: nn.Module, ckpt_path: str) -> None:
sd = torch.load(ckpt_path, map_location="cpu")
msd = model.state_dict()
loaded, skipped, partial = 0, [], []
for k, v in sd.items():
if k not in msd:
continue
if msd[k].shape == v.shape:
msd[k] = v
loaded += 1
elif v.dim() == 2 and msd[k].shape[1] == v.shape[1] and msd[k].shape[0] > v.shape[0]:
msd[k][: v.shape[0]] = v # OGB 类别数变化，前 N 行用预训练
partial.append(k)
loaded += 1
else:
skipped.append((k, tuple(v.shape), tuple(msd[k].shape)))
model.load_state_dict(msd)
print(f"已加载 {loaded}/{len(sd)} 个张量 | 部分加载: {partial} | 跳过: {skipped}")
assert not skipped, "有张量形状不兼容"
def main(ckpt: str, csv_path: str, out_npy: str, col: str = "smiles", batch: int = 64) -> None:
model = GNNComplete().cuda().eval()
load_weights(model, ckpt)
smi = pd.read_csv(csv_path, low_memory=False)[col].astype(str).tolist()
uniq = list(dict.fromkeys(smi))
print(f"{len(smi)} 行, {len(uniq)} 唯一分子")
lut = {}
with torch.no_grad():
for i in range(0, len(uniq), batch):
datas = []
for s in uniq[i:i + batch]:
g = smiles2graph(s)
datas.append(Data(
x=torch.tensor(g["node_feat"], dtype=torch.long),
edge_index=torch.tensor(g["edge_index"], dtype=torch.long),
edge_attr=torch.tensor(g["edge_feat"], dtype=torch.long),
))
b = Batch.from_data_list(datas).cuda()
hg = global_mean_pool(model(b.x, b.edge_index, b.edge_attr), b.batch).cpu().numpy()
for j, s in enumerate(uniq[i:i + batch]):
lut[s] = hg[j]
E = np.stack([lut[s] for s in smi])
np.save(out_npy, E)
print(f"{out_npy} {E.shape}")
if __name__ == "__main__":
p = argparse.ArgumentParser()
p.add_argument("--ckpt", required=True)
p.add_argument("--csv", required=True)
p.add_argument("--out", required=True)
p.add_argument("--col", default="smiles")
a = p.parse_args()
main(a.ckpt, a.csv, a.out, a.col)

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"""GROVER fingerprint 离线编码 (3200d = atom 1600 + bond 1600)
代码：https://github.com/tencent-ailab/grover ，权重 grover_base
原项目基于 Python 3.6.8 / PyTorch 1.1，在现代环境需对 grover 源码打两处补丁：
1. grover/util/utils.py 的 torch.load 加 weights_only=False
2. build_model 前用 checkpoint 自带 args 补齐 current_args 缺失字段，
并为 dropout / features_only / ffn_num_layers 等推理期字段提供默认值
三步流程：
# 1) 生成唯一 SMILES 输入
python encode_grover.py prepare --csv data/interim/internal.csv --out /tmp/grover_in.csv
# 2) 跑 GROVER fingerprint（在 grover 仓库目录下）
python main.py fingerprint --data_path /tmp/grover_in.csv \
--checkpoint_path grover_base.pt --fingerprint_source both --output /tmp/grover_fp.npz
# 3) 按 csv 行顺序对齐存 npy
python encode_grover.py align --fp /tmp/grover_fp.npz --input /tmp/grover_in.csv \
--target data/interim/internal.csv --out data/interim/grover_emb.npy
"""
import argparse
import numpy as np
import pandas as pd
def prepare(csv_path: str, out_path: str, col: str = "smiles") -> None:
smi = pd.read_csv(csv_path, low_memory=False)[col].astype(str).tolist()
uniq = list(dict.fromkeys(smi))
pd.DataFrame({"smiles": uniq, "label": [0] * len(uniq)}).to_csv(out_path, index=False)
print(f"{len(smi)} 行 -> {len(uniq)} 唯一分子 -> {out_path}")
def align(fp_npz: str, input_csv: str, target_csv: str, out_npy: str, col: str = "smiles") -> None:
fps = np.load(fp_npz)["fps"]
uniq = pd.read_csv(input_csv)["smiles"].astype(str).tolist()
assert len(uniq) == fps.shape[0], f"{len(uniq)} vs {fps.shape[0]}"
lut = {s: fps[i] for i, s in enumerate(uniq)}
rows = pd.read_csv(target_csv, low_memory=False)[col].astype(str).tolist()
E = np.stack([lut[s] for s in rows])
np.save(out_npy, E)
print(f"{out_npy} {E.shape}")
if __name__ == "__main__":
p = argparse.ArgumentParser()
sub = p.add_subparsers(dest="cmd", required=True)
a = sub.add_parser("prepare")
a.add_argument("--csv", required=True)
a.add_argument("--out", required=True)
b = sub.add_parser("align")
b.add_argument("--fp", required=True)
b.add_argument("--input", required=True)
b.add_argument("--target", required=True)
b.add_argument("--out", required=True)
args = p.parse_args()
if args.cmd == "prepare":
prepare(args.csv, args.out)
else:
align(args.fp, args.input, args.target, args.out)

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scripts_run/encode_embeddings/encode_molformer.py Normal file
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"""MolFormer-XL 离线 embedding (768d)
依赖：transformers>=4.57（需 transformers.masking_utils）。若与项目 pin 的
4.45 冲突，装到独立目录后用 sys.path 隔离：
pip install --target=/tmp/mf_libs "transformers>=4.57" --no-deps
pip install --target=/tmp/mf_libs tokenizers>=0.22,<=0.23 --no-deps
# 本脚本开头 sys.path.insert(0, '/tmp/mf_libs')
用法：
python encode_molformer.py --csv data/interim/internal.csv \
--out data/interim/molformer_emb.npy
"""
import argparse
import numpy as np
import pandas as pd
import torch
MODEL = "ibm/MoLFormer-XL-both-10pct"
def main(csv_path: str, out_npy: str, col: str = "smiles", batch: int = 32) -> None:
from transformers import AutoModel, AutoTokenizer
smiles = pd.read_csv(csv_path, low_memory=False)[col].astype(str).tolist()
print(f"{len(smiles)} 行, {len(set(smiles))} 唯一分子")
tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModel.from_pretrained(
MODEL, trust_remote_code=True, deterministic_eval=True
).cuda().eval()
embs = []
with torch.no_grad():
for i in range(0, len(smiles), batch):
b = tok(smiles[i:i + batch], padding=True, truncation=True,
max_length=512, return_tensors="pt")
b = {k: v.cuda() for k, v in b.items()}
embs.append(model(**b).pooler_output.cpu().numpy())
E = np.vstack(embs)
np.save(out_npy, E)
print(f"{out_npy} {E.shape}")
if __name__ == "__main__":
p = argparse.ArgumentParser()
p.add_argument("--csv", required=True)
p.add_argument("--out", required=True)
p.add_argument("--col", default="smiles")
a = p.parse_args()
main(a.csv, a.out, a.col)