mirror of
https://github.com/RYDE-WORK/lnp_ml.git
synced 2026-07-22 05:49:55 +08:00
335 lines
8.8 KiB
Markdown
335 lines
8.8 KiB
Markdown
# LNP-ML + MoleculeSTM GNN 快速上手指南
|
||
|
||
适用于：拿到代码包、从未运行过任何代码的新组员。
|
||
|
||
---
|
||
|
||
## 前提条件（开始前确认）
|
||
|
||
- Google Drive 的 `MyDrive/lnp_project_moleculestm/` 下已有以下内容：
|
||
- `lnp_ml/`：项目代码（从 BioT5 版本复制来的，已修改）
|
||
- `moleculestm_weights/`：GNN 预训练权重
|
||
|
||
如果以上文件夹不存在，先看文末的【从零开始】章节。
|
||
|
||
---
|
||
|
||
## 每次重新打开 Colab 都需要运行
|
||
|
||
### Cell 1：挂载 Drive
|
||
|
||
```python
|
||
from google.colab import drive
|
||
drive.mount('/content/drive')
|
||
```
|
||
|
||
> 弹出授权页面时，确认选择有 `lnp_project_moleculestm` 的 Google 账号。
|
||
|
||
---
|
||
|
||
### Cell 2：安装依赖
|
||
|
||
```python
|
||
!pip install rdkit loguru transformers sentencepiece optuna \
|
||
torch_geometric torch_scatter torch_sparse ogb -q
|
||
```
|
||
|
||
---
|
||
|
||
### Cell 3：配置路径
|
||
|
||
```python
|
||
import os, sys
|
||
|
||
LNP_PATH = "/content/drive/MyDrive/lnp_project_moleculestm/lnp_ml"
|
||
MODEL_PATH = "/content/drive/MyDrive/lnp_project_moleculestm/moleculestm_weights/pretrained_MoleculeSTM/SciBERT-Graph-3e-5-1-1e-4-1-InfoNCE-0.1-32-32/molecule_model.pth"
|
||
|
||
sys.path.insert(0, LNP_PATH)
|
||
os.chdir(LNP_PATH)
|
||
print("路径配置完成 ✓")
|
||
```
|
||
|
||
---
|
||
|
||
### Cell 4：确认文件完整
|
||
|
||
```python
|
||
checks = [
|
||
f"{LNP_PATH}/lnp_ml/modeling/models.py",
|
||
f"{LNP_PATH}/lnp_ml/modeling/encoders/moleculestm_encoder.py",
|
||
f"{LNP_PATH}/lnp_ml/modeling/encoders/molecule_gnn_model.py",
|
||
f"{LNP_PATH}/lnp_ml/modeling/encoders/__init__.py",
|
||
f"{MODEL_PATH}",
|
||
f"{LNP_PATH}/data/processed/train.parquet",
|
||
f"{LNP_PATH}/data/processed/val.parquet",
|
||
f"{LNP_PATH}/data/processed/test.parquet",
|
||
f"{LNP_PATH}/data/processed/train_pretrain.parquet",
|
||
f"{LNP_PATH}/data/processed/val_pretrain.parquet",
|
||
]
|
||
for f in checks:
|
||
print(f"{'✓' if os.path.exists(f) else '✗ 缺失'} {f}")
|
||
```
|
||
|
||
**所有文件都显示 ✓ 再继续。**
|
||
|
||
---
|
||
|
||
## 正式流程
|
||
|
||
### Cell 5：验证 MoleculeSTM 模块
|
||
|
||
```python
|
||
!PYTHONPATH={LNP_PATH} \
|
||
python {LNP_PATH}/verify_llm_encoder.py \
|
||
--model_path "{MODEL_PATH}" \
|
||
--lnp_repo_path "{LNP_PATH}"
|
||
```
|
||
|
||
**正常输出：**
|
||
```
|
||
TEST 1 PASSED ✓
|
||
TEST 2 PASSED ✓
|
||
TEST 3 PASSED ✓
|
||
TEST 4 PASSED ✓
|
||
ALL TESTS PASSED ✓
|
||
```
|
||
|
||
---
|
||
|
||
### Cell 6：预训练（约 10 分钟）
|
||
|
||
```python
|
||
!PYTHONPATH={LNP_PATH} \
|
||
python -m lnp_ml.modeling.pretrain main \
|
||
--train-path data/processed/train_pretrain.parquet \
|
||
--val-path data/processed/val_pretrain.parquet \
|
||
--epochs 50 \
|
||
--lr 1e-4 \
|
||
--device cuda
|
||
```
|
||
|
||
完成后确认权重存在：
|
||
```python
|
||
print("✓ 预训练权重存在" if os.path.exists(f"{LNP_PATH}/models/pretrain_delivery.pt")
|
||
else "✗ 预训练权重未生成")
|
||
```
|
||
|
||
参考结果：`Best val_loss: 0.5667`
|
||
|
||
---
|
||
|
||
### Cell 7：正式训练（约 20 分钟）
|
||
|
||
```python
|
||
!PYTHONPATH={LNP_PATH} \
|
||
python lnp_ml/modeling/final_train_optuna_cv.py \
|
||
--init-from-pretrain models/pretrain_delivery.pt \
|
||
--use-llm \
|
||
--llm-model-path "{MODEL_PATH}" \
|
||
--llm-device cuda \
|
||
--n-trials 20 \
|
||
--epochs-per-trial 30 \
|
||
--seed 42 \
|
||
--device cuda \
|
||
--output-dir models/final
|
||
```
|
||
|
||
完成后确认：
|
||
```python
|
||
print("✓ 模型权重存在" if os.path.exists(f"{LNP_PATH}/models/final/model.pt")
|
||
else "✗ 模型未生成")
|
||
```
|
||
|
||
参考结果：`Best val_loss: 2.4433`
|
||
|
||
---
|
||
|
||
### Cell 8：测试评估
|
||
|
||
```python
|
||
!PYTHONPATH={LNP_PATH} \
|
||
python lnp_ml/modeling/predict.py test \
|
||
--test-path data/processed/test.parquet \
|
||
--model-path models/final/model.pt \
|
||
--output-path models/final/test_results.json
|
||
|
||
import json
|
||
with open(f'{LNP_PATH}/models/final/test_results.json') as f:
|
||
results = json.load(f)
|
||
|
||
print("=== 分类任务 ===")
|
||
for task in ['pdi', 'ee', 'toxic']:
|
||
m = results['detailed_metrics'][task]
|
||
print(f" {task}: acc={m['accuracy']:.4f}, f1={m['f1']:.4f}")
|
||
|
||
print("\n=== 回归任务 ===")
|
||
for task in ['size', 'delivery']:
|
||
m = results['detailed_metrics'][task]
|
||
print(f" {task}: R²={m['r2']:.4f}, RMSE={m['rmse']:.4f}")
|
||
|
||
print("\n=== 分布任务 ===")
|
||
m = results['detailed_metrics']['biodist']
|
||
print(f" biodist: KL={m['kl_divergence']:.4f}, JS={m['js_divergence']:.4f}")
|
||
```
|
||
|
||
**参考指标：**
|
||
|
||
| 任务 | 指标 | 参考值 |
|
||
|---|---|---|
|
||
| delivery | R² | ~0.55 |
|
||
| size | R² | ~0.41 |
|
||
| pdi | acc | ~0.71 |
|
||
| ee | acc | ~0.68 |
|
||
| toxic | acc | ~0.95 |
|
||
| biodist | KL | ~0.65 |
|
||
|
||
---
|
||
|
||
### Cell 9：备份模型
|
||
|
||
```python
|
||
!cp -r {LNP_PATH}/models/final \
|
||
{LNP_PATH}/models/final_backup
|
||
print("备份完成 ✓")
|
||
```
|
||
|
||
---
|
||
|
||
## 常见问题
|
||
|
||
| 报错 | 原因 | 解决方法 |
|
||
|---|---|---|
|
||
| `No module named 'rdkit'` | 依赖未安装 | 重新运行 Cell 2 |
|
||
| `No module named 'lnp_ml'` | 路径未配置 | 重新运行 Cell 3 |
|
||
| `Drive not mounted` | Drive 未挂载 | 重新运行 Cell 1 |
|
||
| `Mountpoint must not already contain files` | Drive 已挂载但缓存混乱 | 菜单栏 → 运行时 → 重新启动运行时，再从 Cell 1 开始 |
|
||
| `RuntimeError: size mismatch` | OGB 版本差异 | 已在代码里修复，正常现象 |
|
||
| `KeyError: test_results.json` | 测试未完成 | 确认 Cell 8 的第一条命令输出了 `Saved test results` |
|
||
| 重新连接后变量丢失 | Colab 断开重连 | 从 Cell 1 重跑，Cell 6/7 已有权重可跳过 |
|
||
|
||
---
|
||
|
||
## 如果已有训练好的模型，直接从 Cell 8 开始
|
||
|
||
只需运行 Cell 1 → Cell 2 → Cell 3 → Cell 8，跳过 Cell 4-7。
|
||
|
||
---
|
||
|
||
## 【从零开始】如果 lnp_project_moleculestm 不存在
|
||
|
||
### Step A：从 BioT5 版本复制代码
|
||
|
||
```python
|
||
import os
|
||
os.makedirs("/content/drive/MyDrive/lnp_project_moleculestm", exist_ok=True)
|
||
|
||
!cp -r "/content/drive/MyDrive/lnp_project_biot5-plus/lnp_ml" \
|
||
"/content/drive/MyDrive/lnp_project_moleculestm/lnp_ml"
|
||
print("代码复制完成 ✓")
|
||
```
|
||
|
||
### Step B：下载 MoleculeSTM GNN 权重（约 3GB，需 1-2 分钟）
|
||
|
||
```python
|
||
from huggingface_hub import snapshot_download
|
||
import os
|
||
|
||
os.makedirs("/content/drive/MyDrive/lnp_project_moleculestm/moleculestm_weights", exist_ok=True)
|
||
|
||
snapshot_download(
|
||
repo_id="chao1224/MoleculeSTM",
|
||
repo_type="model",
|
||
local_dir="/content/drive/MyDrive/lnp_project_moleculestm/moleculestm_weights",
|
||
allow_patterns="*Graph*"
|
||
)
|
||
print("权重下载完成 ✓")
|
||
```
|
||
|
||
### Step C：修改代码文件
|
||
|
||
运行以下四个 cell 完成代码适配：
|
||
|
||
```python
|
||
# Step C-1：更新 __init__.py
|
||
path = "/content/drive/MyDrive/lnp_project_moleculestm/lnp_ml/lnp_ml/modeling/encoders/__init__.py"
|
||
new_content = """from lnp_ml.modeling.encoders.rdkit_encoder import CachedRDKitEncoder
|
||
from lnp_ml.modeling.encoders.mpnn_encoder import CachedMPNNEncoder
|
||
from .llm_encoder import LLMEncoder
|
||
from .moleculestm_encoder import MoleculeSTMEncoder
|
||
|
||
__all__ = ["CachedRDKitEncoder", "CachedMPNNEncoder", "LLMEncoder", "MoleculeSTMEncoder"]
|
||
"""
|
||
with open(path, 'w') as f:
|
||
f.write(new_content)
|
||
print("__init__.py ✓")
|
||
```
|
||
|
||
```python
|
||
# Step C-2：更新 models.py
|
||
path = "/content/drive/MyDrive/lnp_project_moleculestm/lnp_ml/lnp_ml/modeling/models.py"
|
||
with open(path, 'r') as f:
|
||
content = f.read()
|
||
content = content.replace(
|
||
"from lnp_ml.modeling.encoders import CachedRDKitEncoder, CachedMPNNEncoder, LLMEncoder",
|
||
"from lnp_ml.modeling.encoders import CachedRDKitEncoder, CachedMPNNEncoder, LLMEncoder, MoleculeSTMEncoder"
|
||
)
|
||
content = content.replace(
|
||
"self.llm_encoder = LLMEncoder(",
|
||
"self.llm_encoder = MoleculeSTMEncoder("
|
||
)
|
||
content = content.replace(
|
||
"model_path=llm_model_path,",
|
||
"checkpoint_path=llm_model_path,"
|
||
)
|
||
content = content.replace(
|
||
"llm_checkpoint_path=llm_model_path,",
|
||
"llm_model_path=llm_model_path,"
|
||
)
|
||
with open(path, 'w') as f:
|
||
f.write(content)
|
||
print("models.py ✓")
|
||
```
|
||
|
||
```python
|
||
# Step C-3：修复 verify 脚本
|
||
path = "/content/drive/MyDrive/lnp_project_moleculestm/lnp_ml/verify_llm_encoder.py"
|
||
with open(path, 'r') as f:
|
||
content = f.read()
|
||
content = content.replace(
|
||
"from lnp_ml.modeling.encoders.llm_encoder import LLMEncoder",
|
||
"from lnp_ml.modeling.encoders.moleculestm_encoder import MoleculeSTMEncoder as LLMEncoder"
|
||
)
|
||
content = content.replace(
|
||
"encoder = LLMEncoder(\n model_path=model_path,",
|
||
"encoder = LLMEncoder(\n checkpoint_path=model_path,"
|
||
)
|
||
content = content.replace(
|
||
"encoder._llm.named_parameters()",
|
||
"encoder._gnn.named_parameters()"
|
||
)
|
||
content = content.replace(
|
||
"model.llm_encoder._llm.parameters()",
|
||
"model.llm_encoder._gnn.parameters()"
|
||
)
|
||
with open(path, 'w') as f:
|
||
f.write(content)
|
||
print("verify_llm_encoder.py ✓")
|
||
```
|
||
|
||
```python
|
||
# Step C-4：修复 pretrain.py
|
||
path = "/content/drive/MyDrive/lnp_project_moleculestm/lnp_ml/lnp_ml/modeling/pretrain.py"
|
||
with open(path, 'r') as f:
|
||
content = f.read()
|
||
content = content.replace(
|
||
"llm_checkpoint_path=llm_model_path,",
|
||
"llm_model_path=llm_model_path,"
|
||
)
|
||
with open(path, 'w') as f:
|
||
f.write(content)
|
||
print("pretrain.py ✓")
|
||
```
|
||
|
||
完成后从 Cell 5 开始正常运行。
|
||
|