mirror of
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210 lines
5.7 KiB
Markdown
210 lines
5.7 KiB
Markdown
# LNP-ML + BioT5-plus 快速上手指南
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适用于：拿到代码包、从未运行过任何代码的新组员。
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---
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## 前提条件（收到代码包前确认）
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- Google Drive 的 `MyDrive/lnp_project/` 下已有以下两个文件夹：
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- `lnp_ml/`：项目代码
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- `biot5-plus-base/`：BioT5-plus 权重（无需重新下载）
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- `lnp_ml/data/processed/` 下已有处理好的数据文件
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---
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## Cell 1：挂载 Drive
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```python
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from google.colab import drive
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drive.mount('/content/drive')
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```
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---
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## Cell 2：安装依赖
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> 每次重新打开 Colab 都需要重跑这一步，依赖不会自动保留。
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```python
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!pip install rdkit loguru transformers sentencepiece optuna -q
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```
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---
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## Cell 3：配置路径 + 确认文件完整
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> 所有 cell 都依赖这里定义的 `LNP_PATH` 和 `MODEL_PATH`，必须先运行。
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```python
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import os, sys
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LNP_PATH = "/content/drive/MyDrive/lnp_project/lnp_ml"
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MODEL_PATH = "/content/drive/MyDrive/lnp_project/biot5-plus-base"
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sys.path.insert(0, LNP_PATH)
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os.chdir(LNP_PATH)
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checks = [
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f"{LNP_PATH}/lnp_ml/modeling/models.py",
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f"{LNP_PATH}/lnp_ml/modeling/encoders/llm_encoder.py",
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f"{LNP_PATH}/lnp_ml/modeling/encoders/__init__.py",
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f"{LNP_PATH}/lnp_ml/featurization/smiles.py",
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f"{MODEL_PATH}/config.json",
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f"{MODEL_PATH}/tokenizer_config.json",
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f"{LNP_PATH}/data/processed/train.parquet",
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f"{LNP_PATH}/data/processed/val.parquet",
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f"{LNP_PATH}/data/processed/test.parquet",
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f"{LNP_PATH}/data/processed/train_pretrain.parquet",
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f"{LNP_PATH}/data/processed/val_pretrain.parquet",
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]
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for f in checks:
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print(f"{'✓' if os.path.exists(f) else '✗ 缺失'} {f}")
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```
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**所有文件都显示 ✓ 再继续。如果有 ✗，先补齐对应文件。**
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---
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## Cell 4：验证 LLM 模块（正式训练前必做）
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> 验证数据流、维度对齐、梯度链路、可复现性，四项全部通过再训练。
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```python
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!PYTHONPATH={LNP_PATH} \
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python {LNP_PATH}/verify_llm_encoder.py \
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--model_path "{MODEL_PATH}" \
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--lnp_repo_path "{LNP_PATH}"
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```
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**正常输出：**
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```
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TEST 1 PASSED ✓
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TEST 2 PASSED ✓
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TEST 3 PASSED ✓
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TEST 4 PASSED ✓
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ALL TESTS PASSED ✓
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```
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---
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## Cell 5：预训练（用外部 LiON 数据）
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> 在约 9000 条外部数据上预训练 delivery 任务，产出 `models/pretrain_delivery.pt`。
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> 约需 10-15 分钟。
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```python
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!PYTHONPATH={LNP_PATH} \
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python -m lnp_ml.modeling.pretrain main \
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--train-path data/processed/train_pretrain.parquet \
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--val-path data/processed/val_pretrain.parquet \
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--epochs 50 \
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--lr 1e-4 \
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--device cuda
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```
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完成后确认：
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```python
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import os
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print("✓ 预训练权重存在" if os.path.exists(f"{LNP_PATH}/models/pretrain_delivery.pt")
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else "✗ 预训练权重未生成，检查上面的输出")
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```
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---
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## Cell 6：正式训练（含 LLM，约 20-25 分钟）
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> 使用 Optuna 做 3-fold 超参搜索（20 trials），然后全量数据训练。
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> 产出 `models/final/model.pt`。
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```python
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!PYTHONPATH={LNP_PATH} \
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python lnp_ml/modeling/final_train_optuna_cv.py \
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--init-from-pretrain models/pretrain_delivery.pt \
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--use-llm \
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--llm-model-path "{MODEL_PATH}" \
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--llm-device cuda \
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--n-trials 20 \
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--epochs-per-trial 30 \
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--seed 42 \
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--device cuda \
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--output-dir models/final
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```
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完成后确认：
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```python
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print("✓ 模型权重存在" if os.path.exists(f"{LNP_PATH}/models/final/model.pt")
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else "✗ 模型未生成，检查上面的输出")
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```
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---
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## Cell 7：测试评估
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```python
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!PYTHONPATH={LNP_PATH} \
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python lnp_ml/modeling/predict.py test \
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--test-path data/processed/test.parquet \
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--model-path models/final/model.pt \
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--output-path models/final/test_results.json
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import json
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with open(f'{LNP_PATH}/models/final/test_results.json') as f:
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results = json.load(f)
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print("=== 分类任务 ===")
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for task in ['pdi', 'ee', 'toxic']:
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m = results['detailed_metrics'][task]
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print(f" {task}: acc={m['accuracy']:.4f}, f1={m['f1']:.4f}")
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print("\n=== 回归任务 ===")
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for task in ['size', 'delivery']:
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m = results['detailed_metrics'][task]
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print(f" {task}: R²={m['r2']:.4f}, RMSE={m['rmse']:.4f}")
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print("\n=== 分布任务 ===")
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m = results['detailed_metrics']['biodist']
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print(f" biodist: KL={m['kl_divergence']:.4f}, JS={m['js_divergence']:.4f}")
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```
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**参考指标（基于已有实验结果）：**
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| 任务 | 指标 | 参考值 |
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| delivery | R² | ~0.63 |
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| size | R² | ~0.42 |
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| pdi | acc | ~0.69 |
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| ee | acc | ~0.68 |
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| toxic | acc | ~0.96 |
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| biodist | KL | ~0.70 |
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---
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## Cell 8：备份模型到 Drive
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```python
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!cp -r {LNP_PATH}/models/final \
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{LNP_PATH}/models/final_backup
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print("备份完成 ✓")
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```
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---
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## 常见问题
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| 报错 | 原因 | 解决方法 |
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| `No module named 'rdkit'` | 依赖未安装 | 重新运行 Cell 2 |
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| `No module named 'lnp_ml'` | 路径未配置 | 重新运行 Cell 3 |
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| `No such option '--use-llm'` | Drive 里的 `final_train_optuna_cv.py` 是旧版本 | 确认代码包里的文件是最新版本 |
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| `running_mean should contain 217 elements not 210` | `models.py` 里 `desc` 维度是旧值 210 | 把 `DEFAULT_INPUT_DIMS` 里的 `"desc": 210` 改为 `"desc": 217` |
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| `KeyError: attribute 'projection' already exists` | `llm_encoder.py` 是旧版本 | 确认代码包里的文件是最新版本 |
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| `RuntimeError: Unexpected key(s) in state_dict` | 加载模型时 strict=True | 确认 `predict.py` 里用的是 `strict=False` |
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| Colab 断开后重连 | 环境变量丢失 | 从 Cell 1 重跑，Cell 5/6 已有权重可跳过 |
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---
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## 如果已有训练好的模型，直接从 Cell 7 开始
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只需运行 Cell 1 → Cell 2 → Cell 3 → Cell 7，跳过 Cell 4、5、6。
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