mirror of
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161 lines
4.5 KiB
Markdown
161 lines
4.5 KiB
Markdown
# lnp-ml
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<a target="_blank" href="https://cookiecutter-data-science.drivendata.org/">
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<img src="https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter" />
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</a>
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LNP（脂质纳米颗粒）药物递送性能预测模型。
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## 快速开始
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### 1. 安装环境
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```bash
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pixi install
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pixi shell
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```
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### 2. 数据处理
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```bash
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# 清洗原始数据 (raw -> interim)
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make clean_data
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# 处理内部数据集 (interim -> processed)
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make data
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# 处理外部预训练数据 (external -> processed)
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make data_pretrain
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```
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### 3. 训练模型
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**方式 A：直接训练（从零开始）**
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```bash
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make train
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```
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**方式 B：预训练 + 微调（推荐）**
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利用外部 LiON 数据集（约 9000 条）进行预训练，再在内部数据上微调：
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```bash
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# Step 1: 处理外部数据
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make data_pretrain
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# Step 2: 在外部数据上预训练 backbone + delivery head
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make pretrain
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# Step 3: 加载预训练权重，在内部数据上多任务微调
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make finetune
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```
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**方式 C：超参数调优**
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```bash
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make tune
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```
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### 4. 测试与预测
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```bash
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# 在测试集上评估
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make test
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# 生成预测结果
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make predict
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```
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## 训练流程详解
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### 预训练 (Pretrain)
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在外部 LiON 数据上，仅训练 `quantified_delivery` 任务：
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```bash
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# 1. 先处理外部数据
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python scripts/process_external.py
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# 2. 预训练
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python -m lnp_ml.modeling.pretrain \
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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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```
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产出:
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- `data/processed/train_pretrain.parquet`: 处理后的训练数据
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- `data/processed/val_pretrain.parquet`: 处理后的验证数据
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- `models/pretrain_delivery.pt`: backbone + delivery head 权重
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- `models/pretrain_history.json`: 训练历史
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### 微调 (Finetune)
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加载预训练权重，在内部多任务数据上训练：
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```bash
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python -m lnp_ml.modeling.train \
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--init-from-pretrain models/pretrain_delivery.pt \
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--load-delivery-head # 可选：是否加载 delivery head 权重
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```
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产出:
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- `models/model.pt`: 完整模型权重
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- `models/history.json`: 训练历史
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## Project Organization
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```
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├── LICENSE <- Open-source license if one is chosen
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├── Makefile <- Makefile with convenience commands like `make data` or `make train`
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├── README.md <- The top-level README for developers using this project.
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├── data
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│ ├── external <- Data from third party sources.
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│ ├── interim <- Intermediate data that has been transformed.
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│ ├── processed <- The final, canonical data sets for modeling.
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│ └── raw <- The original, immutable data dump.
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│
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├── docs <- A default mkdocs project; see www.mkdocs.org for details
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│
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├── models <- Trained and serialized models, model predictions, or model summaries
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│
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
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│ the creator's initials, and a short `-` delimited description, e.g.
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│ `1.0-jqp-initial-data-exploration`.
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│
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├── pyproject.toml <- Project configuration file with package metadata for
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│ lnp_ml and configuration for tools like black
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│
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├── references <- Data dictionaries, manuals, and all other explanatory materials.
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│
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
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│ └── figures <- Generated graphics and figures to be used in reporting
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│
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
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│ generated with `pip freeze > requirements.txt`
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│
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├── setup.cfg <- Configuration file for flake8
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│
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└── lnp_ml <- Source code for use in this project.
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│
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├── __init__.py <- Makes lnp_ml a Python module
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│
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├── config.py <- Store useful variables and configuration
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│
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├── dataset.py <- Scripts to download or generate data
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│
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├── features.py <- Code to create features for modeling
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│
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├── modeling
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│ ├── __init__.py
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│ ├── predict.py <- Code to run model inference with trained models
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│ └── train.py <- Code to train models
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│
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└── plots.py <- Code to create visualizations
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```
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--------
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