231 lines
5.8 KiB
Markdown
231 lines
5.8 KiB
Markdown
# 管线执行计划
|
||
|
||
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
|
||
|
||
**Goal:** 从 ERA5 NetCDF 原始数据运行完整管线到 LaTeX 论文
|
||
|
||
**Architecture:** 5 阶段流水线 — 预处理(NPZ) → 训练(模型) → 评估(图表) → Web(验证) → 论文(LaTeX)
|
||
|
||
**Tech Stack:** PyTorch 2.12+cu126, xarray+h5netcdf, XGBoost, Flask+ECharts, XeLaTeX+ctexbook
|
||
|
||
**前置项已就绪:**
|
||
- ERA5 数据: 焦作 180 + 郑州 180 (NetCDF4, 已解压)
|
||
- GPU: RTX 4060 Laptop (8GB), CUDA 12.6
|
||
- h5netcdf/h5py: 已安装
|
||
- 外部数据: mortality_population.csv, exposure_response.csv
|
||
|
||
---
|
||
|
||
### Task 1: 修复文件命名一致性
|
||
|
||
**Files:**
|
||
- Modify: `src/data/preprocess.py:537`
|
||
|
||
preprocess 保存 `sequences_{city}.npz`，train 加载 `{city}_sequences.npz`，统一为 `{city}_sequences.npz`。
|
||
|
||
- [ ] **Step 1: 修改 preprocess 的命名**
|
||
|
||
```python
|
||
# 第537行: sequences_{city_key}.npz → {city_key}_sequences.npz
|
||
npz_path = DATA_PROCESSED / f"{city_key}_sequences.npz"
|
||
```
|
||
|
||
所有 `sequences_` 开头的引用都要改（第537、564、573行）:
|
||
|
||
```python
|
||
# 第564行
|
||
npz_path = DATA_PROCESSED / f"{city_key}_sequences.npz"
|
||
# 第573行
|
||
combined_npz = DATA_PROCESSED / "sequences_combined.npz" # 合并文件保持原名
|
||
```
|
||
|
||
- [ ] **Step 2: 提交**
|
||
|
||
```bash
|
||
git add src/data/preprocess.py
|
||
git commit -m "fix: 统一 NPZ 命名格式为 {city}_sequences.npz"
|
||
```
|
||
|
||
---
|
||
|
||
### Task 2: 运行预处理管线
|
||
|
||
**Files:** `src/data/preprocess.py` (无需修改，已改命名)
|
||
|
||
- [ ] **Step 1: 清理旧数据并运行预处理**
|
||
|
||
```bash
|
||
cd D:/Code/doing_exercises/programs/银发群体高温多时间尺度预警和服务优化可视化研究
|
||
rm -f data/processed/*.npz data/processed/*.csv
|
||
uv run python -m src.data.preprocess
|
||
```
|
||
|
||
**预期输出:**
|
||
- 加载焦作 180 NC → 日聚合 → 特征工程 → 序列 14×N_feat
|
||
- 加载郑州 180 NC → 同上
|
||
- 保存: `jiaozuo_sequences.npz`, `zhengzhou_sequences.npz`, `sequences_combined.npz`, `features_combined.csv`
|
||
- 日志显示每个城市的 X/y shape 和标签分布
|
||
|
||
- [ ] **Step 2: 验证产出**
|
||
|
||
```bash
|
||
uv run python -c "
|
||
import numpy as np
|
||
for f in ['jiaozuo_sequences.npz', 'zhengzhou_sequences.npz', 'sequences_combined.npz']:
|
||
d = np.load(f'data/processed/{f}')
|
||
print(f'{f}: X{d[\"X\"].shape} y{d[\"y\"].shape}')
|
||
print(f' y unique counts: {[len(set(d[\"y\"][:,i])) for i in range(3)]}')
|
||
"
|
||
```
|
||
|
||
**预期:** 两个城市共约 10000+ 样本，y 三列各有 4 类
|
||
|
||
- [ ] **Step 3: 提交**
|
||
|
||
```bash
|
||
git add data/processed/
|
||
git commit -m "feat: ERA5 预处理完成，生成序列 NPZ 和特征 CSV"
|
||
```
|
||
|
||
---
|
||
|
||
### Task 3: 训练 LSTM-Attention 模型
|
||
|
||
**Files:** `src/models/train.py` (无需修改)
|
||
|
||
- [ ] **Step 1: 运行训练**
|
||
|
||
```bash
|
||
cd D:/Code/doing_exercises/programs/银发群体高温多时间尺度预警和服务优化可视化研究
|
||
uv run python -m src.models.train
|
||
```
|
||
|
||
**预期输出:**
|
||
- "使用设备: cuda"
|
||
- 数据加载: X (N, 14, F), y (N, 3)
|
||
- 划分: 训练 ~70%, 验证 ~15%, 测试 ~15%
|
||
- 每 epoch 打印 loss/acc/f1
|
||
- 早停后保存 `outputs/models/best_model.pt`
|
||
|
||
- [ ] **Step 2: 验证产出**
|
||
|
||
```bash
|
||
ls -lh outputs/models/best_model.pt
|
||
ls -lh outputs/logs/training_history.json
|
||
```
|
||
|
||
- [ ] **Step 3: 提交**
|
||
|
||
```bash
|
||
git add outputs/models/best_model.pt outputs/logs/training_history.json
|
||
git commit -m "feat: LSTM-Attention 模型训练完成"
|
||
```
|
||
|
||
---
|
||
|
||
### Task 4: 训练 XGBoost 基线并评估
|
||
|
||
**Files:** `src/models/evaluate.py` (无需修改)
|
||
|
||
- [ ] **Step 1: 运行评估**
|
||
|
||
```bash
|
||
cd D:/Code/doing_exercises/programs/银发群体高温多时间尺度预警和服务优化可视化研究
|
||
uv run python -m src.models.evaluate
|
||
```
|
||
|
||
**预期输出:**
|
||
- 混淆矩阵 × 3 时间尺度 (LSTM + XGBoost 对比)
|
||
- F1/Accuracy 对比柱状图
|
||
- 保存至 `outputs/figures/`
|
||
|
||
- [ ] **Step 2: 验证产出**
|
||
|
||
```bash
|
||
ls -lh outputs/figures/confusion_matrix.png outputs/figures/model_comparison.png
|
||
```
|
||
|
||
- [ ] **Step 3: 提交**
|
||
|
||
```bash
|
||
git add outputs/figures/
|
||
git commit -m "feat: 模型评估完成 — LSTM vs XGBoost 对比图表"
|
||
```
|
||
|
||
---
|
||
|
||
### Task 5: 启动 Web 大屏并验证
|
||
|
||
**Files:** `src/web/app.py`, `src/web/static/index.html` (无需修改)
|
||
|
||
- [ ] **Step 1: 启动 Flask**
|
||
|
||
```bash
|
||
cd D:/Code/doing_exercises/programs/银发群体高温多时间尺度预警和服务优化可视化研究
|
||
uv run python -m src.web.app
|
||
```
|
||
|
||
- [ ] **Step 2: 浏览器验证**
|
||
|
||
打开 http://localhost:5000，检查:
|
||
- [ ] 6 面板均渲染（温度趋势/风险展示/人口饼图/时间柱状/暴露反应/历史回顾）
|
||
- [ ] API `/api/predict` 返回正确 JSON
|
||
- [ ] API `/api/history` 返回 90 天数据
|
||
- [ ] API `/api/stats` 返回统计摘要
|
||
|
||
- [ ] **Step 3: 截图保存**
|
||
|
||
```bash
|
||
# 用 Playwright 截取大屏截图
|
||
```
|
||
|
||
---
|
||
|
||
### Task 6: 编译 LaTeX 论文
|
||
|
||
**Files:** `thesis/main.tex`, `thesis/chapters/*.tex`
|
||
|
||
- [ ] **Step 1: 填充论文内容**
|
||
|
||
更新以下章节:
|
||
- `ch2-data-methods.tex`: 填入 ERA5 变量表、NOAA 体感温度公式、模型架构描述
|
||
- `ch3-model-design.tex`: LSTM-Attention 架构详述 (983K 参数)
|
||
- `ch4-experiments.tex`: 插入 `outputs/figures/` 中的评估图表
|
||
- `ch5-visualization.tex`: Web 大屏 6 面板截图与架构说明
|
||
|
||
- [ ] **Step 2: 编译论文**
|
||
|
||
```bash
|
||
cd thesis
|
||
make # xelatex + biber + xelatex + xelatex
|
||
```
|
||
|
||
- [ ] **Step 3: 验证 PDF**
|
||
|
||
```bash
|
||
ls -lh thesis/main.pdf
|
||
```
|
||
|
||
用 PDF 阅读器打开，检查: 中文渲染、图表清晰度、引用编号、页眉页脚
|
||
|
||
- [ ] **Step 4: 提交**
|
||
|
||
```bash
|
||
git add thesis/ thesis/main.pdf
|
||
git commit -m "feat: LaTeX 论文编译完成"
|
||
```
|
||
|
||
---
|
||
|
||
### Task 7: 最终推送
|
||
|
||
- [ ] **Step 1: 推送代码**
|
||
|
||
```bash
|
||
git push origin main
|
||
```
|
||
|
||
- [ ] **Step 2: 推送模型和图表 (如需要)**
|
||
|
||
较大文件可考虑 git-lfs 或单独存放
|