HaHafeng
74cf346453
Major features: 1. Missing value imputation (6 simple methods + MICE): - Mean/Median/Mode/Constant imputation - Forward fill (ffill) and Backward fill (bfill) for time series - MICE multivariate imputation (in progress, shape issue to fix) 2. Auto precision detection: - Automatically match decimal places of original data - Prevent false precision (e.g. 13.57 instead of 13.566716417910449) 3. Categorical variable detection: - Auto-detect and skip categorical columns in MICE - Show warnings for unsuitable columns - Suggest mode imputation for categorical data 4. UI improvements: - Rename button: "Delete Missing" to "Missing Value Handling" - Remove standalone "Dedup" and "MICE" buttons - 3-tab dialog: Delete / Fill / Advanced Fill - Display column statistics and recommended methods - Extended warning messages (8 seconds for skipped columns) 5. Bug fixes: - Fix sessionService.updateSessionData -> saveProcessedData - Fix OperationResult interface (add message and stats) - Fix Toolbar button labels and removal Modified files: Python: operations/fillna.py (new, 556 lines), main.py (3 new endpoints) Backend: QuickActionService.ts, QuickActionController.ts, routes/index.ts Frontend: MissingValueDialog.tsx (new, 437 lines), Toolbar.tsx, index.tsx Tests: test_fillna_operations.py (774 lines), test scripts and docs Docs: 5 documentation files updated Known issues: - MICE imputation has DataFrame shape mismatch issue (under debugging) - Workaround: Use 6 simple imputation methods first Status: Development complete, MICE debugging in progress Lines added: ~2000 lines across 3 tiers
99 lines
1.8 KiB
Markdown
99 lines
1.8 KiB
Markdown
# 🚀 快速开始 - 1分钟运行测试
|
||
|
||
## Windows用户
|
||
|
||
### 方法1：双击运行（最简单）
|
||
1. 双击 `run_tests.bat`
|
||
2. 等待测试完成
|
||
|
||
### 方法2：命令行
|
||
```cmd
|
||
cd AIclinicalresearch\tests
|
||
run_tests.bat
|
||
```
|
||
|
||
---
|
||
|
||
## Linux/Mac用户
|
||
|
||
```bash
|
||
cd AIclinicalresearch/tests
|
||
chmod +x run_tests.sh
|
||
./run_tests.sh
|
||
```
|
||
|
||
---
|
||
|
||
## ⚠️ 前提条件
|
||
|
||
**必须先启动Python服务！**
|
||
|
||
```bash
|
||
# 打开新终端
|
||
cd AIclinicalresearch/extraction_service
|
||
python main.py
|
||
```
|
||
|
||
看到这行表示启动成功：
|
||
```
|
||
INFO: Application startup complete.
|
||
INFO: Uvicorn running on http://0.0.0.0:8001
|
||
```
|
||
|
||
---
|
||
|
||
## 📊 预期结果
|
||
|
||
✅ **全部通过**：
|
||
```
|
||
总测试数: 18
|
||
✅ 通过: 18
|
||
❌ 失败: 0
|
||
通过率: 100.0%
|
||
|
||
🎉 所有测试通过！
|
||
```
|
||
|
||
⚠️ **部分失败**：
|
||
- 查看红色错误信息
|
||
- 检查失败的具体测试
|
||
- 查看Python服务日志
|
||
|
||
---
|
||
|
||
## 🎯 测试内容
|
||
|
||
- ✅ 6种简单填补方法（均值、中位数、众数、固定值、前向填充、后向填充）
|
||
- ✅ MICE多重插补（单列、多列）
|
||
- ✅ 边界情况（100%缺失、0%缺失、特殊字符）
|
||
- ✅ 各种数据类型（数值、分类、混合）
|
||
- ✅ 性能测试（1000行数据）
|
||
|
||
---
|
||
|
||
## 💡 提示
|
||
|
||
- **第一次运行**会自动安装依赖（pandas, numpy, requests）
|
||
- **测试时间**约 45-60 秒
|
||
- **测试数据**自动生成，无需手动准备
|
||
- **颜色输出**：绿色=通过，红色=失败，黄色=警告
|
||
|
||
---
|
||
|
||
## 🆘 遇到问题？
|
||
|
||
### 问题1：无法连接到服务
|
||
**解决**：确保Python服务在运行（`python main.py`）
|
||
|
||
### 问题2：依赖安装失败
|
||
**解决**：手动安装 `pip install pandas numpy requests`
|
||
|
||
### 问题3：测试失败
|
||
**解决**：查看错误信息，检查代码逻辑
|
||
|
||
---
|
||
|
||
**准备好了吗？启动服务，运行测试！** 🚀
|
||
|
||
|