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chore: 更新项目文档、依赖和训练脚本

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- 更新 requirements.txt，添加 opencv-python-headless 并补充 uv 安装说明
- 修复 CSV 文件中的换行符格式（CRLF 转 LF）
- 更新 TASK_PROGRESS.md，记录并行训练实现和 WSL 支持
- 优化 train_improved.py 代码格式，移除多余空行和注释
- 更新课程作业要求文档的字符编码
- 添加新的 TensorBoard 日志文件和训练模型
This commit is contained in:
刘航宇
2026-05-01 09:26:23 +08:00
parent 6b929e9790
commit d6860f1f15
16 changed files with 25712 additions and 25680 deletions
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强化学习个人课程作业报告/outputs/tables/personalised_improvement_summary.csv
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model,train_accuracy,val_accuracy,train_f1_macro,val_f1_macro,val_f1_High,val_f1_Low,val_f1_Standard,train_time
Baseline_LR,0.7593680672268908,0.7341714285714286,0.7492574544185482,0.7237629331592531,0.7665209565440987,0.6489501312335958,0.7558177117000646,
RandomForest,1.0,0.7877333333333333,1.0,0.770789728543472,0.7874554916461244,0.7095334685598377,0.8153802254244543,57.91048526763916
XGBoost,0.8519529411764706,0.8371047619047619,0.8297116592669606,0.8143842728003406,0.8904623073719283,0.6944039941751612,0.8582865168539325,67.63970804214478
XGBoost_Tuned,0.9767663865546219,0.8700190476190476,0.9739400525375727,0.8519502714571496,0.9084439578486383,0.7620280474649407,0.8853788090578697,142.65462470054626
XGB_CatA_MissingHandling,0.9772638655462185,0.870552380952381,0.9745439553742655,0.8529411889528661,0.910207423580786,0.763542562338779,0.885073580939033,
Ensemble_SoftVoting,0.9972436974789916,0.8675047619047619,0.9969472283391928,0.851001101708816,0.9024125779343996,0.7684120902511707,0.8821786369408776,
model,train_accuracy,val_accuracy,train_f1_macro,val_f1_macro,val_f1_High,val_f1_Low,val_f1_Standard,train_time
Baseline_LR,0.7593680672268908,0.7341714285714286,0.7492574544185482,0.7237629331592531,0.7665209565440987,0.6489501312335958,0.7558177117000646,
RandomForest,1.0,0.7877333333333333,1.0,0.770789728543472,0.7874554916461244,0.7095334685598377,0.8153802254244543,57.91048526763916
XGBoost,0.8519529411764706,0.8371047619047619,0.8297116592669606,0.8143842728003406,0.8904623073719283,0.6944039941751612,0.8582865168539325,67.63970804214478
XGBoost_Tuned,0.9767663865546219,0.8700190476190476,0.9739400525375727,0.8519502714571496,0.9084439578486383,0.7620280474649407,0.8853788090578697,142.65462470054626
XGB_CatA_MissingHandling,0.9772638655462185,0.870552380952381,0.9745439553742655,0.8529411889528661,0.910207423580786,0.763542562338779,0.885073580939033,
Ensemble_SoftVoting,0.9972436974789916,0.8675047619047619,0.9969472283391928,0.851001101708816,0.9024125779343996,0.7684120902511707,0.8821786369408776,
1 model train_accuracy val_accuracy train_f1_macro val_f1_macro val_f1_High val_f1_Low val_f1_Standard train_time
2 Baseline_LR 0.7593680672268908 0.7341714285714286 0.7492574544185482 0.7237629331592531 0.7665209565440987 0.6489501312335958 0.7558177117000646
3 RandomForest 1.0 0.7877333333333333 1.0 0.770789728543472 0.7874554916461244 0.7095334685598377 0.8153802254244543 57.91048526763916
4 XGBoost 0.8519529411764706 0.8371047619047619 0.8297116592669606 0.8143842728003406 0.8904623073719283 0.6944039941751612 0.8582865168539325 67.63970804214478
5 XGBoost_Tuned 0.9767663865546219 0.8700190476190476 0.9739400525375727 0.8519502714571496 0.9084439578486383 0.7620280474649407 0.8853788090578697 142.65462470054626
6 XGB_CatA_MissingHandling 0.9772638655462185 0.870552380952381 0.9745439553742655 0.8529411889528661 0.910207423580786 0.763542562338779 0.885073580939033
7 Ensemble_SoftVoting 0.9972436974789916 0.8675047619047619 0.9969472283391928 0.851001101708816 0.9024125779343996 0.7684120902511707 0.8821786369408776