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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/predictions/test_result_1234560.csv
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强化学习个人课程作业报告/outputs/tables/clustering_comparison.csv
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k,inertia,silhouette_x,log_likelihood,bic,aic,silhouette_y
2,1092962.434364126,0.174016661115075,181335.84491703784,-359250.54291550705,-362061.6898340757,0.41420390111182703
3,1018586.5047121042,0.17317021187208304,554291.2303605897,-1103445.131905755,-1107666.4607211794,0.2977020104302583
4,953249.4382030136,0.18080059886795355,972834.1094461675,-1938814.7081800548,-1944446.218892335,0.3964327255424141
5,889284.892342685,0.1964251564081267,1002913.0930748597,-1997256.4935405836,-2004298.1861497194,0.40146893512413845
6,818950.9117652641,0.17683056672008368,1180025.734163945,-2349765.5938218986,-2358217.46832789,0.24683353848428613
7,777658.2185885893,0.197056012688701,1203191.531501821,-2394381.006600795,-2404243.063003642,0.3109553553475885
8,691940.8330833976,0.20149802939267383,1261969.3739466753,-2510220.5095936474,-2521492.7478933507,0.17264064800570944
k,inertia,silhouette_x,log_likelihood,bic,aic,silhouette_y
2,1092962.434364126,0.174016661115075,181335.84491703784,-359250.54291550705,-362061.6898340757,0.41420390111182703
3,1018586.5047121042,0.17317021187208304,554291.2303605897,-1103445.131905755,-1107666.4607211794,0.2977020104302583
4,953249.4382030136,0.18080059886795355,972834.1094461675,-1938814.7081800548,-1944446.218892335,0.3964327255424141
5,889284.892342685,0.1964251564081267,1002913.0930748597,-1997256.4935405836,-2004298.1861497194,0.40146893512413845
6,818950.9117652641,0.17683056672008368,1180025.734163945,-2349765.5938218986,-2358217.46832789,0.24683353848428613
7,777658.2185885893,0.197056012688701,1203191.531501821,-2394381.006600795,-2404243.063003642,0.3109553553475885
8,691940.8330833976,0.20149802939267383,1261969.3739466753,-2510220.5095936474,-2521492.7478933507,0.17264064800570944
1 k inertia silhouette_x log_likelihood bic aic silhouette_y
2 2 1092962.434364126 0.174016661115075 181335.84491703784 -359250.54291550705 -362061.6898340757 0.41420390111182703
3 3 1018586.5047121042 0.17317021187208304 554291.2303605897 -1103445.131905755 -1107666.4607211794 0.2977020104302583
4 4 953249.4382030136 0.18080059886795355 972834.1094461675 -1938814.7081800548 -1944446.218892335 0.3964327255424141
5 5 889284.892342685 0.1964251564081267 1002913.0930748597 -1997256.4935405836 -2004298.1861497194 0.40146893512413845
6 6 818950.9117652641 0.17683056672008368 1180025.734163945 -2349765.5938218986 -2358217.46832789 0.24683353848428613
7 7 777658.2185885893 0.197056012688701 1203191.531501821 -2394381.006600795 -2404243.063003642 0.3109553553475885
8 8 691940.8330833976 0.20149802939267383 1261969.3739466753 -2510220.5095936474 -2521492.7478933507 0.17264064800570944
强化学习个人课程作业报告/outputs/tables/model_comparison_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
Baseline_LR,0.7595294117647059,0.7337904761904762,0.7493991157707756,0.7234383324236036,0.7663239074550129,0.6487372909150542,0.7552537989007436
model,train_accuracy,val_accuracy,train_f1_macro,val_f1_macro,val_f1_High,val_f1_Low,val_f1_Standard
Baseline_LR,0.7595294117647059,0.7337904761904762,0.7493991157707756,0.7234383324236036,0.7663239074550129,0.6487372909150542,0.7552537989007436
1 model train_accuracy val_accuracy train_f1_macro val_f1_macro val_f1_High val_f1_Low val_f1_Standard
2 Baseline_LR 0.7595294117647059 0.7337904761904762 0.7493991157707756 0.7234383324236036 0.7663239074550129 0.6487372909150542 0.7552537989007436
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