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Preliminary experiment (selection of impact factors) ├── 01.Single factor perturbation method (preliminary elimination) │ ├── Stratified sampling.py │ └── Summary table of single factor perturbation method.xlsx └── 02.Global sensitivity analysis (global validation) │ ├── Morris method sampling.py │ ├── Morris sampling calculation results.xlsx │ ├── Results │ ├── Code global sensitivity analysis (after factor normalization).xlsx │ ├── Sampling result calculation.py │ └── morris_sensitivity_plot_with_quadrants.png │ └── ~$Morris取样计算结果.xlsx ├── 02.Formal database ├── 01.Latin Hypercube Sampling.py ├── 02.Normalization of sampling results.py ├── 03.Dataset distribution.py ├── 04.Pearson correlation analysis.py ├── A.Latin hypercube sampling results (after normalization of input factors).xlsx ├── B.Latin hypercube sampling results.xlsx └── END.Full dataset.xlsx ├── 03.Training of base learners, meta-learners, and comparison models ├── 00.Elastic Net Regression, ENR │ ├── Optimal hyperparameter training.py │ ├── SMA-ENR.py │ └── final_enr_model_refit_on_full_train.pkl ├── 01.Decision Tree Regression, DTR │ ├── Optimal hyperparameter training.py │ ├── SMA-DTR.py │ └── final_dtr_model_refit_on_full_train.pkl ├── 02.K nearest neighbor regression, KNNR │ ├── Optimal hyperparameter training.py │ ├── SMA-KNNR.py │ └── final_knnr_model_refit_on_full_train.pkl ├── 03.Multilayer Perceptron Regression, MLPR │ ├── Optimal hyperparameter training.py │ ├── SMA-MLPR.py │ └── final_mlpr_model_refit_on_full_train.pkl ├── 04.Support Vector Regression，SVR │ ├── Optimal hyperparameter training.py │ ├── SMA-SVR.py │ └── final_svmr_model_refit_on_full_train.pkl ├── 05.(Bagging Integration)Random Forest │ ├── Optimal hyperparameter training.py │ ├── SMA-RF.py │ └── final_rf_model_refit_on_full_train.pkl ├── 06.(Boosting Integration)XGBoost │ ├── Optimal hyperparameter training.py │ ├── SMA-XGBoost.py │ └── final_xgb_model_refit_on_full_train.pkl ├── 07.(Stacking Integration-New method)SHAP-Transformer │ ├── Optimal hyperparameter training.py │ ├── SMA-Transformer.py │ └── transformer_regressor_cv_refit.pt ├── END.Model evaluation metrics.xlsx ├── Input dataset for 00-06.xlsx ├── Test input dataset for 07.xlsx └── Training input dataset for 07.xlsx ├── 04.Final model ensemble construction ├── 00.Integrated framework packaging.py ├── 01.Testing the framework after packaging.py ├── Prediction and error results of the packaged model.xlsx └── SHAP-Transformer heterogeneous ensemble method.pkl ├── 05.SHAP analysis ├── Global SHAP analysis general.py ├── Input dataset for base learner SHAP analysis.xlsx ├── Input dataset for meta-learner SHAP analysis.xlsx ├── SHAP-SVMR.py ├── SHAP-SVMR │ ├── decision_plot.png │ ├── global_contribution_bar.png │ ├── sample_0_waterfall.png │ └── summary_beeswarm.png ├── SHAP-Transformer.py └── SHAP-Transformer │ ├── decision_plot.png │ ├── global_contribution_bar.png │ ├── sample_0_waterfall.png │ └── summary_beeswarm.png ├── 99.(Under update...) 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