modify scripts

scripts/model_analysis.py

@@ -26,9 +26,9 @@ feature_cols = x_train.columns.tolist()
 
 ###Reload the model
 from h2o import load_model
-restored_model1 = h2o.load_model("../product/top_model_1/StackedEnsemble_AllModels_1_AutoML_1_20250401_220205")
-restored_model2 = h2o.load_model("../product/top_model_2/StackedEnsemble_BestOfFamily_1_AutoML_1_20250401_220205")
-restored_model3 = h2o.load_model("../product/top_model_3/GBM_4_AutoML_1_20250401_220205")
+restored_model1 = h2o.load_model("./product/top_model_1/StackedEnsemble_AllModels_1_AutoML_1_20250401_220205")
+restored_model2 = h2o.load_model("./product/top_model_2/StackedEnsemble_BestOfFamily_1_AutoML_1_20250401_220205")
+restored_model3 = h2o.load_model("./product/top_model_3/GBM_4_AutoML_1_20250401_220205")
 
 ###Test the model
 perf1=restored_model1.model_performance(test_h2o)

scripts/model_constrcution.py

@@ -52,36 +52,3 @@ for model_id in model_ids:
 
 cv_all_models_df = pd.concat(all_model_summaries, ignore_index=True)
 cv_all_models_df.to_csv("./intermediate/cross_validation_result.csv",index=False)
-
-###Reload the model for addtional test
-from h2o import load_model
-restored_model1 = h2o.load_model("./product/top_model_1/StackedEnsemble_AllModels_1_AutoML_1_20250401_220205")
-restored_model2 = h2o.load_model("./product/top_model_2/StackedEnsemble_BestOfFamily_1_AutoML_1_20250401_220205")
-restored_model3 = h2o.load_model("./product/top_model_3/GBM_4_AutoML_1_20250401_220205")
-
-perf1=restored_model1.model_performance(test_h2o)
-perf2=restored_model2.model_performance(test_h2o)
-perf3=restored_model3.model_performance(test_h2o)
-
-###Obtain the performance
-threshold = 0.5
-acc = perf1.accuracy(thresholds=[threshold])[0][1]
-f1 = perf1.F1(thresholds=[threshold])[0][1]
-prec = perf1.precision(thresholds=[threshold])[0][1]
-rec = perf1.recall(thresholds=[threshold])[0][1]
-spec = perf1.specificity(thresholds=[threshold])[0][1]
-AUC= perf1.auc()
-LogLoss=perf1.logloss()
-print(f"AUC = {AUC}")
-print(f"LogLoss = {LogLoss}")
-print(f"Threshold = {threshold}")
-print(f"Accuracy     = {acc:.4f}")
-print(f"F1 Score     = {f1:.4f}")
-print(f"Precision    = {prec:.4f}")
-print(f"Recall       = {rec:.4f}")
-print(f"Specificity  = {spec:.4f}")
-
-###SHAP analysis for the third-best model
-restored_model3.shap_summary_plot(test_h2o[:,:-1])
-fig = plt.gcf()
-fig.savefig("./product/3shap_summary_plot.png", dpi=300, bbox_inches="tight")