checking results for DT

model_selection/test_models.py

@@ -219,8 +219,8 @@ if __name__ == "__main__":
         'FN':FN_scorer, 
         'FP':FP_scorer, 
         'TP':TP_scorer,
-        'AUROC': make_scorer(roc_auc_score, needs_threshold=True),  # AUROC requires decision function or probability outputs
-        'AUPRC': make_scorer(average_precision_score, needs_proba=True)  # AUPRC requires probability outputs
+        'AUROC': make_scorer(roc_auc_score),  # AUROC requires decision function or probability outputs
+        'AUPRC': make_scorer(average_precision_score)  # AUPRC requires probability outputs
         } 
     method_names = {
         0: "ORIG",
@@ -235,9 +235,10 @@ if __name__ == "__main__":
     scores_sheets = {} # To store score dfs as sheets in the same excel file
     for i, group in enumerate(['pre', 'post']):
         # Get test dataset based on group
-        X_test = data_dic['X_test' + group]
-        y_test = data_dic['y_test' + group]
+        X_test = data_dic['X_test_' + group]
+        y_test = data_dic['y_test_' + group]
         for j, method in enumerate(['', '', 'over_', 'under_']):
+            print(f"{group}-{method}")
             # Get train dataset based on group and method
             X_train = data_dic['X_train_' + method + group]
             y_train = data_dic['y_train_' + method + group]
@@ -259,7 +260,7 @@ if __name__ == "__main__":
             sheet_name = f"{group}_{method_names[j]}"
             scores_sheets[sheet_name] = scores_df
     # Write results to Excel file
-    with pd.ExcelWriter('./model_selection/test_results/testing_tuned_models.xlsx') as writer:
+    with pd.ExcelWriter('./test_results/testing_tuned_models.xlsx') as writer:
         for sheet_name, data in scores_sheets.items():
             data.to_excel(writer, sheet_name=sheet_name)
     # --------------------------------------------------------------------------------------------------------