Metrics generated as expected for DT, generate curves for each cv split

model_selection/cv_metric_gen.py

@@ -153,14 +153,14 @@ if __name__ == "__main__":
         'F1':make_scorer(f1_score), 
         'PREC':make_scorer(precision_score), 
         'REC':make_scorer(recall_score), 
-        # 'ACC': make_scorer(accuracy_score),
-        # 'NREC': negative_recall_scorer, 
-        # 'TN':TN_scorer, 
-        # 'FN':FN_scorer, 
-        # 'FP':FP_scorer, 
-        # 'TP':TP_scorer,
-        # 'AUROC': make_scorer(roc_auc_score), 
-        # 'AUPRC': make_scorer(average_precision_score)
+        'ACC': make_scorer(accuracy_score),
+        'NREC': negative_recall_scorer, 
+        'TN':TN_scorer, 
+        'FN':FN_scorer, 
+        'FP':FP_scorer, 
+        'TP':TP_scorer,
+        'AUROC': make_scorer(roc_auc_score), 
+        'AUPRC': make_scorer(average_precision_score)
         } 
     method_names = {
         0: "ORIG",
@@ -188,13 +188,12 @@ if __name__ == "__main__":
             scores_df = pd.DataFrame(columns=range(1,11), index=[f"{model_name}_{metric_name}" for model_name in models.keys() for metric_name in scorings.keys()])
             # Metric generation for each model
             for model_name, model in models.items():
-                if model_name == 'DT':
-                    print(f"{group}-{method_names[j]}-{model_name}")
-                    # Retrieve cv scores for our metrics of interest
-                    scores = cross_validate(model, X_train, y_train, scoring=scorings, cv=cv, return_train_score=True, n_jobs=10)
-                    # Save results of each fold
-                    for metric_name in scorings.keys():
-                        scores_df.loc[model_name + f'_{metric_name}']=list(np.around(np.array(scores[f"test_{metric_name}"]),4)) 
+                print(f"{group}-{method_names[j]}-{model_name}")
+                # Retrieve cv scores for our metrics of interest
+                scores = cross_validate(model, X_train, y_train, scoring=scorings, cv=cv, return_train_score=True, n_jobs=10)
+                # Save results of each fold
+                for metric_name in scorings.keys():
+                    scores_df.loc[model_name + f'_{metric_name}']=list(np.around(np.array(scores[f"test_{metric_name}"]),4)) 
             # Store the DataFrame in the dictionary with a unique key for each sheet
             sheet_name = f"{group}_{method_names[j]}"
             scores_sheets[sheet_name] = scores_df