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",
@@ -188,7 +188,6 @@ 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)