Corrected hyperparam_tuning metric computation

model_selection/hyperparam_tuning.py

@@ -73,13 +73,13 @@ if __name__ == "__main__":
     # --------------------------------------------------------------------------------------------------------
     # 1. No class weight
     models_simple = {"DT" : DecisionTreeClassifier(), 
-            # "RF" : RandomForestClassifier(), 
-            # "Bagging" : BaggingClassifier(),
-            # "AB" : AdaBoostClassifier(algorithm='SAMME'), 
-            # "XGB": XGBClassifier(),
-            # "LR" : LogisticRegression(max_iter=1000), 
-            # "SVM" : SVC(probability=True, max_iter=1000), 
-            # "MLP" : MLPClassifier(max_iter=500)
+            "RF" : RandomForestClassifier(), 
+            "Bagging" : BaggingClassifier(),
+            "AB" : AdaBoostClassifier(algorithm='SAMME'), 
+            "XGB": XGBClassifier(),
+            "LR" : LogisticRegression(max_iter=1000), 
+            "SVM" : SVC(probability=True, max_iter=1000), 
+            "MLP" : MLPClassifier(max_iter=500)
             # "ElNet" : LogisticRegression(max_iter=1000, penalty='elasticnet')
             }
     
@@ -141,8 +141,8 @@ if __name__ == "__main__":
     # --------------------------------------------------------------------------------------------------------
     # Store each df as a sheet in an excel file
     sheets_dict = {}
-    for i, group in enumerate(['pre']): #['pre', 'post']
-        for j, method in enumerate(['under_']): #['', '', 'over_', 'under_']
+    for i, group in enumerate(['pre', 'post']):
+        for j, method in enumerate(['', '', 'over_', 'under_']):
             # Get dataset based on group and method
             X = data_dic['X_train_' + method + group]
             y = data_dic['y_train_' + method + group]