Trying to solve the mean PR curve issue

model_selection/cv_metric_gen.py

@@ -190,8 +190,8 @@ if __name__ == "__main__":
     # Metric generation through cv for tuned models3
     # --------------------------------------------------------------------------------------------------------
     scores_sheets = {} # To store score dfs as sheets in the same excel file
-    for i, group in enumerate(['pre', 'post']):
-        for j, method in enumerate(['', '', 'over_', 'under_']): 
+    for i, group in enumerate(['post']): # ['pre', 'post']
+        for j, method in enumerate(['']): # ['', '', 'over_', 'under_']
             # Get train dataset based on group and method
             X_train = data_dic['X_train_' + method + group]
             y_train = data_dic['y_train_' + method + group]
@@ -217,7 +217,7 @@ if __name__ == "__main__":
                 # Manually loop through each fold in the cross-validation
                 for fold_idx, (train_idx, test_idx) in enumerate(cv.split(X_train, y_train)):
                     X_train_fold, X_test_fold = X_train[train_idx], X_train[test_idx]
-                    y_train_fold, y_test_fold = y_train[train_idx], y[test_idx]
+                    y_train_fold, y_test_fold = y_train[train_idx], y_train[test_idx]
                     # Fit the model on the training data
                     model.fit(X_train_fold, y_train_fold)
                     # --------------------- SCORINGS ---------------------------