Thinking about merging scroings and plots

model_selection/cv_metric_gen.py

@@ -175,8 +175,8 @@ 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(['pre', 'post']): # '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]
@@ -190,7 +190,6 @@ if __name__ == "__main__":
                 axes = [axes]
             # Metric generation for each model
             for model_idx, (model_name, model) in enumerate(models.items()):
-                if model_name == 'XGB':
                 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)