Generated first ROC curve to see behavior

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'
+        for j, method in enumerate(['']): # '', 'over_', 'under_'
             # print(f"{group}-{method_names[j]}")
             # Get train dataset based on group and method
             X_train = data_dic['X_train_' + method + group]
@@ -191,6 +191,7 @@ if __name__ == "__main__":
                 axes = [axes]
             # Metric generation for each model
             for model_idx, (model_name, model) in enumerate(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)
@@ -220,19 +221,16 @@ if __name__ == "__main__":
                 mean_tpr[-1] = 1.0
                 mean_auc = auc(mean_fpr, mean_tpr)  # Calculate the mean AUC
                 std_auc = np.std(aucs)
-
                 # Plot the mean ROC curve
                 axes[model_idx].plot(mean_fpr, mean_tpr, color='b',
                                     label=r'Mean ROC (AUC = %0.2f $\pm$ %0.2f)' % (mean_auc, std_auc),
                                     lw=2, alpha=.8)
-
                 # Plot the standard deviation of the TPRs
                 std_tpr = np.std(tprs, axis=0)
                 tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
                 tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
                 axes[model_idx].fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,
                                             label=r'$\pm$ 1 std. dev.')
-
                 # Set plot limits and title
                 axes[model_idx].set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05],
                                     title=f"ROC Curve - {model_name} ({group}-{method_names[j]})")