ROC curves looking good

model_selection/cv_metric_gen.py

@@ -216,25 +216,23 @@ if __name__ == "__main__":
                         # Append the interpolated TPR and AUC for this fold
                         tprs.append(interp_tpr)
                         aucs.append(roc_display.roc_auc)
-            
                     # Plot the diagonal line representing random guessing
-                    axes[model_idx].plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', alpha=.8)
+                    axes[model_idx].plot([0, 1], [0, 1], linestyle='--', lw=2, color='r', alpha=.8, label='Random guessing')
                     # Compute the mean of the TPRs
                     mean_tpr = np.mean(tprs, axis=0)
                     mean_tpr[-1] = 1.0
                     mean_auc = auc(mean_fpr, mean_tpr)  # Calculate the mean AUC
                     # Plot the mean ROC curve with a thicker line and distinct color
-                    axes[model_idx].plot(mean_fpr, mean_tpr, color='b', lw=4, 
-                                        label=r'Mean ROC (AUC = %0.2f)' % mean_auc, alpha=.8)
+                    axes[model_idx].plot(mean_fpr, mean_tpr, color='b', lw=4,
+                                         label=r'Mean ROC (AUC = %0.2f)' % mean_auc, alpha=.8)
                     # 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]})")
                     axes[model_idx].legend(loc="lower right")
-                    
             # Store the DataFrame in the dictionary with a unique key for each sheet
             sheet_name = f"{group}_{method_names[j]}"
             scores_sheets[sheet_name] = scores_df
-            # Adjust layout and save/show figure
+            # Adjust layout and save figure
             plt.tight_layout()
             plt.savefig(f'./output_cv_metrics/curves/{group}_{method_names[j]}.svg', format='svg', dpi=500)
             plt.close(fig)