changed colors and removed sd

model_selection/cv_metric_gen.py

@@ -201,42 +201,43 @@ if __name__ == "__main__":
                     # Generate ROC curves
                     mean_fpr = np.linspace(0, 1, 100)
                     tprs, aucs = [], []
+                    cmap = plt.get_cmap('tab10')  # Colormap for stronger colors
                     # Loop through each fold in the cross-validation
                     for fold_idx, (train, test) in enumerate(cv.split(X_train, y_train)):
                         # Fit the model on the training data
                         model.fit(X_train[train], y_train[train])
                         # Use RocCurveDisplay to generate the ROC curve
                         roc_display = RocCurveDisplay.from_estimator(model, X_train[test], y_train[test],
-                                                                    name=f"ROC fold {fold_idx}", alpha=0.3, lw=1, ax=axes[model_idx])
+                                                                    name=f"ROC fold {fold_idx}", alpha=0.6, lw=2, 
+                                                                    ax=axes[model_idx], color=cmap(fold_idx % 10))
                         # Interpolate the true positive rates to get a smooth curve
                         interp_tpr = np.interp(mean_fpr, roc_display.fpr, roc_display.tpr)
                         interp_tpr[0] = 0.0
                         # 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)
                     # 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
-                axes[model_idx].plot(mean_fpr, mean_tpr, color='b',
-                                    label=r'Mean ROC (AUC = %0.2f)' % mean_auc,
-                                    lw=2, alpha=.8)
+                    # 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)
                     # 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
-            # Saving curves plots
             # Adjust layout and save/show figure
             plt.tight_layout()
             plt.savefig(f'./output_cv_metrics/curves/{group}_{method_names[j]}.svg', format='svg', dpi=500)
             plt.close(fig)
-            # 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
     # Write results to Excel file
     with pd.ExcelWriter('./output_cv_metrics/metrics.xlsx') as writer:
         for sheet_name, data in scores_sheets.items():