Trying to fix issue

model_selection/cv_metric_gen.py

@@ -226,7 +226,7 @@ if __name__ == "__main__":
                         score = scorer(model, X_test_fold, y_test_fold)
                         fold_scores[metric_name].append(score)
                     # --------------------- END SCORINGS ---------------------------
-                # --------------------- CURVES ---------------------------
+                    # --------------------- CURVES ---------------------------
                     # Generate ROC curve for the fold
                     roc_display = RocCurveDisplay.from_estimator(model, X_test_fold, y_test_fold,
                                                                 name=f"ROC fold {fold_idx}", alpha=0.6, lw=2,
@@ -235,11 +235,21 @@ if __name__ == "__main__":
                     interp_tpr[0] = 0.0
                     tprs.append(interp_tpr)
                     aucs.append(roc_display.roc_auc)
+                    
                     # Generate Precision-Recall curve for the fold
                     pr_display = PrecisionRecallDisplay.from_estimator(model, X_test_fold, y_test_fold,
                                                                     name=f"PR fold {fold_idx}", alpha=0.6, lw=2,
                                                                     ax=axes[model_idx][1], color=cmap(fold_idx % 10))
-                    interp_precision = np.interp(mean_recall, pr_display.recall[::-1], pr_display.precision[::-1])
+                    # Reverse the recall and precision arrays for interpolation
+                    recall_for_interp = pr_display.recall[::-1]
+                    precision_for_interp = pr_display.precision[::-1]
+                    
+                    # Handle the edge case where recall_for_interp has duplicates, which can break np.interp
+                    recall_for_interp, unique_indices = np.unique(recall_for_interp, return_index=True)
+                    precision_for_interp = precision_for_interp[unique_indices]
+                    
+                    # Interpolate precision
+                    interp_precision = np.interp(mean_recall, recall_for_interp, precision_for_interp)
                     precisions.append(interp_precision)
                     pr_aucs.append(pr_display.average_precision)
                 # Plot diagonal line for random guessing in ROC curve
@@ -252,6 +262,7 @@ if __name__ == "__main__":
                 # Set ROC plot limits and title
                 axes[model_idx][0].set(xlim=[-0.05, 1.05], ylim=[-0.05, 1.05], title=f"ROC Curve - {model_name} ({group}-{method_names[j]})")
                 axes[model_idx][0].legend(loc="lower right")
+                
                 # Compute mean Precision-Recall curve
                 mean_precision = np.mean(precisions, axis=0)
                 mean_pr_auc = np.mean(pr_aucs)
@@ -266,7 +277,7 @@ if __name__ == "__main__":
                 # Store the fold scores in the dataframe
                 for metric_name, scores in fold_scores.items():
                     scores_df.loc[f"{model_name}_{metric_name}"] = np.around(scores, 4)
-            # 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 figure