<jupyter_start><jupyter_text>Validation Metrics<jupyter_code>if val_metrics_csv.is_file():
    print_metrics(val_metrics_csv=val_metrics_csv, test_metrics_csv=val_metrics_csv)<jupyter_output><empty_output><jupyter_text>Test Metrics<jupyter_code>if val_metrics_csv.is_file() and test_metrics_csv.is_file():
    print_metrics(val_metrics_csv=val_metrics_csv, test_metrics_csv=test_metrics_csv)<jupyter_output><empty_output><jupyter_text>AUC and PR curves Test Set<jupyter_code>if test_metrics_csv.is_file():
    plot_pr_and_roc_curves_from_csv(test_metrics_csv)<jupyter_output><empty_output><jupyter_text>Validation set<jupyter_code>if val_metrics_csv.is_file():
    plot_pr_and_roc_curves_from_csv(val_metrics_csv)<jupyter_output><empty_output><jupyter_text>Training set<jupyter_code>if train_metrics_csv.is_file():
    plot_pr_and_roc_curves_from_csv(train_metrics_csv)<jupyter_output><empty_output><jupyter_text>Best and worst samples by ID<jupyter_code>if val_metrics_csv.is_file() and test_metrics_csv.is_file():
    print_k_best_and_worst_performing(val_metrics_csv=val_metrics_csv, test_metrics_csv=test_metrics_csv, 
                                      k=number_best_and_worst_performing)<jupyter_output><empty_output><jupyter_text>Plot best and worst sample images<jupyter_code>if val_metrics_csv.is_file() and test_metrics_csv.is_file() and dataset_csv_path.is_file() and \
 dataset_subject_column and dataset_file_column:
    plot_k_best_and_worst_performing(val_metrics_csv=val_metrics_csv, test_metrics_csv=test_metrics_csv, 
                                     k=number_best_and_worst_performing, dataset_csv_path=dataset_csv_path,
                                     dataset_subject_column=dataset_subject_column, dataset_file_column=dataset_file_column)<jupyter_output><empty_output>
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