        y_pred_label = np.round(y_pred)
        
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            logloss = log_loss(y_true, y_pred)
            acc = accuracy_score(y_true, y_pred_label)
            precision = precision_score(y_true, y_pred_label, average='macro')
            recall = recall_score(y_true, y_pred_label, average='macro')
            f1 = f1_score(y_true, y_pred_label, average='macro')
            fpr, tpr, thresholds = roc_curve(y_true, y_pred)
            auc_score = auc(fpr, tpr)

        metrics = [logloss,
                   acc,
                   auc_score,
                   precision,
                   recall,
                   f1]