def check_metric(actual, prediction, metric, round=4):
    """
    reserved for docstring
    """
    import numpy as np
    if metric == 'accuracy':
        from sklearn import metrics
        result = metrics.accuracy_score(actual, prediction)
        result = result.round(round)
    elif metric == 'recall':
        from sklearn import metrics
        result = metrics.recall_score(actual, prediction)
        result = result.round(round)
    elif metric == 'precision':
        from sklearn import metrics
        result = metrics.precision_score(actual, prediction)
        result = result.round(round)
    elif metric == 'f1':
        from sklearn import metrics
        result = metrics.f1_score(actual, prediction)
        result = result.round(round)
    elif metric == 'kappa':
        from sklearn import metrics
        result = metrics.cohen_kappa_score(actual, prediction)
        result = result.round(round)
    elif metric == 'auc':
        from sklearn import metrics
        result = metrics.roc_auc_score(actual, prediction)
        result = result.round(round)
    elif metric == 'mae':
        from sklearn import metrics
        result = metrics.mean_absolute_error(actual, prediction)
        result = result.round(round)
    elif metric == 'mse':
        from sklearn import metrics
        result = metrics.mean_squared_error(actual, prediction)
        result = result.round(round)
    elif metric == 'rmse':
        from sklearn import metrics
        result = metrics.mean_squared_error(actual, prediction)
        result = np.sqrt(result)
        result = result.round(round)
    elif metric == 'r2':
        from sklearn import metrics
        result = metrics.r2_score(actual, prediction)
        result = result.round(round)
    elif metric == 'rmsle':
        result = np.sqrt(np.mean(np.power(np.log(np.array(abs(prediction)) +
            1) - np.log(np.array(abs(actual)) + 1), 2)))
        result = result.round(round)
    elif metric == 'mape':
        mask = actual != 0
        result = (np.fabs(actual - prediction) / actual)[mask].mean()
        result = result.round(round)
    return result
