Mean Absolute Percentage Error (MAPE) metric for python sklearn. Written in response to a question on Cross Validated: http://stats.stackexchange.com/questions/58391/mean-absolute-percentage-error-mape-in-scikit-learn/62511#62511

sklearn-MAPE.py

from sklearn.utils import check_arrays

def mean_absolute_percentage_error(y_true, y_pred): 
    """
    Use of this metric is not recommended; for illustration only. 
    See other regression metrics on sklearn docs:
      http://scikit-learn.org/stable/modules/classes.html#regression-metrics

    Use like any other metric
    >>> y_true = [3, -0.5, 2, 7]; y_pred = [2.5, -0.3, 2, 8]
    >>> mean_absolute_percentage_error(y_true, y_pred)
    Out[]: 24.791666666666668
    """

    y_true, y_pred = check_arrays(y_true, y_pred)

    ## Note: does not handle mix 1d representation
    #if _is_1d(y_true): 
    #    y_true, y_pred = _check_1d_array(y_true, y_pred)

    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100