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How to use the kaggler.const.EPS function in Kaggler

To help you get started, we’ve selected a few Kaggler examples, based on popular ways it is used in public projects.

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jeongyoonlee / Kaggler / kaggler / metrics / classification.py View on Github

def logloss(y, p):
    """Bounded log loss error.

    Args:
        y (numpy.array): target
        p (numpy.array): prediction

    Returns:
        bounded log loss error
    """

    p[p &lt; EPS] = EPS
    p[p &gt; 1 - EPS] = 1 - EPS
    return log_loss(y, p)

jeongyoonlee / Kaggler / kaggler / metrics / regression.py View on Github

def mape(y, p):
    """Mean Absolute Percentage Error (MAPE).

    Args:
        y (numpy.array): target
        p (numpy.array): prediction

    Returns:
        e (numpy.float64): MAPE
    """

    filt = np.abs(y) > EPS
    return np.mean(np.abs(1 - p[filt] / y[filt]))