logloss.py

import numpy as np

def compute_log_loss(predicted, actual, eps=1e-14):
    """ Computes the logarithmic loss between predicted and
    actual when these are 1D arrays.
    :param predicted: The predicted probabilities as floats between 0-1
    :param actual: The actual binary labels. Either 0 or 1.
    :param eps (optional): log(0) is inf, so we need to offset our
    predicted values slightly by eps from 0 or 1.
    """
    predicted = np.clip(predicted, eps, 1 - eps)
    loss = -1 * np.mean(actual * np.log(predicted)
    + (1 - actual)
    * np.log(1 - predicted))
    return loss