log_loss_weighted.py 文件源码

def log_loss_value_from_scores(weights, total_weights, scores):
    """
    computes the logistic loss value from a vector of scores in a numerically stable way
    where scores = Z.dot(rho)

    see also: http://stackoverflow.com/questions/20085768/

    this function is used for heuristics (discrete_descent, sequential_rounding).
    to save computation when running the heuristics, we store the scores and
    call this function to compute the loss directly from the scores
    this reduces the need to recompute the dot product.

    Parameters
    ----------
    scores          numpy.array of scores = Z.dot(rho)
    total_weights   numpy.sum(total_weights) (only included to reduce computation)
    weights         numpy.array of sample weights with shape (n_rows,)

    Returns
    -------
    loss_value  scalar = 1/n_rows * sum(log( 1 .+ exp(-Z*rho))

    """
    pos_idx = scores > 0
    loss_value = np.empty_like(scores)
    loss_value[pos_idx] = np.log1p(np.exp(-scores[pos_idx]))
    loss_value[~pos_idx] = -scores[~pos_idx] + np.log1p(np.exp(scores[~pos_idx]))
    loss_value = loss_value.dot(weights) / total_weights

    return loss_value