log_loss_weighted.py 文件源码

def log_loss_value(Z, weights, total_weights, rho):
    """
    computes the value and slope of the logistic loss in a numerically stable way
    supports sample non-negative weights for each example in the training data
    see http://stackoverflow.com/questions/20085768/

    Parameters
    ----------
    Z               numpy.array containing training data with shape = (n_rows, n_cols)
    rho             numpy.array of coefficients with shape = (n_cols,)
    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))

    """
    scores = Z.dot(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