def _score(p_pos, p_neg, w=1.0):
    """Score probability vs. weighted negative probability.

    e.g. P(pos) - (w * P(neg))

    Parameters
    ----------
    p_pos : float
        Probability of positive assignment.
    p_neg : float
        Probability of negative assignment.
    w : float (default: 1.0)
        Weight for negative assignment probability.

    Returns
    -------
    float
        Total probability of positive identification, minus
        contribution to negative identification.

    """
    # p_in = results[in_label][col]
    # p_out = np.sum([results[fam]['probability'] for fam in out_labels], axis=0)
    # p_bg = bg_labels
    return p_pos - (w * p_neg)