metrics.py 文件源码

def has_approx_support(m, m_hat, prob=0.01):
    """Returns 1 if model selection error is less than or equal to prob rate,
    0 else.

    NOTE: why does np.nonzero/np.flatnonzero create so much problems?
    """
    m_nz = np.flatnonzero(np.triu(m, 1))
    m_hat_nz = np.flatnonzero(np.triu(m_hat, 1))

    upper_diagonal_mask = np.flatnonzero(np.triu(np.ones(m.shape), 1))
    not_m_nz = np.setdiff1d(upper_diagonal_mask, m_nz)

    intersection = np.in1d(m_hat_nz, m_nz)  # true positives
    not_intersection = np.in1d(m_hat_nz, not_m_nz)  # false positives

    true_positive_rate = 0.0
    if len(m_nz):
        true_positive_rate = 1. * np.sum(intersection) / len(m_nz)
        true_negative_rate = 1. - true_positive_rate

    false_positive_rate = 0.0
    if len(not_m_nz):
        false_positive_rate = 1. * np.sum(not_intersection) / len(not_m_nz)

    return int(np.less_equal(true_negative_rate + false_positive_rate, prob))