def METHOD_NAME(y_label, y_score, p_target=0.01, c_miss=1, c_fa=1):
    # https://github.com/kaldi-asr/kaldi/blob/master/egs/sre08/v1/sid/compute_min_dcf.py
    from sklearn.metrics import det_curve
    fpr, fnr, thresholds = det_curve(y_label, y_score, pos_label=1)
    min_c_det = float("inf")
    min_c_det_threshold = thresholds[0]
    for i in range(0, len(fpr)):
        # See Equation (2).  it is a weighted sum of false negative
        # and false positive errors.
        c_det = c_miss * fnr[i] * p_target + c_fa * fpr[i] * (1 - p_target)
        if c_det < min_c_det:
            min_c_det = c_det
            min_c_det_threshold = thresholds[i]
    # See Equations (3) and (4).  Now we normalize the cost.
    c_def = min(c_miss * p_target, c_fa * (1 - p_target))
    min_dcf = min_c_det / c_def
    return min_dcf, min_c_det_threshold