from sklearn import metrics
from scipy.optimize import brentq
from scipy.interpolate import interp1d
def cal_eer(score_true, score_false):
    """ 计算EER

    Args:
    scores_true: 正样例的分数列表
    scores_false: 负样例的分数列表

    Return:
    (EER, threshold)
    """

    fpr, tpr, thresholds = metrics.roc_curve([1]*len(score_true)+[0]*len(score_false), score_true+score_false, pos_label=1)

    eer = brentq(lambda x : 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
    thresh = interp1d(fpr, thresholds)(eer)
    return eer, thresh