tuneThreshold.py

from sklearn import metrics
import numpy
from operator import itemgetter


def tuneThresholdfromScore(scores, labels, target_fa, target_fr=None):
    fpr, tpr, thresholds = metrics.roc_curve(labels, scores, pos_label=1)
    fnr = 1 - tpr

    tunedThreshold = []
    if target_fr:
        for tfr in target_fr:
            idx = numpy.nanargmin(numpy.absolute((tfr - fnr)))
            tunedThreshold.append([thresholds[idx], fpr[idx], fnr[idx]])

    for tfa in target_fa:
        idx = numpy.nanargmin(numpy.absolute((tfa - fpr)))  # numpy.where(fpr<=tfa)[0][-1] nanargmin 返回轴上最小的值忽略Nans
        tunedThreshold.append([thresholds[idx], fpr[idx], fnr[idx]])

    idxE = numpy.nanargmin(numpy.absolute((fnr - fpr)))
    eer = max(fpr[idxE], fnr[idxE]) * 100

    return tunedThreshold, eer, fpr, fnr

# Creates a list of false-negative rates, a list of false-positive rates
# and a list of decision thresholds that give those error-rates.
def ComputeErrorRates(scores, labels):
    sorted_indexes, thresholds = zip(*sorted([(index, threshold) for index, threshold in enumerate(scores)],
                                           key=itemgetter(1)))
    labels = [labels[i] for i in sorted_indexes]
    fnrs = []  # 负样本接受
    fprs = []  # 正样本接受

    for i in range(0, len(labels)):
        if i == 0:
            fnrs.append(labels[i])
            fprs.append(1 - labels[i])
        else:
            fnrs.append(fnrs[i-1] + labels[i])
            fprs.append(fprs[i-1] + 1 - labels[i])

    fnrs_norm = sum(labels)  # 真正样本个数
    fprs_norm = len(labels) - fnrs_norm  # 负样本个数
    fnrs = [x / float(fnrs_norm) for x in fnrs]  # 错误的拒绝 正样本分错的比例
    fprs = [1 - x / float(fprs_norm) for x in fprs]  # 错误接受 负样本分错的比例

    return fnrs, fprs, thresholds

# Computes the minimum of the detection cost function.  The comments refer to
# equations in Section 3 of the NIST 2016 Speaker Recognition Evaluation Plan.
def ComputeMinDcf(fnrs, fprs, thresholds, p_target, c_miss, c_fa):
    min_c_det = float("inf")
    min_c_det_threshold = thresholds[0]
    for i in range(0, len(fnrs)):
        c_det = c_miss * fnrs[i] * p_target + c_fa * fprs[i] * (1 - p_target)
        if c_det < min_c_det:
            min_c_det = c_det
            min_c_det_threshold = thresholds[i]

    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