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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 |