Source code for espnet2.utils.eer

"""
Source code from:
https://github.com/clovaai/voxceleb_trainer/blob/master/tuneThreshold.py
"""

from operator import itemgetter

import numpy
from sklearn import metrics


[docs]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] 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.
[docs]def ComputeErrorRates(scores, labels): # Sort the scores from smallest to largest, and also get the corresponding # indexes of the sorted scores. We will treat the sorted scores as the # thresholds at which the the error-rates are evaluated. sorted_indexes, thresholds = zip( *sorted( [(index, threshold) for index, threshold in enumerate(scores)], key=itemgetter(1), ) ) sorted_labels = [] labels = [labels[i] for i in sorted_indexes] fnrs = [] fprs = [] # At the end of this loop, fnrs[i] is the number of errors made by # incorrectly rejecting scores less than thresholds[i]. And, fprs[i] # is the total number of times that we have correctly accepted scores # greater than thresholds[i]. 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) fprs_norm = len(labels) - fnrs_norm # Now divide by the total number of false negative errors to # obtain the false positive rates across all thresholds fnrs = [x / float(fnrs_norm) for x in fnrs] # Divide by the total number of corret positives to get the # true positive rate. Subtract these quantities from 1 to # get the false positive rates. fprs = [1 - x / float(fprs_norm) for x in fprs] 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.
[docs]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)): # See Equation (2). it is a weighted sum of false negative # and false positive errors. 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] # 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