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#!/usr/bin/python
#-*- coding: utf-8 -*-
import os
import glob
import sys
import time
from sklearn import metrics
import numpy
import pdb
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]
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):
# 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.
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 |