#! /usr/bin/python # -*- encoding: utf-8 -*- import numpy import torch import torch.nn.functional as F from sklearn import metrics from operator import itemgetter def accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) res.append(correct_k.mul_(100.0 / batch_size)) return res 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);