EER.py

from sklearn.metrics import roc_curve
import numpy as np

def evaluateEER(user_scores, imposter_scores):
    labels = [0]*len(user_scores) + [1]*len(imposter_scores)
    fpr, tpr, thresholds = roc_curve(labels, user_scores + imposter_scores)
#    print 'fpr','tpr','thres',fpr,tpr,thresholds
    missrates = 1 - tpr
    farates = fpr
#    array = np.zeros((123,3))
#    array[:,0] = missrates
#    array[:,1] = farates
#    array[:,2] = thresholds
#    print array
    dists = missrates - farates
    idx1 = np.argmin(dists[dists >= 0])
    idx2 = np.argmax(dists[dists < 0])
    x = [missrates[idx1], farates[idx1]]
    y = [missrates[idx2], farates[idx2]]
    a = ( x[0] - x[1] ) / ( y[1] - x[1] - y[0] + x[0] )
    eer = x[0] + a * ( y[0] - x[0] )
    return eer