Equal Error Rate in Python

Question

Could anybody tell me how could I compute Equal Error Rate(EER) from ROC Curve in python? In scikit-learn there is method to compute roc curve and auc but could not find the method to compute EER.

from sklearn.metrics import roc_curve, auc

ANSRWER:

I think I implemented myself.

The idea of ROC EER is the intersection point between a stright line joining (1,0) and (0,1) and the roc Curve. It is a only point where it intersects. For a straight line with a=1 and b=1, the equation would be x+y =1 (x/a +y/b =1.0) . So the intersection point would be the values of true positive rate (tpr) and false positive rate (fpr) which statisfies the following equation:

    x + y - 1.0 = 0.0

Thus implemented the method as:

 def compute_roc_EER(fpr, tpr):
    roc_EER = []
    cords = zip(fpr, tpr)
    for item in cords:
        item_fpr, item_tpr = item
        if item_tpr + item_fpr == 1.0:
            roc_EER.append((item_fpr, item_tpr))
assert(len(roc_EER) == 1.0)
return np.array(roc_EER)

So here one value is error rate and another value is accuracy.

May be somebody could help me to verify.

Answer 1

For any one else whom arrives here via a Google search. The Fran answer is incorrect as Gerhard points out. The correct code would be:

import numpy as np
from sklearn.metrics import roc_curve

fpr, tpr, threshold = roc_curve(y, y_pred, pos_label=1)
fnr = 1 - tpr
eer_threshold = threshold[np.nanargmin(np.absolute((fnr - fpr)))]

Note that this gets you the threshold at which the EER occurs not, the EER. The EER is defined as FPR = 1 - PTR = FNR. Thus to get the EER (the actual error rate) you could use the following:

EER = fpr[np.nanargmin(np.absolute((fnr - fpr)))]

as a sanity check the value should be close to

EER = fnr[np.nanargmin(np.absolute((fnr - fpr)))]

since this is an approximation.