### Merge branch...

```Merge branch '27-far-and-frr-thresholds-are-computed-even-when-there-is-no-data-support' of https://gitlab.idiap.ch/bob/bob.measure into 27-far-and-frr-thresholds-are-computed-even-when-there-is-no-data-support

Conflicts:
bob/measure/cpp/error.cpp
bob/measure/plot.py
bob/measure/test_error.py```
parents 8d1dd320 549f9f6c
 ... ... @@ -108,6 +108,30 @@ def test_nan_for_uncomputable_thresholds(): assert math.isnan(frr_threshold(negatives, positives, 0.09)) def test_nan_for_uncomputable_thresholds(): # in some cases, we cannot compute an FAR or FRR threshold, e.g., when we have too little data or too many equal scores # in these cases, the methods should return NaN from . import far_threshold, frr_threshold # case 1: several scores are identical positives = [0., 0., 0., 0., 0.1, 0.2, 0.3, 0.4, 0.5] negatives = [0.5, 0.6, 0.7, 0.8, 0.9, 1., 1., 1., 1.] # test that reasonable thresholds for reachable data points are provided assert far_threshold(negatives, positives, 0.5) == 0.95, far_threshold(negatives, positives, 0.5) assert frr_threshold(negatives, positives, 0.5) == 0.05, frr_threshold(negatives, positives, 0.5) assert math.isnan(far_threshold(negatives, positives, 0.4)) assert math.isnan(frr_threshold(negatives, positives, 0.4)) # case 2: too few scores for the desired threshold positives = numpy.arange(10.) negatives = numpy.arange(10.) assert math.isnan(far_threshold(negatives, positives, 0.09)) assert math.isnan(frr_threshold(negatives, positives, 0.09)) def test_indexing(): from . import correctly_classified_positives, correctly_classified_negatives ... ...
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