def test_original_class_probability():
predictions = np.array([0.1, 0.5, 0.7, 10.0, 0.4])
c = criteria.OriginalClassProbability(0.1)
assert c.is_adversarial(predictions, 0)
assert c.is_adversarial(predictions, 1)
assert c.is_adversarial(predictions, 2)
assert not c.is_adversarial(predictions, 3)
assert c.is_adversarial(predictions, 4)
assert "0.1" in c.name()
def test_combined_criteria():
c1 = criteria.Misclassification()
c2 = criteria.OriginalClassProbability(0.2)
c3 = c1 & c2
probabilities = np.array([0.09, 0.11, 0.39, 0.41])
predictions = np.log(probabilities)
for i in range(len(predictions)):
b1 = c1.is_adversarial(predictions, i)
b2 = c2.is_adversarial(predictions, i)
b3 = c3.is_adversarial(predictions, i)
assert (b1 and b2) == b3
assert c1.name() == "Top1Misclassification"
assert c2.name() == "OriginalClassProbability-0.2000"
assert c3.name() == c2.name() + "__" + c1.name()