def test_poison_ensemble(self):
classifier_1 = DummyClassifier()
attack_1 = DummyPoisonAttack()
classifier_2 = DummyClassifier()
attack_2 = DummyPoisonAttack()
attack_pairs = AttackPairs()
attack_pairs.add(classifier_1, attack_1, 1)
attack_pairs.add(classifier_2, attack_2, 1)
attack_pairs.fit_all(X, Y)
ensemble = PoisonEnsemble(attack_pairs, X)
defender = DummyClassifier()
ensemble.poison(defender)
def test_fit_predict_all(self):
"""Test that you can fit and predict on all classifiers"""
classifier_1 = DummyClassifier()
attack_1 = DummyPoisonAttack()
classifier_2 = DummyClassifier()
attack_2 = DummyPoisonAttack()
attack_pairs = AttackPairs()
attack_pairs.add(classifier_1, attack_1, 1)
attack_pairs.add(classifier_2, attack_2, 1)
attack_pairs.fit_all(X, Y)
y_out = attack_pairs.predict_all(X)
assert isinstance(y_out, np.ndarray)
assert y_out.shape == (X.shape[0], 2)
def test_classifier_output(self):
"""Test that the output is a numpy array of the correct size.
"""
model = DummyClassifier()
model.fit(X, Y)
model.partial_fit(X, Y)
assert isinstance(model.predict(X_TEST_1), np.ndarray)
assert isinstance(model.predict(X_TEST_2), np.ndarray)
assert model.predict(X_TEST_1).shape == (X_TEST_1.shape[0],)
assert model.predict(X_TEST_2).shape == (X_TEST_2.shape[0],)
def test_get_attack_point(self):
"""Test that you can get an attack point from a particular attack"""
classifier_1 = DummyClassifier()
attack_1 = DummyPoisonAttack()
classifier_2 = DummyClassifier()
attack_2 = DummyPoisonAttack()
attack_pairs = AttackPairs()
attack_pairs.add(classifier_1, attack_1, 1)
attack_pairs.add(classifier_2, attack_2, 1)
attack_pairs.fit_all(X, Y)
(x_attack_1, y_attack_1) = attack_pairs.get_attack_point(0)
assert isinstance(x_attack_1, np.ndarray)
assert x_attack_1.shape == (1, 2)
(x_attack_2, y_attack_2) = attack_pairs.get_attack_point(1)
assert isinstance(x_attack_1, np.ndarray)
assert x_attack_1.shape == (1, 2)
def test_get_beliefs(self):
"""Test that you can get the current beliefs as a numpy array"""
classifier = DummyClassifier()
attack = DummyPoisonAttack()
attack_pairs = AttackPairs()
assert np.array_equal(attack_pairs.get_beliefs(), np.zeros((0, )))
attack_pairs.add(classifier, attack, 1)
assert np.array_equal(attack_pairs.get_beliefs(), np.array([1]))
attack_pairs.add(classifier, attack, 1)
assert np.array_equal(attack_pairs.get_beliefs(), np.array([1, 1]))
def test_add_attack(self):
"""Test that an attack can be added"""
classifier = DummyClassifier()
attack = DummyPoisonAttack()
attack_pairs = AttackPairs()
attack_pairs.add(classifier, attack, 1)
assert attack_pairs.attack_pairs == [{
'classifier': classifier,
'attack': attack,
'belief': 1
}]
def test_normalize_belief(self):
"""Test that the beliefs can be normalized"""
classifier = DummyClassifier()
attack = DummyPoisonAttack()
attack_pairs = AttackPairs()
attack_pairs.add(classifier, attack, 1)
attack_pairs.add(classifier, attack, 1)
attack_pairs.add(classifier, attack, 1)
attack_pairs.normalize_beliefs()
assert np.array_equal(attack_pairs.get_beliefs(),
np.array([1.0, 1.0, 1.0]) / 3)
rand_beliefs_3 = np.random.rand(3)
attack_pairs.set_beliefs(rand_beliefs_3)
attack_pairs.normalize_beliefs()
assert np.array_equal(attack_pairs.get_beliefs(),
rand_beliefs_3 / np.sum(rand_beliefs_3))
def test_set_beliefs(self):
"""Test that you can set the current beliefs as a numpy array"""
classifier = DummyClassifier()
attack = DummyPoisonAttack()
attack_pairs = AttackPairs()
attack_pairs.add(classifier, attack, 1)
rand_beliefs_1 = np.random.rand(1)
attack_pairs.set_beliefs(rand_beliefs_1)
assert np.array_equal(attack_pairs.get_beliefs(), rand_beliefs_1)
attack_pairs.add(classifier, attack, 1)
rand_beliefs_2 = np.random.rand(2)
attack_pairs.set_beliefs(rand_beliefs_2)
assert np.array_equal(attack_pairs.get_beliefs(), rand_beliefs_2)
attack_pairs.add(classifier, attack, 1)
rand_beliefs_3 = np.random.rand(3)
attack_pairs.set_beliefs(rand_beliefs_3)
assert np.array_equal(attack_pairs.get_beliefs(), rand_beliefs_3)