def test_multiclass_prediction(self):
classes = [['a', 'b', 'c']]
predicted_probabilities = [[0, 0, 0.99], [0, 0.99, 0], [0.99, 0, 0],
[0, 0.99, 0], [0, 0, 0.99]]
predicted_indexes = [2, 1, 0, 1, 2]
expected_result = ['c', 'b', 'a', 'b', 'c']
automl_mock = unittest.mock.Mock()
automl_mock.predict.return_value = np.array(predicted_probabilities)
classifier = AutoMLClassifier(automl_mock)
classifier._classes = [np.array(classes)]
classifier._n_outputs = 1
classifier._n_classes = np.array([3])
actual_result = classifier.predict([None] * len(predicted_indexes))
np.testing.assert_array_equal(expected_result, actual_result)
def test_multilabel_prediction(self):
classes = [['a', 'b', 'c'], [13, 17]]
predicted_probabilities = [[[0, 0, 0.99], [0.99, 0]],
[[0, 0.99, 0], [0.99, 0]],
[[0.99, 0, 0], [0, 0.99]],
[[0, 0.99, 0], [0, 0.99]],
[[0, 0, 0.99], [0, 0.99]]]
predicted_indexes = [[2, 0], [1, 0], [0, 1], [1, 1], [2, 1]]
expected_result = np.array([['c', 13], ['b', 13], ['a', 17], ['b', 17], ['c', 17]], dtype=object)
automl_mock = unittest.mock.Mock()
automl_mock.predict.return_value = np.matrix(predicted_probabilities)
classifier = AutoMLClassifier(automl_mock)
classifier._classes = list(map(np.array, classes))
classifier._n_outputs = 2
classifier._n_classes = np.array([3, 2])
actual_result = classifier.predict([None] * len(predicted_indexes))
np.testing.assert_array_equal(expected_result, actual_result)
def test_multiclass_prediction(self):
classes = [['a', 'b', 'c']]
predicted_probabilities = [[0, 0, 0.99], [0, 0.99, 0], [0.99, 0, 0],
[0, 0.99, 0], [0, 0, 0.99]]
predicted_indexes = [2, 1, 0, 1, 2]
expected_result = ['c', 'b', 'a', 'b', 'c']
automl_mock = mock.Mock()
automl_mock.predict.return_value = np.array(predicted_probabilities)
classifier = AutoMLClassifier(automl_mock)
classifier._classes = [np.array(classes)]
classifier._n_outputs = 1
classifier._n_classes = np.array([3])
actual_result = classifier.predict([None] * len(predicted_indexes))
np.testing.assert_array_equal(expected_result, actual_result)
def test_multilabel_prediction(self):
classes = [[1, 2], [13, 17]]
predicted_probabilities = [[0.99, 0], [0.99, 0], [0, 0.99],
[0.99, 0.99], [0.99, 0.99]]
predicted_indexes = np.array([[1, 0], [1, 0], [0, 1], [1, 1], [1, 1]])
expected_result = np.array([[2, 13], [2, 13], [1, 17], [2, 17],
[2, 17]])
automl_mock = unittest.mock.Mock()
automl_mock.predict.return_value = np.array(predicted_probabilities)
classifier = AutoMLClassifier(automl_mock)
classifier._classes = list(map(np.array, classes))
classifier._n_outputs = 2
classifier._n_classes = np.array([3, 2])
actual_result = classifier.predict([None] * len(predicted_indexes))
np.testing.assert_array_equal(expected_result, actual_result)