"""Tests for functions in np_utils.py.
"""
import numpy as np
import pytest
from keras.utils import to_categorical


def test_to_categorical():
    num_classes = 5
    shapes = [(3,), (4, 3), (5, 4, 3), (3, 1), (3, 2, 1)]
    expected_shapes = [(3, num_classes),
                       (4, 3, num_classes),
                       (5, 4, 3, num_classes),
                       (3, num_classes)]
    labels = [np.random.randint(0, num_classes, shape) for shape in shapes]
    one_hots = [to_categorical(label, num_classes) for label in labels]
    for label, one_hot, expected_shape in zip(labels,
                                              one_hots,
                                              expected_shapes):
        # Check shape
        assert one_hot.shape == expected_shape
        # Make sure there are only 0s and 1s
        assert np.array_equal(one_hot, one_hot.astype(bool))
        # Make sure there is exactly one 1 in a row
        assert np.all(one_hot.sum(axis=-1) == 1)
        # Get original labels back from one hots
        assert np.all(np.argmax(one_hot, -1).reshape(label.shape) == label)


if __name__ == '__main__':
    pytest.main([__file__])

"""Tests for functions in np_utils.py.
"""
import numpy as np
import pytest
from keras.utils import to_categorical


def test_to_categorical():
    num_classes = 5
    shapes = [(3,), (4, 3), (5, 4, 3), (3, 1), (3, 2, 1)]
    expected_shapes = [(3, num_classes),
                       (4, 3, num_classes),
                       (5, 4, 3, num_classes),
                       (3, num_classes),
                       (3, 2, num_classes)]
    labels = [np.random.randint(0, num_classes, shape) for shape in shapes]
    one_hots = [to_categorical(label, num_classes) for label in labels]
    for label, one_hot, expected_shape in zip(labels,
                                              one_hots,
                                              expected_shapes):
        # Check shape
        assert one_hot.shape == expected_shape
        # Make sure there are only 0s and 1s
        assert np.array_equal(one_hot, one_hot.astype(bool))
        # Make sure there is exactly one 1 in a row
        assert np.all(one_hot.sum(axis=-1) == 1)
        # Get original labels back from one hots
        assert np.all(np.argmax(one_hot, -1).reshape(label.shape) == label)


if __name__ == '__main__':
    pytest.main([__file__])

add missing test (#8507)