def test_relabel():
""" relabel() works with multi-class labels.
"""
nb_classes = 3
inputs = np.array([
[True, False, False],
[False, True, False],
[True, False, True],
])
expected_0 = np.array([True, False, True])
expected_1 = np.array([False, True, False])
expected_2 = np.array([False, False, True])
assert np.array_equal(relabel(inputs, 0, nb_classes), expected_0)
assert np.array_equal(relabel(inputs, 1, nb_classes), expected_1)
assert np.array_equal(relabel(inputs, 2, nb_classes), expected_2)
def test_relabel():
""" relabel() works with multi-class labels.
"""
nb_classes = 3
inputs = np.array([
[True, False, False],
[False, True, False],
[True, False, True],
])
expected_0 = np.array([True, False, True])
expected_1 = np.array([False, True, False])
expected_2 = np.array([False, False, True])
assert np.array_equal(relabel(inputs, 0, nb_classes), expected_0)
assert np.array_equal(relabel(inputs, 1, nb_classes), expected_1)
assert np.array_equal(relabel(inputs, 2, nb_classes), expected_2)
def test_relabel_binary():
""" relabel() works with binary classification (no changes to labels)
"""
nb_classes = 2
inputs = np.array([True, False, False])
assert np.array_equal(relabel(inputs, 0, nb_classes), inputs)
def test_relabel_binary():
""" relabel() works with binary classification (no changes to labels)
"""
nb_classes = 2
inputs = np.array([True, False, False])
assert np.array_equal(relabel(inputs, 0, nb_classes), inputs)
