def test_select():
meta_test = METADES()
competences = np.asarray(
[0.8, 0.6, 0.7, 0.2, 0.3, 0.4, 0.6, 0.1, 1.0, 0.98])
expected = np.asarray(
[True, True, True, False, False, False, True, False, True, True])
selected_matrix = meta_test.select(competences)
assert np.array_equal(selected_matrix, expected.reshape(1, -1))
def test_select_batch():
meta_test = METADES()
competences = np.tile(
np.array([0.8, 0.6, 0.7, 0.2, 0.3, 0.4, 0.6, 0.1, 1.0, 0.98]), (10, 1))
expected = np.tile(
[True, True, True, False, False, False, True, False, True, True],
(10, 1))
selected_matrix = meta_test.select(competences)
assert np.array_equal(selected_matrix, expected)
def test_select_no_competent_classifiers():
meta_test = METADES(create_pool_classifiers())
competences = np.zeros(meta_test.n_classifiers)
indices = meta_test.select(competences)
assert indices == list(range(meta_test.n_classifiers))
def test_select():
meta_test = METADES(create_pool_classifiers())
competences = np.array([0.8, 0.6, 0.7, 0.2, 0.3, 0.4, 0.6, 0.1, 1.0, 0.98])
indices = meta_test.select(competences)
assert set(indices) == {0, 1, 2, 6, 8, 9}
def test_select_no_competent_classifiers_batch():
meta_test = METADES()
meta_test.n_classifiers_ = 3
competences = np.zeros((10, meta_test.n_classifiers_))
selected_matrix = meta_test.select(competences)
assert np.all(selected_matrix)
