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)