def test_select_none_competent(): knora_e_test = KNORAE() competences = np.zeros(100) selected = knora_e_test.select(competences) expected = np.atleast_2d([True] * 100) assert np.array_equal(expected, selected)
def test_select(index, expected): query = np.atleast_2d([1, 1]) knora_e_test = KNORAE(create_pool_classifiers()) knora_e_test.fit(X_dsel_ex1, y_dsel_ex1) neighbors = neighbors_ex1[index, :].reshape(1, -1) competences = knora_e_test.estimate_competence(query, neighbors) selected = knora_e_test.select(competences) assert np.array_equal(selected, expected)
def test_select(index, expected): query = np.atleast_2d([1, 1]) knora_e_test = KNORAE(create_pool_classifiers()) knora_e_test.fit(X_dsel_ex1, y_dsel_ex1) knora_e_test.DFP_mask = np.ones(knora_e_test.n_classifiers) knora_e_test.neighbors = neighbors_ex1[index, :] knora_e_test.distances = distances_ex1[index, :] competences = knora_e_test.estimate_competence(query) selected = knora_e_test.select(competences) assert selected == expected
def test_select(index, expected, create_pool_classifiers, example_estimate_competence): X, y, neighbors, distances, _, _ = example_estimate_competence knora_e_test = KNORAE(create_pool_classifiers) knora_e_test.fit(X, y) neighbors = neighbors[index, :].reshape(1, -1) distances = distances[index, :].reshape(1, -1) competences = knora_e_test.estimate_competence(neighbors, distances=distances) selected = knora_e_test.select(competences) assert np.array_equal(selected, expected)
def test_select_none_competent(): query = np.atleast_2d([1, 1]) knora_e_test = KNORAE(create_pool_all_agree(2, 100)) knora_e_test.fit(X_dsel_ex1, y_dsel_ex1) knora_e_test.neighbors = neighbors_ex1[0, :] knora_e_test.distances = distances_ex1[0, :] knora_e_test.DFP_mask = np.ones(knora_e_test.n_classifiers) competences = knora_e_test.estimate_competence(query) indices = knora_e_test.select(competences) assert indices == list(range(knora_e_test.n_classifiers))