def test_estimate_competence(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) assert np.isclose(competences, expected, atol=0.01).all()
def test_estimate_competence_batch(): query = np.ones((3, 2)) expected = np.array([[1.0, 0.0, 1.0], [2.0, 0.0, 2.0], [0.0, 3.0, 0.0]]) 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 knora_e_test.distances = distances_ex1 competences = knora_e_test.estimate_competence(query) assert np.allclose(competences, 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_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))