def test_weights_zero(): query = np.atleast_2d([1, 1]) knora_u_test = KNORAU(create_pool_classifiers()) knora_u_test.estimate_competence = MagicMock(return_value=np.zeros(3)) result = knora_u_test.select(query) assert np.array_equal(result, np.array([0, 1, 0]))
def test_estimate_competence_batch(): query = np.ones((3, 2)) expected = np.array([[4.0, 3.0, 4.0], [5.0, 2.0, 5.0], [2.0, 5.0, 2.0]]) knora_u_test = KNORAU(create_pool_classifiers()) knora_u_test.fit(X_dsel_ex1, y_dsel_ex1) neighbors = neighbors_ex1 competences = knora_u_test.estimate_competence(query, neighbors) assert np.allclose(competences, expected, atol=0.01)
def test_estimate_competence(index, expected): query = np.atleast_2d([1, 1]) knora_u_test = KNORAU(create_pool_classifiers()) knora_u_test.fit(X_dsel_ex1, y_dsel_ex1) knora_u_test.DFP_mask = np.ones(knora_u_test.n_classifiers) knora_u_test.neighbors = neighbors_ex1[index, :] knora_u_test.distances = distances_ex1[index, :] competences = knora_u_test.estimate_competence(query) assert np.isclose(competences, expected, atol=0.01).all()
def test_estimate_competence_batch(example_estimate_competence, create_pool_classifiers): X, y, neighbors = example_estimate_competence[0:3] expected = np.array([[4.0, 3.0, 4.0], [5.0, 2.0, 5.0], [2.0, 5.0, 2.0]]) knora_u_test = KNORAU(create_pool_classifiers) knora_u_test.fit(X, y) competences = knora_u_test.estimate_competence(neighbors) assert np.allclose(competences, expected, atol=0.01)