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(): 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) knora_u_test.DFP_mask = np.ones((3, knora_u_test.n_classifiers)) knora_u_test.neighbors = neighbors_ex1 knora_u_test.distances = distances_ex1 competences = knora_u_test.estimate_competence(query) assert np.allclose(competences, expected, atol=0.01)
def test_classify(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, :] prediction = knora_u_test.classify_instance(query) assert prediction == expected
def test_classify(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, :] predictions = [] for clf in knora_u_test.pool_classifiers: predictions.append(clf.predict(query)[0]) prediction = knora_u_test.classify_instance(query, np.array(predictions)) assert prediction == expected