def test_select_less_diverse(): """ Test case: 10 base classifiers; select 5 based on accuracy, then the 3 less diverse accuracies (/10): 4 6 1 2 9 8 7 9 3 2 (should select indices_ 1, 4, 5, 6, 7) diversity: 0 8 0 0 1 6 7 2 0 0 (should select indices_ 4, 5, 7 as most diverse) """ pool_classifiers = [create_base_classifier(1) for _ in range(10)] accuracies = np.array([[4, 6, 1, 2, 9, 8, 7, 9, 3, 2]]) / 10. diversity = np.array([[0, 8, 0, 0, 1, 6, 7, 2, 0, 0]]) target = DESKNN(pool_classifiers, k=7, pct_accuracy=5. / 10, pct_diversity=3. / 10, more_diverse=False) target.N_ = 5 target.J_ = 3 selected_classifiers = target.select(accuracies, diversity) expected = np.array([[4, 5, 7]]) assert np.array_equal(np.unique(selected_classifiers), np.unique(expected))
def test_select_batch(): """ Test case: 10 base classifiers; select 5 based on accuracy, then the 3 most diverse. accuracies (/10): 4 6 1 2 9 8 7 9 3 2 (should select indices_ 1, 4, 5, 6, 7) diversity: 0 8 0 0 1 6 7 2 0 0 (should select indices_ 1, 5, 6 as most diverse) """ n_samples = 10 pool_classifiers = [create_base_classifier(1) for _ in range(10)] accuracies = np.tile([4, 6, 1, 2, 9, 8, 7, 9, 3, 2], (n_samples, 1)) / 10. diversity = np.tile([0, 8, 0, 0, 1, 6, 7, 2, 0, 0], (n_samples, 1)) target = DESKNN(pool_classifiers, k=7, pct_accuracy=5. / 10, pct_diversity=3. / 10) target.N_ = 5 target.J_ = 3 selected_classifiers = target.select(accuracies, diversity) expected = np.tile([1, 5, 6], (n_samples, 1)) assert np.array_equal(np.unique(selected_classifiers), np.unique(expected))
def test_classify_instance(): query = np.atleast_2d([1, -1]) des_knn_test = DESKNN(create_pool_classifiers() * 4, k=2) des_knn_test.select = MagicMock(return_value=[0, 1, 2, 3, 5, 6, 7, 9]) predicted = des_knn_test.classify_instance(query) assert predicted == 0
def test_classify_instance(): query = np.atleast_2d([1, -1]) des_knn_test = DESKNN(create_pool_classifiers() * 4, k=2) des_knn_test.select = MagicMock(return_value=[0, 1, 2, 3, 5, 6, 7, 9]) predictions = [] for clf in des_knn_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted = des_knn_test.classify_instance(query, predictions=np.array(predictions)) assert predicted == 0
def test_classify_with_ds_single_sample(): query = np.ones(2) predictions = np.array([0, 1, 0]) clf1 = create_base_classifier(np.array([1, 0, 1, 0, 0, 0, 0])) clf2 = create_base_classifier(np.array([1, 0, 0, 0, 1, 0, 0])) clf3 = create_base_classifier(np.array([0, 0, 1, 0, 1, 1, 0])) pool_classifiers = [clf1, clf2, clf3] desknn_test = DESKNN(pool_classifiers) desknn_test.estimate_competence = MagicMock( return_value=(np.ones(3), np.ones(3))) desknn_test.select = MagicMock(return_value=np.array([[0, 2]])) result = desknn_test.classify_with_ds(query, predictions) assert np.allclose(result, 0)
def test_select(): """ Test case: 10 base classifiers; select 5 based on accuracy, then the 3 most diverse accuracies (/10): 4 6 1 2 9 8 7 9 3 2 (should select indices 1, 4, 5, 6, 7) diversity: 0 8 0 0 1 6 7 2 0 0 (should select indices 1, 5, 6 as most diverse) """ pool_classifiers = [create_base_classifier(1) for _ in range(10)] accuracies = np.array([4, 6, 1, 2, 9, 8, 7, 9, 3, 2]) / 10. diversity = np.array([0, 8, 0, 0, 1, 6, 7, 2, 0, 0]) target = DESKNN(pool_classifiers, k=7, pct_accuracy=5./10, pct_diversity=3./10) target.estimate_competence = lambda x: (accuracies, diversity) selected_indices = target.select(2) assert set(selected_indices) == {1, 5, 6}