def test_predict_proba_hybrid(create_pool_classifiers): query = np.array([-1, 1]) pool_classifiers = create_pool_classifiers + create_pool_classifiers des_test = BaseDES(pool_classifiers, mode='hybrid') des_test.n_classes_ = 2 selected_indices = [0, 1, 5] competences = np.array([[0.55, 1.0, 0.2, 0.60, 0.75, 0.3]]) expected = np.array([0.5744, 0.4256]) des_test.estimate_competence = MagicMock(return_value=competences) selected_classifiers = np.zeros((1, 6), dtype=bool) selected_classifiers[0, selected_indices] = 1 des_test.select = MagicMock(return_value=selected_classifiers) predictions = [] probabilities = [] for clf in des_test.pool_classifiers: predictions.append(clf.predict(query)[0]) probabilities.append(clf.predict_proba(query)[0]) query = np.atleast_2d(query) predictions = np.atleast_2d(predictions) probabilities = np.array(probabilities) probabilities = np.expand_dims(probabilities, axis=0) predicted_proba = des_test.predict_proba_with_ds(query, predictions, probabilities) assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_weighting_hard_voting(create_pool_classifiers): query = np.array([[-1, 1]]) expected = np.array([0.4117, 0.5883]) competences = np.array([[0.5, 1.0, 0.2]]) predictions = np.array([[0, 1, 0]]) pool_classifiers = create_pool_classifiers des_test = BaseDES(pool_classifiers, mode='weighting', voting='hard') des_test.estimate_competence = MagicMock(return_value=competences) des_test.n_classes_ = 2 predicted_proba = des_test.predict_proba_with_ds(predictions) assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_hybrid_hard_voting(create_pool_classifiers): query = np.array([[-1, 1]]) expected = np.array([0.4594, 0.5405]) competences = np.array([[0.55, 1.0, 0.2, 0.60, 0.75, 0.3]]) predictions = np.array([[0, 1, 0, 0, 1, 0]]) selected_indices = np.array([[True, True, False, False, False, True]]) pool_classifiers = create_pool_classifiers + create_pool_classifiers des_test = BaseDES(pool_classifiers, mode='hybrid', voting='hard') des_test.n_classes_ = 2 des_test.estimate_competence = MagicMock(return_value=competences) des_test.select = MagicMock(return_value=selected_indices) predicted_proba = des_test.predict_proba_with_ds(predictions) assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_predict_proba_weighting_soft_voting(create_pool_classifiers): query = np.array([[-1, 1]]) expected = np.array([0.5769, 0.4231]) competences = np.array([[0.5, 1.0, 0.2]]) predictions = np.array([[0, 1, 0]]) probabilities = np.array([[[0.5, 0.5], [1., 0.], [0.33, 0.67]]]) pool_classifiers = create_pool_classifiers des_test = BaseDES(pool_classifiers, mode='weighting', voting='soft') des_test.estimate_competence = MagicMock(return_value=competences) des_test.n_classes_ = 2 predicted_proba = des_test.predict_proba_with_ds(predictions, probabilities) assert np.isclose(predicted_proba, expected, atol=0.01).all()
def test_classify_instance_weighting(create_pool_classifiers): query = np.array([-1, 1]) pool_classifiers = create_pool_classifiers + create_pool_classifiers des_test = BaseDES(pool_classifiers, mode='weighting') des_test.classes_ = np.array([0, 1]) des_test.n_classes_ = 2 competences = np.array([0.55, 1.0, 0.2, 0.60, 0.75, 0.3]) des_test.estimate_competence = MagicMock(return_value=competences) predictions = [] for clf in des_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted_label = des_test.classify_with_ds(query, np.array(predictions)) assert predicted_label == 1.0
def test_classify_instance_weighting(create_pool_classifiers): n_samples = 3 query = np.ones((n_samples, 2)) pool_classifiers = create_pool_classifiers + create_pool_classifiers des_test = BaseDES(pool_classifiers, mode='weighting') des_test.classes_ = np.array([0, 1]) des_test.n_classes_ = 2 competences = np.tile([0.55, 1.0, 0.2, 0.60, 0.75, 0.3], (3, 1)) des_test.estimate_competence = MagicMock(return_value=competences) predictions = [] for clf in des_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted_label = des_test.classify_with_ds(np.tile(predictions, (3, 1))) assert np.allclose(predicted_label, 1) and predicted_label.size == 3
def test_classify_instance_hybrid(create_pool_classifiers): query = np.array([-1, 1]) expected = 1 pool_classifiers = create_pool_classifiers + create_pool_classifiers des_test = BaseDES(pool_classifiers, mode='hybrid') des_test.classes_ = np.array([0, 1]) des_test.n_classes_ = 2 selected_indices = np.array([[True, True, False, False, False, True]]) competences = np.array([[0.55, 1.0, 0.2, 0.60, 0.75, 0.3]]) des_test.estimate_competence = MagicMock(return_value=competences) des_test.select = MagicMock(return_value=selected_indices) predictions = [] for clf in des_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted_label = des_test.classify_with_ds(query, np.array(predictions)) assert expected == predicted_label
def test_classify_instance_hybrid_batch(): query = np.ones((3, 2)) expected = 1 pool_classifiers = create_pool_classifiers() + create_pool_classifiers() des_test = BaseDES(pool_classifiers, mode='hybrid') des_test.classes_ = np.array([0, 1]) des_test.n_classes_ = 2 selected_indices = np.tile([True, True, False, False, False, True], (3, 1)) competences = np.tile([0.55, 1.0, 0.2, 0.60, 0.75, 0.3], (3, 1)) des_test.estimate_competence = MagicMock(return_value=competences) des_test.select = MagicMock(return_value=selected_indices) predictions = [] for clf in des_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted_label = des_test.classify_with_ds(query, np.tile(predictions, (3, 1))) assert np.allclose(predicted_label, expected)
def test_predict_proba_weighting(create_pool_classifiers): query = np.array([-1, 1]) pool_classifiers = create_pool_classifiers des_test = BaseDES(pool_classifiers, mode='weighting') competences = np.array([[0.5, 1.0, 0.2]]) des_test.estimate_competence = MagicMock(return_value=competences) des_test.n_classes_ = 2 expected = np.array([0.5769, 0.4231]) predictions = [] probabilities = [] for clf in des_test.pool_classifiers: predictions.append(clf.predict(query)[0]) probabilities.append(clf.predict_proba(query)[0]) query = np.atleast_2d(query) predictions = np.atleast_2d(predictions) probabilities = np.array(probabilities) probabilities = np.expand_dims(probabilities, axis=0) predicted_proba = des_test.predict_proba_with_ds(query, predictions, probabilities) assert np.isclose(predicted_proba, expected, atol=0.01).all()