def test_predict_proba_instance(): query = np.array([-1, 1]) n_classifiers = 3 pool_classifiers = create_pool_classifiers() dcs_test = BaseDCS(pool_classifiers) dcs_test.n_classes_ = 2 competences = np.random.rand(n_classifiers) dcs_test.estimate_competence = MagicMock(return_value=competences) expected = pool_classifiers[np.argmax(competences)].predict_proba(query) predictions = [] probabilities = [] for clf in dcs_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 = dcs_test.predict_proba_with_ds(query, predictions, probabilities) assert np.array_equal(predicted_proba, expected)
def test_classify_instance_all(competences, expected): query = np.array([-1, 1]) pool_classifiers = create_pool_classifiers() dcs_test = BaseDCS(pool_classifiers, selection_method='all') dcs_test.estimate_competence = MagicMock(return_value=np.array(competences)) predictions = [] for clf in dcs_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted_label = dcs_test.classify_with_ds(query, np.array(predictions)) assert predicted_label == expected
def test_classify_instance_all_batch(): competences = np.array([[0.6, 0.2, 0.6], [0.5, 0.8, 0.5]]) expected = [0, 1] n_samples = 2 query = np.ones((n_samples, 2)) pool_classifiers = create_pool_classifiers() dcs_test = BaseDCS(pool_classifiers, selection_method='all') dcs_test.estimate_competence = MagicMock(return_value=np.array(competences)) predictions = [] for clf in dcs_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted_label = dcs_test.classify_with_ds(query, np.tile(predictions, (n_samples, 1))) assert np.array_equal(predicted_label, expected)
def test_classify_instance(): query = np.array([-1, 1]) n_classifiers = 3 pool_classifiers = create_pool_classifiers() dcs_test = BaseDCS(pool_classifiers) competences = np.random.rand(n_classifiers) dcs_test.estimate_competence = MagicMock(return_value=competences) expected = pool_classifiers[np.argmax(competences)].predict(query)[0] predictions = [] for clf in dcs_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted_label = dcs_test.classify_with_ds(query, np.array(predictions)) assert predicted_label == expected
def test_classify_instance_batch(): n_samples = 3 n_classifiers = 3 query = np.ones((n_samples, 2)) pool_classifiers = create_pool_classifiers() dcs_test = BaseDCS(pool_classifiers) competences = np.random.rand(n_samples, n_classifiers) dcs_test.estimate_competence = MagicMock(return_value=competences) expected = [] for ind in range(n_samples): expected.append(pool_classifiers[np.argmax(competences[ind, :])].predict(query)[0]) predictions = [] for clf in dcs_test.pool_classifiers: predictions.append(clf.predict(query)[0]) predicted_label = dcs_test.classify_with_ds(query, np.tile(predictions, (3, 1))) assert np.array_equal(predicted_label, expected)
def test_predict_proba_instance_all(competences, expected): query = np.array([-1, 1]) pool_classifiers = create_pool_classifiers() dcs_test = BaseDCS(pool_classifiers, selection_method='all') dcs_test.n_classes_ = 2 dcs_test.estimate_competence = MagicMock(return_value=np.array(competences)) predictions = [] probabilities = [] for clf in dcs_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 = dcs_test.predict_proba_with_ds(query, predictions, probabilities) assert np.isclose(predicted_proba, expected).all()