def test_frienemy_safe_region(): ds_test = DS(create_pool_classifiers(), safe_k=3) ds_test.processed_dsel = dsel_processed_ex1 ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 ds_test.neighbors = np.array([0, 1, 2, 6, 7, 8, 14]) result = ds_test._frienemy_pruning() assert np.array_equal(result, np.array([[1, 1, 1]]))
def test_frienemy_not_all_classifiers_crosses(): ds_test = DS(create_pool_classifiers(), safe_k=3) ds_test.processed_dsel = dsel_processed_ex1 ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 ds_test.neighbors = neighbors_ex1[0, :] result = ds_test._frienemy_pruning() assert np.array_equal(result, np.array([[1, 1, 0]]))
def test_frienemy_no_classifier_crosses(): X = X_dsel_ex1 y = y_dsel_ex1 ds_test = DS(create_pool_classifiers()) ds_test.fit(X, y) ds_test.neighbors = neighbors_ex1[0, :] mask = ds_test._frienemy_pruning() assert mask.shape == (1, 3) and np.allclose(mask, 1)
def test_frienemy_all_classifiers_crosses(index): ds_test = DS(create_pool_classifiers()) ds_test.processed_dsel = dsel_processed_all_ones ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 ds_test.neighbors = neighbors_ex1[index, :] result = ds_test._frienemy_pruning() assert result.all() == 1.0
def test_frienemy_no_classifier_crosses(): X = X_dsel_ex1 y = y_dsel_ex1 ds_test = DS(create_pool_classifiers()) ds_test.fit(X, y) ds_test.neighbors = neighbors_ex1[0, :] mask = ds_test._frienemy_pruning() assert mask.size == 3 and mask.all() == 1
def test_frienemy_not_all_classifiers_crosses_batch(): expected = np.array([[1, 1, 0], [0, 1, 0], [1, 1, 1]]) ds_test = DS(create_pool_classifiers(), safe_k=3) ds_test.processed_dsel = dsel_processed_ex1 ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 # passing three samples to compute the DFP at the same time ds_test.neighbors = neighbors_ex1 result = ds_test._frienemy_pruning() assert np.array_equal(result, expected)
def test_label_encoder_only_dsel_allagree(): X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]]) y_dsel_ex1 = np.array(['cat', 'dog', 'plane']) query = np.atleast_2d([[1, 0], [-1, -1]]) ds_test = DS(create_pool_classifiers_dog(), k=2) ds_test.fit(X_dsel_ex1, y_dsel_ex1) ds_test.neighbors = neighbors_ex1[0, :] ds_test.distances = distances_ex1[0, :] predictions = ds_test.predict(query) assert np.array_equal(predictions, ['dog', 'dog'])
def test_predict_proba_instance_called(index): query = np.atleast_2d([1, 1]) ds_test = DS(create_pool_classifiers(), with_IH=True, IH_rate=0.10) ds_test.fit(X_dsel_ex1, y_dsel_ex1) ds_test.neighbors = neighbors_ex1[index, :] ds_test.distances = distances_ex1[index, :] ds_test.predict_proba_instance = MagicMock(return_value=np.atleast_2d([0.25, 0.75])) proba = ds_test.predict_proba(query) assert np.isclose(proba, np.atleast_2d([0.25, 0.75])).all()
def test_DFP_is_used(): query = np.atleast_2d([1, 0]) ds_test = DS(create_pool_classifiers(), DFP=True, safe_k=3) ds_test.fit(X_dsel_ex1, y_dsel_ex1) ds_test.processed_dsel = dsel_processed_ex1 ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 ds_test.neighbors = neighbors_ex1[0, :] ds_test.distances = distances_ex1[0, :] ds_test.classify_with_ds = MagicMock(return_value=0) ds_test.predict(query) assert np.array_equal(ds_test.DFP_mask, np.atleast_2d([1, 1, 0]))
def test_predict_proba_IH_knn(index): query = np.atleast_2d([1, 1]) ds_test = DS(create_pool_classifiers(), with_IH=True, IH_rate=0.5) ds_test.fit(X_dsel_ex1, y_dsel_ex1) ds_test.dsel_scores = dsel_scores_ex1 ds_test.neighbors = neighbors_ex1[index, :] ds_test.distances = distances_ex1[index, :] ds_test.roc_algorithm.predict_proba = MagicMock(return_value=np.atleast_2d([0.45, 0.55])) proba = ds_test.predict_proba(query) assert np.isclose(proba, np.atleast_2d([0.45, 0.55])).all()
def test_frienemy_safe_region_batch(): n_samples = 10 n_classifiers = 3 expected = np.ones((n_samples, n_classifiers)) ds_test = DS(create_pool_classifiers(), safe_k=3) ds_test.processed_dsel = dsel_processed_ex1 ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 ds_test.neighbors = np.tile(np.array([0, 1, 2, 6, 7, 8, 14]), (n_samples, 1)) result = ds_test._frienemy_pruning() assert np.array_equal(result, expected)
def test_label_encoder_only_dsel(): X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]]) y_dsel_ex1 = np.array(['cat', 'dog', 'plane']) query = np.atleast_2d([[1, 0], [-1, -1]]) ds_test = DS(create_pool_classifiers_dog_cat_plane(), k=2) ds_test.fit(X_dsel_ex1, y_dsel_ex1) ds_test.neighbors = neighbors_ex1[0, :] ds_test.distances = distances_ex1[0, :] ds_test.classify_with_ds = Mock() ds_test.classify_with_ds.return_value = [ 1, 0 ] # changed here due to batch processing predictions = ds_test.predict(query) assert np.array_equal(predictions, ['dog', 'cat'])
def test_predict_proba_DFP(): query = np.atleast_2d([1, 1]) ds_test = DS(create_pool_classifiers(), DFP=True, safe_k=3) ds_test.fit(X_dsel_ex1, y_dsel_ex1) # change the state of the system ds_test.processed_dsel = dsel_processed_ex1 ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 ds_test.neighbors = neighbors_ex1[0, :] ds_test.distances = distances_ex1[0, :] ds_test.predict_proba_instance = MagicMock(return_value=np.atleast_2d([0.25, 0.75])) ds_test.predict_proba(query) assert np.array_equal(ds_test.DFP_mask, np.array([1, 1, 0]))
def test_IH_is_used(index, expected): query = np.atleast_2d([1, 0]) ds_test = DS(create_pool_classifiers(), with_IH=True, IH_rate=0.5) ds_test.fit(X_dsel_ex1, y_dsel_ex1) ds_test.processed_dsel = dsel_processed_ex1 ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 ds_test.neighbors = neighbors_ex1[index, :] ds_test.distances = distances_ex1[index, :] predicted = ds_test.predict(query) assert predicted == expected
def test_IH_is_used(): expected = [0, 0, 1] query = np.ones((3, 2)) ds_test = DS(create_pool_classifiers(), with_IH=True, IH_rate=0.5) ds_test.fit(X_dsel_ex1, y_dsel_ex1) ds_test.processed_dsel = dsel_processed_ex1 ds_test.DSEL_target = y_dsel_ex1 ds_test.DSEL_data = X_dsel_ex1 ds_test.neighbors = neighbors_ex1 ds_test.distances = distances_ex1 predicted = ds_test.predict(query) assert np.array_equal(predicted, expected)