def test_input_X_1D(): X = np.ones(10) ds_test = DS(create_pool_classifiers()) ds_test.fit(X_dsel_ex1, y_dsel_ex1) with pytest.raises(Warning): ds_test.predict(X)
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_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_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_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_label_encoder_base(): from sklearn.linear_model import LogisticRegression X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]]) y_dsel_ex1 = np.array(['cat', 'dog', 'plane']) x = [[-2, -2], [2, 2]] y = ['cat', 'dog'] pool_classifiers = [LogisticRegression().fit(x, y) for _ in range(2)] query = np.atleast_2d([[1, 0], [-1, -1]]) ds_test = DS(pool_classifiers, k=2) ds_test.fit(X_dsel_ex1, y_dsel_ex1) predictions = ds_test.predict(query) assert np.equal(predictions, ['cat', 'dog'])
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)
def test_not_fitted_ds(): query = np.array([[1.0, 1.0]]) ds_test = DS(create_pool_classifiers()) with pytest.raises(NotFittedError): ds_test.predict(query)
def test_different_input_shape(): query = np.array([[1.0, 1.0, 2.0]]) ds_test = DS(create_pool_classifiers()) ds_test.fit(X_dsel_ex1, y_dsel_ex1) with pytest.raises(ValueError): ds_test.predict(query)
def test_predict_value(query): pool_classifiers = create_classifiers_disagree() ds = DS(pool_classifiers) with pytest.raises(ValueError): ds.predict(query)
def test_bad_input_X(X): ds_test = DS(create_pool_classifiers()) ds_test.fit(X_dsel_ex1, y_dsel_ex1) with pytest.raises(ValueError): ds_test.predict(X)
def test_input_X_3D(): X = np.ones((10, 10, 10)) ds_test = DS(create_pool_classifiers()) ds_test.fit(X_dsel_ex1, y_dsel_ex1) with pytest.raises(ValueError): ds_test.predict(X)