def test_preprocessor_error_message(): """Tests whether the preprocessor returns a preprocessor error when there is a problem using the preprocessor """ preprocessor = ArrayIndexer(np.array([[1.2, 3.3], [3.1, 3.2]])) # with tuples X = np.array([[[2, 3], [3, 3]], [[2, 3], [3, 2]]]) # There are less samples than the max index we want to preprocess with pytest.raises(PreprocessorError): preprocess_tuples(X, preprocessor) # with points X = np.array([[1], [2], [3], [3]]) with pytest.raises(PreprocessorError): preprocess_points(X, preprocessor)
def test_preprocessor_error_message(): """Tests whether the preprocessor returns a preprocessor error when there is a problem using the preprocessor """ preprocessor = ArrayIndexer(np.array([[1.2, 3.3], [3.1, 3.2]])) # with tuples X = np.array([[[2, 3], [3, 3]], [[2, 3], [3, 2]]]) # There are less samples than the max index we want to preprocess with pytest.raises(PreprocessorError): preprocess_tuples(X, preprocessor) # with points X = np.array([[1], [2], [3], [3]]) with pytest.raises(PreprocessorError): preprocess_points(X, preprocessor)
def test_preprocess_points_simple_example(): """Test the preprocessor on very simple examples of points to ensure the result is as expected""" array = np.array([1, 2, 4]) def fun(row): return [[1, 1], [3, 3], [4, 4]] expected_result = np.array([[1, 1], [3, 3], [4, 4]]) assert (preprocess_points(array, fun) == expected_result).all()
def test_preprocess_points_simple_example(): """Test the preprocessor on very simple examples of points to ensure the result is as expected""" array = np.array([1, 2, 4]) def fun(row): return [[1, 1], [3, 3], [4, 4]] expected_result = np.array([[1, 1], [3, 3], [4, 4]]) assert (preprocess_points(array, fun) == expected_result).all()
def test_check_classic_invalid_n_samples(estimator, context, load_points, preprocessor): """Checks that the right warning is printed if n_samples is too small""" points = load_points() msg = ("Found array with 2 sample(s) (shape={}) while a minimum of 3 " "is required{}.".format((preprocess_points(points, preprocessor) if preprocessor is not None and points.ndim == 1 else points).shape, context)) with pytest.raises(ValueError) as raised_error: check_input(points, type_of_inputs='classic', preprocessor=preprocessor, ensure_min_samples=3, estimator=estimator) assert str(raised_error.value) == msg
def test_check_classic_invalid_n_samples(estimator, context, load_points, preprocessor): """Checks that the right warning is printed if n_samples is too small""" points = load_points() msg = ("Found array with 2 sample(s) (shape={}) while a minimum of 3 " "is required{}.".format((preprocess_points(points, preprocessor) if preprocessor is not None and points.ndim == 1 else points).shape, context)) with pytest.raises(ValueError) as raised_error: check_input(points, type_of_inputs='classic', preprocessor=preprocessor, ensure_min_samples=3, estimator=estimator) assert str(raised_error.value) == msg