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
