def validate_feature_api(
feature: Feature,
X_df: pd.DataFrame,
y_df: Union[pd.DataFrame, pd.Series],
subsample: bool,
log_advice: bool = False,
) -> bool:
logger.debug(f'Validating feature {feature!r}')
if subsample:
X_df, y_df = subsample_data_for_validation(X_df, y_df)
valid, failures, advice = check_from_class(FeatureApiCheck, feature, X_df,
y_df)
if valid:
logger.info('Feature is valid')
else:
if log_advice:
logger.info(
'Feature is NOT valid; here is some advice for resolving the '
'feature API issues.')
for failure, advice_item in zip(failures, advice):
logger.info(f'{failure}: {advice_item}')
else:
logger.info(f'Feature is NOT valid; failures were {failures}')
return valid
def test_producing_missing_values_fails(self):
assert has_nans(self.X)
feature = Feature(input='size', transformer=IdentityTransformer())
valid, failures = check_from_class(FeatureApiCheck, feature, self.X,
self.y)
self.assertFalse(valid)
self.assertIn(NoMissingValuesCheck.__name__, failures)
def test_bad_feature_transform_errors(self):
# transformer throws errors
feature = Feature(input='size',
transformer=FragileTransformer((lambda x: True, ),
(RuntimeError, )))
valid, failures = check_from_class(FeatureApiCheck, feature, self.X,
self.y)
self.assertFalse(valid)
self.assertIn(CanTransformCheck.__name__, failures)
def test_producing_missing_values_fails(sample_data):
assert has_nans(sample_data.X)
feature = Feature(
input='size',
transformer=IdentityTransformer()
)
valid, failures, advice = check_from_class(
FeatureApiCheck, feature, sample_data.X, sample_data.y)
assert not valid
assert NoMissingValuesCheck.__name__ in failures
def test_bad_feature_input(sample_data):
# bad input
feature = Feature(
input=3,
transformer=SimpleImputer(),
)
valid, failures, advice = check_from_class(
FeatureApiCheck, feature, sample_data.X, sample_data.y)
assert not valid
assert HasCorrectInputTypeCheck.__name__ in failures
def test_good_feature(sample_data):
feature = Feature(
input='size',
transformer=SimpleImputer(),
)
valid, failures, advice = check_from_class(
FeatureApiCheck, feature, sample_data.X, sample_data.y)
assert valid
assert len(failures) == 0
def test_bad_feature_input(self):
# bad input
feature = Feature(
input=3,
transformer=SimpleImputer(),
)
valid, failures = check_from_class(
FeatureApiCheck, feature, self.X, self.y)
self.assertFalse(valid)
self.assertIn(HasCorrectInputTypeCheck.__name__, failures)
def test_good_feature(self):
feature = Feature(
input='size',
transformer=SimpleImputer(),
)
valid, failures = check_from_class(
FeatureApiCheck, feature, self.X, self.y)
self.assertTrue(valid)
self.assertEqual(len(failures), 0)
def test_bad_feature_transform_errors(sample_data):
# transformer throws errors
feature = Feature(
input='size',
transformer=FragileTransformer(
(lambda x: True, ), (RuntimeError, ))
)
valid, failures, advice = check_from_class(
FeatureApiCheck, feature, sample_data.X, sample_data.y)
assert not valid
assert CanTransformCheck.__name__ in failures
def validate_feature_api(feature, X, y, subsample=False):
logger.debug('Validating feature {feature!r}'.format(feature=feature))
if subsample:
X, y = subsample_data_for_validation(X, y)
valid, failures = check_from_class(FeatureApiCheck, feature, X, y)
if valid:
logger.info('Feature is valid')
else:
logger.info('Feature is NOT valid; failures were {failures}'.format(
failures=failures))
return valid
def test_bad_feature_deepcopy_fails(sample_data):
class _CopyFailsTransformer(IdentityTransformer):
def __deepcopy__(self, memo):
raise RuntimeError
feature = Feature(
input='size',
transformer=_CopyFailsTransformer(),
)
valid, failures, advice = check_from_class(
FeatureApiCheck, feature, sample_data.X, sample_data.y)
assert not valid
assert CanDeepcopyCheck.__name__ in failures
def test_bad_feature_deepcopy_fails(self):
class _CopyFailsTransformer(IdentityTransformer):
def __deepcopy__(self, memo):
raise RuntimeError
feature = Feature(
input='size',
transformer=_CopyFailsTransformer(),
)
valid, failures = check_from_class(
FeatureApiCheck, feature, self.X, self.y)
self.assertFalse(valid)
self.assertIn(CanDeepcopyCheck.__name__, failures)
def test_bad_feature_wrong_transform_length(sample_data):
class _WrongLengthTransformer(BaseTransformer):
def transform(self, X, **transform_kwargs):
new_shape = list(X.shape)
new_shape[0] += 1
output = np.arange(np.prod(new_shape)).reshape(new_shape)
return output
# doesn't return correct length
feature = Feature(
input='size',
transformer=_WrongLengthTransformer(),
)
valid, failures, advice = check_from_class(
FeatureApiCheck, feature, sample_data.X, sample_data.y)
assert not valid
assert HasCorrectOutputDimensionsCheck.__name__ in failures
def test_bad_feature_wrong_transform_length(self):
class _WrongLengthTransformer(BaseTransformer):
def transform(self, X, **transform_kwargs):
new_shape = list(X.shape)
new_shape[0] += 1
output = np.arange(np.prod(new_shape)).reshape(new_shape)
return output
# doesn't return correct length
feature = Feature(
input='size',
transformer=_WrongLengthTransformer(),
)
valid, failures = check_from_class(
FeatureApiCheck, feature, self.X, self.y)
self.assertFalse(valid)
self.assertIn(HasCorrectOutputDimensionsCheck.__name__, failures)