def test_valid_tag_types(estimator):
"""Check that estimator tags are valid."""
tags = _safe_tags(estimator)
for name, tag in tags.items():
correct_tags = type(_DEFAULT_TAGS[name])
if name == "_xfail_checks":
# _xfail_checks can be a dictionary
correct_tags = (correct_tags, dict)
assert isinstance(tag, correct_tags)
def transform(self, X, y=None):
"""Reduce X to the selected features.
Parameters
----------
X : ndarray of shape [n_samples, n_features]
The input samples.
y : ignored
Returns
-------
X_r : ndarray
The selected subset of the input.
"""
if len(X.shape) == 1:
X = X.reshape(-1, 1)
mask = self.get_support()
# note: we use _safe_tags instead of _get_tags because this is a
# public Mixin.
X = self._validate_data(
X,
dtype=None,
accept_sparse="csr",
force_all_finite=not _safe_tags(self, key="allow_nan"),
reset=False,
ensure_2d=self._axis,
)
if len(mask) != X.shape[self._axis]:
raise ValueError("X has a different shape than during fitting.")
if self._axis == 1:
return X[:, safe_mask(X, mask)]
else:
return X[safe_mask(X, mask)]
def _more_tags(self):
# check if first estimator expects pairwise input
return {'pairwise': _safe_tags(self.steps[0][1], "pairwise")}
def test_safe_tags_no_get_tags(estimator, key, expected_results):
# check the behaviour of _safe_tags when an estimator does not implement
# _get_tags
assert _safe_tags(estimator, key=key) == expected_results
def test_safe_tags_error(estimator, err_msg):
# Check that safe_tags raises error in ambiguous case.
with pytest.raises(ValueError, match=err_msg):
_safe_tags(estimator, key="xxx")
