8000 CLN Only check for n_features_in_ only when it exists by thomasjpfan · Pull Request #18011 · scikit-learn/scikit-learn · GitHub
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CLN Only check for n_features_in_ only when it exists #18011

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Merged
merged 7 commits into from
Oct 13, 2020
Merged

CLN Only check for n_features_in_ only when it exists #18011

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6eb9905
CLN Checks n_features_in only if it exists
thomasjpfan Jul 27, 2020
ed5a7db
Merge remote-tracking branch 'upstream/master' into cln_check_n_features
thomasjpfan Jul 27, 2020
49fed40
Merge remote-tracking branch 'upstream/master' into cln_check_n_features
thomasjpfan Oct 9, 2020
d71201f
Update sklearn/base.py
thomasjpfan Oct 12, 2020
edb6029
DOC Update docstring
thomasjpfan Oct 12, 2020
833f185
DOC Grammer
thomasjpfan Oct 12, 2020
2e09089
Grammar [ci skip]
ogrisel Oct 13, 2020
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29 changes: 15 additions & 14 deletions sklearn/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -359,8 +359,9 @@ def _check_n_features(self, X, reset):
The input samples.
reset : bool
If True, the `n_features_in_` attribute is set to `X.shape[1]`.
Else, the attribute must already exist and the function checks
that it is equal to `X.shape[1]`.
If False and the attribute exists, then check that it is equal to
`X.shape[1]`. If False and the attribute does *not* exist, then
the check is skipped.
.. note::
It is recommended to call reset=True in `fit` and in the first
call to `partial_fit`. All other methods that validate `X`
Expand All @@ -370,18 +371,18 @@ def _check_n_features(self, X, reset):

if reset:
self.n_features_in_ = n_features
else:
if not hasattr(self, 'n_features_in_'):
raise RuntimeError(
"The reset parameter is False but there is no "
"n_features_in_ attribute. Is this estimator fitted?"
)
if n_features != self.n_features_in_:
raise ValueError(
'X has {} features, but {} is expecting {} features '
'as input.'.format(n_features, self.__class__.__name__,
self.n_features_in_)
)
return

if not hasattr(self, "n_features_in_"):
# Skip this check if the expected number of expected input features
# was not recorded by calling fit first. This is typically the case
# for stateless transformers.
return

if n_features != self.n_features_in_:
raise ValueError(
f"X has {n_features} features, but {self.__class__.__name__} "
f"is expecting {self.n_features_in_} features as input.")

def _validate_data(self, X, y='no_validation', reset=True,
validate_separately=False, **check_params):
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