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ENH Adds feature_names_in_ to sklearn.semi_supervised #20788

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Merged
merged 5 commits into from
Aug 26, 2021
Merged

ENH Adds feature_names_in_ to sklearn.semi_supervised #20788

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c3ae5db
column name consistency check on sklearnsemi_supervised
ogrisel Aug 20, 2021
c997a0b
Document feature_names_in_ in SelfTrainingClassifier's docstring
ogrisel Aug 20, 2021
e65759a
Apply suggestions from code review
ogrisel Aug 21, 2021
03c5be2
Just use _check_feature_names instead of nesting _validate_data
ogrisel Aug 23, 2021
cf38620
Merge branch 'main' into column-name-consistency-semi_supervised
ogrisel Aug 25, 2021
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11 changes: 11 additions & 0 deletions sklearn/semi_supervised/_self_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,6 +91,12 @@ class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator):

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.

.. versionadded:: 1.0

n_iter_ : int
The number of rounds of self-training, that is the number of times the
base estimator is fitted on relabeled variants of the training set.
Expand Down Expand Up @@ -285,6 +291,7 @@ def predict(self, X):
Array with predicted labels.
"""
check_is_fitted(self)
self._check_feature_names(X, reset=False)
return self.base_estimator_.predict(X)

def predict_proba(self, X):
Expand All @@ -301,6 +308,7 @@ def predict_proba(self, X):
Array with prediction probabilities.
"""
check_is_fitted(self)
self._check_feature_names(X, reset=False)
return self.base_estimator_.predict_proba(X)

@if_delegate_has_method(delegate="base_estimator")
Expand All @@ -318,6 +326,7 @@ def decision_function(self, X):
Result of the decision function of the `base_estimator`.
"""
check_is_fitted(self)
self._check_feature_names(X, reset=False)
return self.base_estimator_.decision_function(X)

@if_delegate_has_method(delegate="base_estimator")
Expand All @@ -335,6 +344,7 @@ def predict_log_proba(self, X):
Array with log prediction probabilities.
"""
check_is_fitted(self)
self._check_feature_names(X, reset=False)
return self.base_estimator_.predict_log_proba(X)

@if_delegate_has_method(delegate="base_estimator")
Expand All @@ -355,4 +365,5 @@ def score(self, X, y):
Result of calling score on the `base_estimator`.
"""
check_is_fitted(self)
self._check_feature_names(X, reset=False)
return self.base_estimator_.score(X, y)
1 change: 0 additions & 1 deletion sklearn/tests/test_common.py
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Expand Up @@ -329,7 +329,6 @@ def test_check_n_features_in_after_fitting(estimator):
"kernel_approximation",
"model_selection",
"multioutput",
"semi_supervised",
}

_estimators_to_test = list(
Expand Down