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ENH feature_names_in_ for sklearn.ensemble #20818

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Merged
merged 1 commit into from
Aug 25, 2021
Merged

ENH feature_names_in_ for sklearn.ensemble #20818

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27 changes: 26 additions & 1 deletion sklearn/ensemble/_forest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -565,7 +565,7 @@ def _validate_X_predict(self, X):
"""
Validate X whenever one tries to predict, apply, predict_proba."""
check_is_fitted(self)

self._check_feature_names(X, reset=False)
return self.estimators_[0]._validate_X_predict(X, check_input=True)

@property
Expand Down Expand Up @@ -1265,6 +1265,11 @@ class labels (multi-output problem).

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Suggested change
feature_names_in_ : ndarray of shape (`n_features_in_`,)
feature_names_in_ : ndarray of shape (n_features_in_,)

We also do X : array-like of shape (n_samples, n_features) without ticks.

Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0

n_outputs_ : int
The number of outputs when ``fit`` is performed.

Expand Down Expand Up @@ -1590,6 +1595,11 @@ class RandomForestRegressor(ForestRegressor):

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Suggested change
feature_names_in_ : ndarray of shape (`n_features_in_`,)
feature_names_in_ : ndarray of shape (n_features_in_,)

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As explained in another PR, this would break sphinx rendering because it would treat those as a link to a reference.

Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.
.. versionadded:: 1.0

n_outputs_ : int
The number of outputs when ``fit`` is performed.

Expand Down Expand Up @@ -1920,6 +1930,11 @@ class labels (multi-output problem).

.. 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_outputs_ : int
The number of outputs when ``fit`` is performed.

Expand Down Expand Up @@ -2222,6 +2237,11 @@ class ExtraTreesRegressor(ForestRegressor):

.. 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_outputs_ : int
The number of outputs.

Expand Down Expand Up @@ -2454,6 +2474,11 @@ class RandomTreesEmbedding(BaseForest):

.. 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_outputs_ : int
The number of outputs when ``fit`` is performed.

Expand Down
15 changes: 14 additions & 1 deletion sklearn/ensemble/_stacking.py
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Expand Up @@ -165,8 +165,11 @@ def fit(self, X, y, sample_weight=None):
est_fitted_idx = 0
for name_est, org_est in zip(names, all_estimators):
if org_est != "drop":
self.named_estimators_[name_est] = self.estimators_[est_fitted_idx]
current_estimator = self.estimators_[est_fitted_idx]
self.named_estimators_[name_est] = current_estimator
est_fitted_idx += 1
if hasattr(current_estimator, "feature_names_in_"):
self.feature_names_in_ = current_estimator.feature_names_in_
else:
self.named_estimators_[name_est] = "drop"

Expand Down Expand Up @@ -373,6 +376,11 @@ class StackingClassifier(ClassifierMixin, _BaseStacking):

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
underlying estimators expose such an attribute when fit.
.. versionadded:: 1.0

final_estimator_ : estimator
The classifier which predicts given the output of `estimators_`.

Expand Down Expand Up @@ -649,6 +657,11 @@ class StackingRegressor(RegressorMixin, _BaseStacking):

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
underlying estimators expose such an attribute when fit.
.. versionadded:: 1.0

final_estimator_ : estimator
The regressor to stacked the base estimators fitted.

Expand Down
13 changes: 13 additions & 0 deletions sklearn/ensemble/_voting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,6 +92,9 @@ def fit(self, X, y, sample_weight=None):
current_est = est if est == "drop" else next(est_iter)
self.named_estimators_[name] = current_est

if hasattr(current_est, "feature_names_in_"):
self.feature_names_in_ = current_est.feature_names_in_

return self

def fit_transform(self, X, y=None, **fit_params):
Expand Down Expand Up @@ -217,6 +220,11 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
underlying estimators expose such an attribute when fit.
.. versionadded:: 1.0

See Also
--------
VotingRegressor : Prediction voting regressor.
Expand Down Expand Up @@ -466,6 +474,11 @@ class VotingRegressor(RegressorMixin, _BaseVoting):

.. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
underlying estimators expose such an attribute when fit.
.. versionadded:: 1.0

See Also
--------
VotingClassifier : Soft Voting/Majority Rule classifier.
Expand Down
1 change: 0 additions & 1 deletion sklearn/tests/test_common.py
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Expand Up @@ -325,7 +325,6 @@ def test_check_n_features_in_after_fitting(estimator):

COLUMN_NAME_MODULES_TO_IGNORE = {
"compose",
"ensemble",
"feature_extraction",
"kernel_approximation",
"model_selection",
Expand Down