BUG Fixes stacker compatibility with estimators without n_features_in_

doc/whats_new/v0.23.rst

@@ -2,6 +2,21 @@
 
 .. currentmodule:: sklearn
 
+.. _changes_0_23_2:
+
+Version 0.23.2
+==============
+
+Changelog
+---------
+
+:mod:`sklearn.ensemble`
+.......................
+
+- |Fix| Fixes :class:`ensemble.StackingClassifier` and
+  :class:`ensemble.StackingRegressor` compatibility with estimators that
+  do not define `n_features_in_`. :pr:`17357` by `Thomas Fan`_.
+
 .. _changes_0_23_1:
 
 Version 0.23.1

sklearn/ensemble/_stacking.py

@@ -13,6 +13,7 @@
 from ..base import clone
 from ..base import ClassifierMixin, RegressorMixin, TransformerMixin
 from ..base import is_classifier, is_regressor
+from ..exceptions import NotFittedError
 from ..utils._estimator_html_repr import _VisualBlock
 
 from ._base import _fit_single_estimator
@@ -146,7 +147,6 @@ def fit(self, X, y, sample_weight=None):
             delayed(_fit_single_estimator)(clone(est), X, y, sample_weight)
             for est in all_estimators if est != 'drop'
         )
-        self.n_features_in_ = self.estimators_[0].n_features_in_
 
         self.named_estimators_ = Bunch()
         est_fitted_idx = 0
@@ -197,6 +197,17 @@ def fit(self, X, y, sample_weight=None):
 
         return self
 
+    @property
+    def n_features_in_(self):
+        """Number of features seen during :term:`fit`."""
+        try:
+            check_is_fitted(self)
+        except NotFittedError as nfe:
+            raise AttributeError(
+                f"{self.__class__.__name__} object has no attribute "
+                f"n_features_in_") from nfe
+        return self.estimators_[0].n_features_in_
+
     def _transform(self, X):
         """Concatenate and return the predictions of the estimators."""
         check_is_fitted(self)

sklearn/ensemble/tests/test_stacking.py

@@ -17,6 +17,8 @@
 from sklearn.datasets import load_iris
 from sklearn.datasets import load_diabetes
 from sklearn.datasets import load_breast_cancer
+from sklearn.datasets import make_regression
+from sklearn.datasets import make_classification
 
 from sklearn.dummy import DummyClassifier
 from sklearn.dummy import DummyRegressor
@@ -491,3 +493,32 @@ def test_stacking_cv_influence(stacker, X, y):
     with pytest.raises(AssertionError, match='Not equal'):
         assert_allclose(stacker_cv_3.final_estimator_.coef_,
                         stacker_cv_5.final_estimator_.coef_)
+
+
+@pytest.mark.parametrize("make_dataset, Stacking, Estimator", [
+    (make_classification, StackingClassifier, LogisticRegression),
+    (make_regression, StackingRegressor, LinearRegression)
+])
+def test_stacking_without_n_features_in(make_dataset, Stacking, Estimator):
+    # Stacking supports estimators without `n_features_in_`. Regression test
+    # for #17353
+
+    class MyEstimator(Estimator):
+        """Estimator without n_features_in_"""
+        def fit(self, X, y):
+            super().fit(X, y)
+            del self.n_features_in_
+
+    X, y = make_dataset(random_state=0, n_samples=100)
+    stacker = Stacking(estimators=[('lr', MyEstimator())])
+
+    msg = f"{Stacking.__name__} object has no attribute n_features_in_"
+    with pytest.raises(AttributeError, match=msg):
+        stacker.n_features_in_
+
+    # Does not raise
+    stacker.fit(X, y)
+
+    msg = "'MyEstimator' object has no attribute 'n_features_in_'"
+    with pytest.raises(AttributeError, match=msg):
+        stacker.n_features_in_