Add test coverage

sklearn/ensemble/tests/test_bagging.py

@@ -447,7 +447,9 @@ def test_error():
     # Test support of decision_function
     X, y = iris.data, iris.target
     base = DecisionTreeClassifier()
-    assert not hasattr(BaggingClassifier(base).fit(X, y), "decision_function")
+    assert not hasattr(
+        BaggingClassifier(base).fit(X, y), "decision_function"
+    ) and "decision_function" not in dir(BaggingClassifier(base).fit(X, y))
 
 
 def test_parallel_classification():

sklearn/ensemble/tests/test_stacking.py

@@ -882,8 +882,11 @@ def test_stacking_final_estimator_attribute_error():
         estimators=estimators, final_estimator=final_estimator, cv=3
     )
 
+    assert "decision_function" not in dir(clf.fit(X, y))
+
     outer_msg = "This 'StackingClassifier' has no attribute 'decision_function'"
     inner_msg = "'RandomForestClassifier' object has no attribute 'decision_function'"
+
     with pytest.raises(AttributeError, match=outer_msg) as exec_info:
         clf.fit(X, y).decision_function(X)
     assert isinstance(exec_info.value.__cause__, AttributeError)

sklearn/ensemble/tests/test_voting.py

@@ -78,9 +78,9 @@ def test_predictproba_hardvoting():
     assert isinstance(exec_info.value.__cause__, AttributeError)
     assert inner_msg in str(exec_info.value.__cause__)
 
-    assert not hasattr(eclf, "predict_proba")
+    assert not hasattr(eclf, "predict_proba") and "predict_proba" not in dir(eclf)
     eclf.fit(X_scaled, y)
-    assert not hasattr(eclf, "predict_proba")
+    assert not hasattr(eclf, "predict_proba") and "predict_proba" not in dir(eclf)
 
 
 def test_notfitted():

sklearn/feature_selection/_from_model.py

@@ -270,9 +270,6 @@ def __init__(
         self.norm_order = norm_order
         self.max_features = max_features
 
-    def __dir__(self):
-        return [attr for attr in super().__dir__() if hasattr(self, attr)]
-
     def _get_support_mask(self):
         estimator = getattr(self, "estimator_", self.estimator)
         max_features = getattr(self, "max_features_", self.max_features)

sklearn/feature_selection/tests/test_rfe.py

@@ -645,6 +645,7 @@ def test_rfe_estimator_attribute_error():
 
     outer_msg = "This 'RFE' has no attribute 'decision_function'"
     inner_msg = "'LinearRegression' object has no attribute 'decision_function'"
+    assert "decision_function" not in dir(rfe.fit(iris.data, iris.target))
     with pytest.raises(AttributeError, match=outer_msg) as exec_info:
         rfe.fit(iris.data, iris.target).decision_function(iris.data)
     assert isinstance(exec_info.value.__cause__, AttributeError)