MNT Checking function _estimator_has also raises AttributeError

sklearn/ensemble/_stacking.py

@@ -45,14 +45,20 @@
 def _estimator_has(attr):
     """Check if we can delegate a method to the underlying estimator.
 
-    First, we check the first fitted final estimator if available, otherwise we
-    check the unfitted final estimator.
+    First, we check the fitted final_estimator if available, otherwise we check the
+    unfitted final_estimator. We raise the original `AttributeError` if `attr` does
+    not exist. This function is used together with `avaliable_if`.
     """
-    return lambda self: (
-        hasattr(self.final_estimator_, attr)
-        if hasattr(self, "final_estimator_")
-        else hasattr(self.final_estimator, attr)
-    )
+
+    def check(self):
+        if hasattr(self, "final_estimator_"):
+            getattr(self.final_estimator_, attr)
+            return True
+        else:
+            getattr(self.final_estimator, attr)
+            return True
+
+    return check
 
 
 class _BaseStacking(TransformerMixin, _BaseHeterogeneousEnsemble, metaclass=ABCMeta):

sklearn/feature_selection/_from_model.py

@@ -74,14 +74,20 @@ def _calculate_threshold(estimator, importances, threshold):
 def _estimator_has(attr):
     """Check if we can delegate a method to the underlying estimator.
 
-    First, we check the fitted estimator if available, otherwise we
-    check the unfitted estimator.
+    First, we check the fitted estimator if available, otherwise we check the
+    unfitted estimator. We raise the original `AttributeError` if `attr` does
+    not exist. This function is used together with `avaliable_if`.
     """
-    return lambda self: (
-        hasattr(self.estimator_, attr)
-        if hasattr(self, "estimator_")
-        else hasattr(self.estimator, attr)
-    )
+
+    def check(self):
+        if hasattr(self, "estimator_"):
+            getattr(self.estimator_, attr)
+            return True
+        else:
+            getattr(self.estimator, attr)
+            return True
+
+    return check
 
 
 class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):

sklearn/feature_selection/_rfe.py

@@ -49,14 +49,20 @@ def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer):
 def _estimator_has(attr):
     """Check if we can delegate a method to the underlying estimator.
 
-    First, we check the first fitted estimator if available, otherwise we
-    check the unfitted estimator.
+    First, we check the fitted estimator if available, otherwise we check the
+    unfitted estimator. We raise the original `AttributeError` if `attr` does
+    not exist. This function is used together with `avaliable_if`.
     """
-    return lambda self: (
-        hasattr(self.estimator_, attr)
-        if hasattr(self, "estimator_")
-        else hasattr(self.estimator, attr)
-    )
+
+    def check(self):
+        if hasattr(self, "estimator_"):
+            getattr(self.estimator_, attr)
+            return True
+        else:
+            getattr(self.estimator, attr)
+            return True
+
+    return check
 
 
 class RFE(_RoutingNotSupportedMixin, SelectorMixin, MetaEstimatorMixin, BaseEstimator):

sklearn/multiclass.py

@@ -181,12 +181,19 @@ def _estimators_has(attr):
     """Check if self.estimator or self.estimators_[0] has attr.
 
     If `self.estimators_[0]` has the attr, then its safe to assume that other
-    values has it too. This function is used together with `avaliable_if`.
+    estimators have it too. We raise the original `AttributeError` if `attr`
+    does not exist. This function is used together with `avaliable_if`.
     """
-    return lambda self: (
-        hasattr(self.estimator, attr)
-        or (hasattr(self, "estimators_") and hasattr(self.estimators_[0], attr))
-    )
+
+    def check(self):
+        if hasattr(self, "estimators_"):
+            getattr(self.estimators_[0], attr)
+            return True
+        else:
+            getattr(self.estimator, attr)
+            return True
+
+    return check
 
 
 class OneVsRestClassifier(
@@ -434,12 +441,6 @@ def partial_fit(self, X, y, classes=None, **partial_fit_params):
         )
 
         if _check_partial_fit_first_call(self, classes):
-            if not hasattr(self.estimator, "partial_fit"):
-                raise ValueError(
-                    ("Base estimator {0}, doesn't have partial_fit method").format(
-                        self.estimator
-                    )
-                )
             self.estimators_ = [clone(self.estimator) for _ in range(self.n_classes_)]
 
             # A sparse LabelBinarizer, with sparse_output=True, has been

sklearn/semi_supervised/_self_training.py

@@ -18,12 +18,22 @@
 
 
 def _estimator_has(attr):
-    """Check if `self.base_estimator_ `or `self.base_estimator_` has `attr`."""
-    return lambda self: (
-        hasattr(self.base_estimator_, attr)
-        if hasattr(self, "base_estimator_")
-        else hasattr(self.base_estimator, attr)
-    )
+    """Check if we can delegate a method to the underlying estimator.
+
+    First, we check the fitted base_estimator if available, otherwise we check
+    the unfitted base_estimator. We raise the original `AttributeError` if
+    `attr` does not exist. This function is used together with `avaliable_if`.
+    """
+
+    def check(self):
+        if hasattr(self, "base_estimator_"):
+            getattr(self.base_estimator_, attr)
+            return True
+        else:
+            getattr(self.base_estimator, attr)
+            return True
+
+    return check
 
 
 class SelfTrainingClassifier(