MAINT move _estimator_has function to utils (scikit-learn#29319)

Co-authored-by: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com>
Co-authored-by: adrinjalali <adrin.jalali@gmail.com>

Author: 3 people

sklearn/ensemble/_bagging.py

@@ -41,6 +41,7 @@
     _check_method_params,
     _check_sample_weight,
     _deprecate_positional_args,
+    _estimator_has,
     check_is_fitted,
     has_fit_parameter,
     validate_data,
@@ -269,22 +270,6 @@ def _parallel_predict_regression(estimators, estimators_features, X):
     )
 
 
-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 estimator attribute.
-    """
-
-    def check(self):
-        if hasattr(self, "estimators_"):
-            return hasattr(self.estimators_[0], attr)
-        else:  # self.estimator is not None
-            return hasattr(self.estimator, attr)
-
-    return check
-
-
 class BaseBagging(BaseEnsemble, metaclass=ABCMeta):
     """Base class for Bagging meta-estimator.
 
@@ -1033,7 +1018,9 @@ def predict_log_proba(self, X):
 
         return log_proba
 
-    @available_if(_estimator_has("decision_function"))
+    @available_if(
+        _estimator_has("decision_function", delegates=("estimators_", "estimator"))
+    )
     def decision_function(self, X):
         """Average of the decision functions of the base classifiers.
 

sklearn/ensemble/_stacking.py

@@ -40,31 +40,13 @@
     _check_feature_names_in,
     _check_response_method,
     _deprecate_positional_args,
+    _estimator_has,
     check_is_fitted,
     column_or_1d,
 )
 from ._base import _BaseHeterogeneousEnsemble, _fit_single_estimator
 
 
-def _estimator_has(attr):
-    """Check if we can delegate a method to the underlying 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 `available_if`.
-    """
-
-    def check(self):
-        if hasattr(self, "final_estimator_"):
-            getattr(self.final_estimator_, attr)
-        else:
-            getattr(self.final_estimator, attr)
-
-        return True
-
-    return check
-
-
 class _BaseStacking(TransformerMixin, _BaseHeterogeneousEnsemble, metaclass=ABCMeta):
     """Base class for stacking method."""
 
@@ -364,7 +346,9 @@ def get_feature_names_out(self, input_features=None):
 
         return np.asarray(meta_names, dtype=object)
 
-    @available_if(_estimator_has("predict"))
+    @available_if(
+        _estimator_has("predict", delegates=("final_estimator_", "final_estimator"))
+    )
     def predict(self, X, **predict_params):
         """Predict target for X.
 
@@ -732,7 +716,9 @@ def fit(self, X, y, *, sample_weight=None, **fit_params):
             fit_params["sample_weight"] = sample_weight
         return super().fit(X, y_encoded, **fit_params)
 
-    @available_if(_estimator_has("predict"))
+    @available_if(
+        _estimator_has("predict", delegates=("final_estimator_", "final_estimator"))
+    )
     def predict(self, X, **predict_params):
         """Predict target for X.
 
@@ -785,7 +771,11 @@ def predict(self, X, **predict_params):
             y_pred = self._label_encoder.inverse_transform(y_pred)
         return y_pred
 
-    @available_if(_estimator_has("predict_proba"))
+    @available_if(
+        _estimator_has(
+            "predict_proba", delegates=("final_estimator_", "final_estimator")
+        )
+    )
     def predict_proba(self, X):
         """Predict class probabilities for `X` using the final estimator.
 
@@ -809,7 +799,11 @@ def predict_proba(self, X):
             y_pred = np.array([preds[:, 0] for preds in y_pred]).T
         return y_pred
 
-    @available_if(_estimator_has("decision_function"))
+    @available_if(
+        _estimator_has(
+            "decision_function", delegates=("final_estimator_", "final_estimator")
+        )
+    )
     def decision_function(self, X):
         """Decision function for samples in `X` using the final estimator.
 
@@ -1125,7 +1119,9 @@ def fit_transform(self, X, y, *, sample_weight=None, **fit_params):
             fit_params["sample_weight"] = sample_weight
         return super().fit_transform(X, y, **fit_params)
 
-    @available_if(_estimator_has("predict"))
+    @available_if(
+        _estimator_has("predict", delegates=("final_estimator_", "final_estimator"))
+    )
     def predict(self, X, **predict_params):
         """Predict target for X.
 

sklearn/feature_selection/_from_model.py

@@ -19,6 +19,7 @@
 from ..utils.metaestimators import available_if
 from ..utils.validation import (
     _check_feature_names,
+    _estimator_has,
     _num_features,
     check_is_fitted,
     check_scalar,
@@ -76,25 +77,6 @@ def _calculate_threshold(estimator, importances, threshold):
     return 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`. We raise the original `AttributeError` if `attr` does
-    not exist. This function is used together with `available_if`.
-    """
-
-    def check(self):
-        if hasattr(self, "estimator_"):
-            getattr(self.estimator_, attr)
-        else:
-            getattr(self.estimator, attr)
-
-        return True
-
-    return check
-
-
 class SelectFromModel(MetaEstimatorMixin, SelectorMixin, BaseEstimator):
     """Meta-transformer for selecting features based on importance weights.
 

sklearn/feature_selection/_rfe.py

@@ -29,6 +29,7 @@
 from ..utils.validation import (
     _check_method_params,
     _deprecate_positional_args,
+    _estimator_has,
     check_is_fitted,
     validate_data,
 )
@@ -64,25 +65,6 @@ def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer, routed_params):
     return rfe.step_scores_, rfe.step_n_features_
 
 
-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`. We raise the original `AttributeError` if `attr` does
-    not exist. This function is used together with `available_if`.
-    """
-
-    def check(self):
-        if hasattr(self, "estimator_"):
-            getattr(self.estimator_, attr)
-        else:
-            getattr(self.estimator, attr)
-
-        return True
-
-    return check
-
-
 class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):
     """Feature ranking with recursive feature elimination.
 

sklearn/model_selection/_classification_threshold.py

@@ -36,6 +36,7 @@
 from ..utils.parallel import Parallel, delayed
 from ..utils.validation import (
     _check_method_params,
+    _estimator_has,
     _num_samples,
     check_is_fitted,
     indexable,
@@ -50,23 +51,6 @@ def _check_is_fitted(estimator):
         check_is_fitted(estimator, "estimator_")
 
 
-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.
-    """
-
-    def check(self):
-        if hasattr(self, "estimator_"):
-            getattr(self.estimator_, attr)
-        else:
-            getattr(self.estimator, attr)
-        return True
-
-    return check
-
-
 class BaseThresholdClassifier(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
     """Base class for binary classifiers that set a non-default decision threshold.
 

sklearn/model_selection/_search.py

@@ -356,7 +356,7 @@ def _check_refit(search_cv, attr):
         )
 
 
-def _estimator_has(attr):
+def _search_estimator_has(attr):
     """Check if we can delegate a method to the underlying estimator.
 
     Calling a prediction method will only be available if `refit=True`. In
@@ -555,7 +555,7 @@ def score(self, X, y=None, **params):
             score = score[self.refit]
         return score
 
-    @available_if(_estimator_has("score_samples"))
+    @available_if(_search_estimator_has("score_samples"))
     def score_samples(self, X):
         """Call score_samples on the estimator with the best found parameters.
 
@@ -578,7 +578,7 @@ def score_samples(self, X):
         check_is_fitted(self)
         return self.best_estimator_.score_samples(X)
 
-    @available_if(_estimator_has("predict"))
+    @available_if(_search_estimator_has("predict"))
     def predict(self, X):
         """Call predict on the estimator with the best found parameters.
 
@@ -600,7 +600,7 @@ def predict(self, X):
         check_is_fitted(self)
         return self.best_estimator_.predict(X)
 
-    @available_if(_estimator_has("predict_proba"))
+    @available_if(_search_estimator_has("predict_proba"))
     def predict_proba(self, X):
         """Call predict_proba on the estimator with the best found parameters.
 
@@ -623,7 +623,7 @@ def predict_proba(self, X):
         check_is_fitted(self)
         return self.best_estimator_.predict_proba(X)
 
-    @available_if(_estimator_has("predict_log_proba"))
+    @available_if(_search_estimator_has("predict_log_proba"))
     def predict_log_proba(self, X):
         """Call predict_log_proba on the estimator with the best found parameters.
 
@@ -646,7 +646,7 @@ def predict_log_proba(self, X):
         check_is_fitted(self)
         return self.best_estimator_.predict_log_proba(X)
 
-    @available_if(_estimator_has("decision_function"))
+    @available_if(_search_estimator_has("decision_function"))
     def decision_function(self, X):
         """Call decision_function on the estimator with the best found parameters.
 
@@ -669,7 +669,7 @@ def decision_function(self, X):
         check_is_fitted(self)
         return self.best_estimator_.decision_function(X)
 
-    @available_if(_estimator_has("transform"))
+    @available_if(_search_estimator_has("transform"))
     def transform(self, X):
         """Call transform on the estimator with the best found parameters.
 
@@ -691,7 +691,7 @@ def transform(self, X):
         check_is_fitted(self)
         return self.best_estimator_.transform(X)
 
-    @available_if(_estimator_has("inverse_transform"))
+    @available_if(_search_estimator_has("inverse_transform"))
     def inverse_transform(self, X=None, Xt=None):
         """Call inverse_transform on the estimator with the best found params.
 
@@ -746,7 +746,7 @@ def classes_(self):
 
         Only available when `refit=True` and the estimator is a classifier.
         """
-        _estimator_has("classes_")(self)
+        _search_estimator_has("classes_")(self)
         return self.best_estimator_.classes_
 
     def _run_search(self, evaluate_candidates):

sklearn/semi_supervised/_self_training.py

@@ -17,33 +17,14 @@
     process_routing,
 )
 from ..utils.metaestimators import available_if
-from ..utils.validation import check_is_fitted, validate_data
+from ..utils.validation import _estimator_has, check_is_fitted, validate_data
 
 __all__ = ["SelfTrainingClassifier"]
 
 # Authors: The scikit-learn developers
 # SPDX-License-Identifier: BSD-3-Clause
 
 
-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`. We raise the original `AttributeError` if
-    `attr` does not exist. This function is used together with `available_if`.
-    """
-
-    def check(self):
-        if hasattr(self, "estimator_"):
-            getattr(self.estimator_, attr)
-        else:
-            getattr(self.estimator, attr)
-
-        return True
-
-    return check
-
-
 class SelfTrainingClassifier(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
     """Self-training classifier.