Allow sklearn to update to current version.

afqinsight/_serial_bagging.py

@@ -25,7 +25,7 @@
 from sklearn.ensemble._base import _partition_estimators
 from sklearn.utils import check_random_state, check_array, indices_to_mask, resample
 from sklearn.utils.random import sample_without_replacement
-from sklearn.utils.metaestimators import if_delegate_has_method
+from sklearn.utils.metaestimators import available_if
 from sklearn.utils.validation import (
     check_is_fitted,
     _check_sample_weight,
@@ -103,7 +103,7 @@ def _parallel_build_estimators(
     max_samples = ensemble._max_samples
     bootstrap = ensemble.bootstrap
     bootstrap_features = ensemble.bootstrap_features
-    support_sample_weight = has_fit_parameter(ensemble.base_estimator_, "sample_weight")
+    support_sample_weight = has_fit_parameter(ensemble.estimator_, "sample_weight")
     if not support_sample_weight and sample_weight is not None:
         raise ValueError("The base estimator doesn't support sample weight")
 
@@ -182,7 +182,7 @@ class SerialBaggingClassifier(BaggingClassifier):
 
     Parameters
     ----------
-    base_estimator : object, default=None
+    estimator : object, default=None
         The base estimator to fit on random subsets of the dataset.
         If None, then the base estimator is a decision tree.
 
@@ -236,7 +236,7 @@ class SerialBaggingClassifier(BaggingClassifier):
 
     Attributes
     ----------
-    base_estimator_ : estimator
+    estimator_ : estimator
         The base estimator from which the ensemble is grown.
 
     n_features_in_ : int
@@ -287,7 +287,7 @@ class SerialBaggingClassifier(BaggingClassifier):
 
     def __init__(
         self,
-        base_estimator=None,
+        estimator=None,
         n_estimators=10,
         *,
         max_samples=1.0,
@@ -301,7 +301,7 @@ def __init__(
         verbose=0,
     ):
         super().__init__(
-            base_estimator=base_estimator,
+            estimator=estimator,
             n_estimators=n_estimators,
             max_samples=max_samples,
             max_features=max_features,
@@ -367,7 +367,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
         self._validate_estimator()
 
         if max_depth is not None:  # pragma: no cover
-            self.base_estimator_.max_depth = max_depth
+            self.estimator_.max_depth = max_depth
 
         # Validate max_samples
         if max_samples is None:  # pragma: no cover
@@ -568,7 +568,7 @@ def predict_log_proba(self, X):
             classes corresponds to that in the attribute :term:`classes_`.
         """
         check_is_fitted(self)
-        if hasattr(self.base_estimator_, "predict_log_proba"):
+        if hasattr(self.estimator_, "predict_log_proba"):
             # Check data
             X = check_array(
                 X, accept_sparse=["csr", "csc"], dtype=None, force_all_finite=False
@@ -610,7 +610,7 @@ def predict_log_proba(self, X):
         else:
             return np.log(self.predict_proba(X))
 
-    @if_delegate_has_method(delegate="base_estimator")
+    @available_if(lambda self: hasattr(self, "estimator"))
     def decision_function(self, X):
         """Average of the decision functions of the base classifiers.
 
@@ -690,7 +690,7 @@ class SerialBaggingRegressor(BaggingRegressor):
 
     Parameters
     ----------
-    base_estimator : object, default=None
+    estimator : object, default=None
         The base estimator to fit on random subsets of the dataset.
         If None, then the base estimator is a decision tree.
 
@@ -745,7 +745,7 @@ class SerialBaggingRegressor(BaggingRegressor):
 
     Attributes
     ----------
-    base_estimator_ : estimator
+    estimator_ : estimator
         The base estimator from which the ensemble is grown.
 
     n_features_in_ : int
@@ -780,7 +780,7 @@ class SerialBaggingRegressor(BaggingRegressor):
     >>> X, y = make_regression(n_samples=100, n_features=4,
     ...                        n_informative=2, n_targets=1,
     ...                        random_state=0, shuffle=False)
-    >>> regr = BaggingRegressor(base_estimator=SVR(),
+    >>> regr = BaggingRegressor(estimator=SVR(),
     ...                         n_estimators=10, random_state=0).fit(X, y)
     >>> regr.predict([[0, 0, 0, 0]])
     array([-2.8720...])
@@ -803,7 +803,7 @@ class SerialBaggingRegressor(BaggingRegressor):
 
     def __init__(
         self,
-        base_estimator=None,
+        estimator=None,
         n_estimators=10,
         max_samples=1.0,
         max_features=1.0,
@@ -816,7 +816,7 @@ def __init__(
         verbose=0,
     ):
         super().__init__(
-            base_estimator=base_estimator,
+            estimator=estimator,
             n_estimators=n_estimators,
             max_samples=max_samples,
             max_features=max_features,
@@ -881,7 +881,7 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
         self._validate_estimator()
 
         if max_depth is not None:  # pragma: no cover
-            self.base_estimator_.max_depth = max_depth
+            self.estimator_.max_depth = max_depth
 
         # Validate max_samples
         if max_samples is None:  # pragma: no cover

afqinsight/pipeline.py

@@ -273,7 +273,7 @@ def call_with_kwargs(Transformer, kwargs):
                 else:
                     ensembler_kwargs = {}
 
-                ensembler_kwargs["base_estimator"] = base_estimator
+                ensembler_kwargs["estimator"] = base_estimator
 
                 if isinstance(ensemble_meta_estimator, str):
                     if ensemble_meta_estimator.lower() == "bagging":