MAINT compatibility sklearn 1.4

azure-pipelines.yml

@@ -115,10 +115,10 @@ jobs:
         ne(variables['Build.Reason'], 'Schedule')
       )
     matrix:
-      py38_conda_forge_openblas_ubuntu_1804:
+      py39_conda_forge_openblas_ubuntu_1804:
         DISTRIB: 'conda'
         CONDA_CHANNEL: 'conda-forge'
-        PYTHON_VERSION: '3.8'
+        PYTHON_VERSION: '3.9'
         BLAS: 'openblas'
         COVERAGE: 'false'
 
@@ -188,7 +188,7 @@ jobs:
       pylatest_conda_tensorflow:
         DISTRIB: 'conda-latest-tensorflow'
         CONDA_CHANNEL: 'conda-forge'
-        PYTHON_VERSION: '3.8'
+        PYTHON_VERSION: '3.9'
         TEST_DOCS: 'true'
         TEST_DOCSTRINGS: 'true'
         CHECK_WARNINGS: 'true'
@@ -214,7 +214,7 @@ jobs:
       pylatest_conda_keras:
         DISTRIB: 'conda-latest-keras'
         CONDA_CHANNEL: 'conda-forge'
-        PYTHON_VERSION: '3.8'
+        PYTHON_VERSION: '3.9'
         TEST_DOCS: 'true'
         TEST_DOCSTRINGS: 'true'
         CHECK_WARNINGS: 'true'
@@ -301,7 +301,7 @@ jobs:
       py38_conda_forge_mkl:
         DISTRIB: 'conda'
         CONDA_CHANNEL: 'conda-forge'
-        PYTHON_VERSION: '3.8'
+        PYTHON_VERSION: '3.10'
         CHECK_WARNINGS: 'true'
         PYTHON_ARCH: '64'
         PYTEST_VERSION: '*'

doc/ensemble.rst

@@ -33,8 +33,7 @@ data set, this classifier will favor the majority classes::
   >>> from sklearn.ensemble import BaggingClassifier
   >>> from sklearn.tree import DecisionTreeClassifier
   >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
-  >>> bc = BaggingClassifier(base_estimator=DecisionTreeClassifier(),
-  ...                        random_state=0)
+  >>> bc = BaggingClassifier(DecisionTreeClassifier(), random_state=0)
   >>> bc.fit(X_train, y_train) #doctest:
   BaggingClassifier(...)
   >>> y_pred = bc.predict(X_test)
@@ -50,7 +49,7 @@ sampling is controlled by the parameter `sampler` or the two parameters
 :class:`~imblearn.under_sampling.RandomUnderSampler`::
 
   >>> from imblearn.ensemble import BalancedBaggingClassifier
-  >>> bbc = BalancedBaggingClassifier(base_estimator=DecisionTreeClassifier(),
+  >>> bbc = BalancedBaggingClassifier(DecisionTreeClassifier(),
   ...                                 sampling_strategy='auto',
   ...                                 replacement=False,
   ...                                 random_state=0)

doc/whats_new/v0.12.rst

@@ -23,9 +23,13 @@ Compatibility
 
 - :class:`~imblearn.ensemble.BalancedRandomForestClassifier` now support missing values
   and monotonic constraints if scikit-learn >= 1.4 is installed.
+
 - :class:`~imblearn.pipeline.Pipeline` support metadata routing if scikit-learn >= 1.4
   is installed.
 
+- Compatibility with scikit-learn 1.4.
+  :pr:`1058` by :user:`Guillaume Lemaitre <glemaitre>`.
+
 Deprecations
 ............
 

imblearn/ensemble/_bagging.py

@@ -5,7 +5,6 @@
 # License: MIT
 
 import copy
-import inspect
 import numbers
 import warnings
 
@@ -15,6 +14,7 @@
 from sklearn.ensemble import BaggingClassifier
 from sklearn.ensemble._bagging import _parallel_decision_function
 from sklearn.ensemble._base import _partition_estimators
+from sklearn.exceptions import NotFittedError
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils import parse_version
 from sklearn.utils.validation import check_is_fitted
@@ -121,30 +121,13 @@ class BalancedBaggingClassifier(_ParamsValidationMixin, BaggingClassifier):
 
         .. versionadded:: 0.8
 
-    base_estimator : 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.
-
-        .. deprecated:: 0.10
-           `base_estimator` was renamed to `estimator` in version 0.10 and
-           will be removed in 0.12.
-
     Attributes
     ----------
     estimator_ : estimator
         The base estimator from which the ensemble is grown.
 
         .. versionadded:: 0.10
 
-    base_estimator_ : estimator
-        The base estimator from which the ensemble is grown.
-
-        .. deprecated:: 1.2
-           `base_estimator_` is deprecated in `scikit-learn` 1.2 and will be
-           removed in 1.4. Use `estimator_` instead. When the minimum version
-           of `scikit-learn` supported by `imbalanced-learn` will reach 1.4,
-           this attribute will be removed.
-
     n_features_ : int
         The number of features when `fit` is performed.
 
@@ -266,7 +249,7 @@ class BalancedBaggingClassifier(_ParamsValidationMixin, BaggingClassifier):
     """
 
     # make a deepcopy to not modify the original dictionary
-    if sklearn_version >= parse_version("1.3"):
+    if sklearn_version >= parse_version("1.4"):
         _parameter_constraints = copy.deepcopy(BaggingClassifier._parameter_constraints)
     else:
         _parameter_constraints = copy.deepcopy(_bagging_parameter_constraints)
@@ -283,6 +266,9 @@ class BalancedBaggingClassifier(_ParamsValidationMixin, BaggingClassifier):
             "sampler": [HasMethods(["fit_resample"]), None],
         }
     )
+    # TODO: remove when minimum supported version of scikit-learn is 1.4
+    if "base_estimator" in _parameter_constraints:
+        del _parameter_constraints["base_estimator"]
 
     def __init__(
         self,
@@ -301,18 +287,8 @@ def __init__(
         random_state=None,
         verbose=0,
         sampler=None,
-        base_estimator="deprecated",
     ):
-        # TODO: remove when supporting scikit-learn>=1.2
-        bagging_classifier_signature = inspect.signature(super().__init__)
-        estimator_params = {"base_estimator": base_estimator}
-        if "estimator" in bagging_classifier_signature.parameters:
-            estimator_params["estimator"] = estimator
-        else:
-            self.estimator = estimator
-
         super().__init__(
-            **estimator_params,
             n_estimators=n_estimators,
             max_samples=max_samples,
             max_features=max_features,
@@ -324,6 +300,7 @@ def __init__(
             random_state=random_state,
             verbose=verbose,
         )
+        self.estimator = estimator
         self.sampling_strategy = sampling_strategy
         self.replacement = replacement
         self.sampler = sampler
@@ -349,42 +326,17 @@ def _validate_y(self, y):
     def _validate_estimator(self, default=DecisionTreeClassifier()):
         """Check the estimator and the n_estimator attribute, set the
         `estimator_` attribute."""
-        if self.estimator is not None and (
-            self.base_estimator not in [None, "deprecated"]
-        ):
-            raise ValueError(
-                "Both `estimator` and `base_estimator` were set. Only set `estimator`."
-            )
-
         if self.estimator is not None:
-            base_estimator = clone(self.estimator)
-        elif self.base_estimator not in [None, "deprecated"]:
-            warnings.warn(
-                "`base_estimator` was renamed to `estimator` in version 0.10 and "
-                "will be removed in 0.12.",
-                FutureWarning,
-            )
-            base_estimator = clone(self.base_estimator)
+            estimator = clone(self.estimator)
         else:
-            base_estimator = clone(default)
+            estimator = clone(default)
 
         if self.sampler_._sampling_type != "bypass":
             self.sampler_.set_params(sampling_strategy=self._sampling_strategy)
 
-        self._estimator = Pipeline(
-            [("sampler", self.sampler_), ("classifier", base_estimator)]
+        self.estimator_ = Pipeline(
+            [("sampler", self.sampler_), ("classifier", estimator)]
         )
-        try:
-            # scikit-learn < 1.2
-            self.base_estimator_ = self._estimator
-        except AttributeError:
-            pass
-
-    # TODO: remove when supporting scikit-learn>=1.4
-    @property
-    def estimator_(self):
-        """Estimator used to grow the ensemble."""
-        return self._estimator
 
     # TODO: remove when supporting scikit-learn>=1.2
     @property
@@ -483,6 +435,22 @@ def decision_function(self, X):
 
         return decisions
 
+    @property
+    def base_estimator_(self):
+        """Attribute for older sklearn version compatibility."""
+        error = AttributeError(
+            f"{self.__class__.__name__} object has no attribute 'base_estimator_'."
+        )
+        if sklearn_version < parse_version("1.2"):
+            # The base class require to have the attribute defined. For scikit-learn
+            # > 1.2, we are going to raise an error.
+            try:
+                check_is_fitted(self)
+                return self.estimator_
+            except NotFittedError:
+                raise error
+        raise error
+
     def _more_tags(self):
         tags = super()._more_tags()
         tags_key = "_xfail_checks"