MAINT avoid warning deprecation sklearn 1.5 (#1084)

Author: glemaitre

doc/under_sampling.rst

@@ -497,8 +497,7 @@ The class can be used as::
   >>> from sklearn.linear_model import LogisticRegression
   >>> from imblearn.under_sampling import InstanceHardnessThreshold
   >>> iht = InstanceHardnessThreshold(random_state=0,
-  ...                                 estimator=LogisticRegression(
-  ...                                     solver='lbfgs', multi_class='auto'))
+  ...                                 estimator=LogisticRegression())
   >>> X_resampled, y_resampled = iht.fit_resample(X, y)
   >>> print(sorted(Counter(y_resampled).items()))
   [(0, 64), (1, 64), (2, 64)]

doc/whats_new/v0.12.rst

@@ -12,7 +12,7 @@ Compatibility
 .............
 
 - Compatibility with scikit-learn 1.5
-  :pr:`1074` by :user:`Guillaume Lemaitre <glemaitre>`.
+  :pr:`1074` and :pr:`1084` by :user:`Guillaume Lemaitre <glemaitre>`.
 
 Version 0.12.2
 ==============

examples/applications/plot_outlier_rejections.py

@@ -109,12 +109,12 @@ def outlier_rejection(X, y):
 
 pipe = make_pipeline(
     FunctionSampler(func=outlier_rejection),
-    LogisticRegression(solver="lbfgs", multi_class="auto", random_state=rng),
+    LogisticRegression(random_state=rng),
 )
 y_pred = pipe.fit(X_train, y_train).predict(X_test)
 print(classification_report(y_test, y_pred))
 
-clf = LogisticRegression(solver="lbfgs", multi_class="auto", random_state=rng)
+clf = LogisticRegression(random_state=rng)
 y_pred = clf.fit(X_train, y_train).predict(X_test)
 print(classification_report(y_test, y_pred))
 

imblearn/ensemble/tests/test_bagging.py

@@ -174,7 +174,7 @@ def test_probability():
 
         # Degenerate case, where some classes are missing
         ensemble = BalancedBaggingClassifier(
-            estimator=LogisticRegression(solver="lbfgs", multi_class="auto"),
+            estimator=LogisticRegression(solver="lbfgs"),
             random_state=0,
             max_samples=5,
         )
@@ -435,7 +435,7 @@ def test_estimators_samples():
     # remap the y outside of the BalancedBaggingclassifier
     # _, y = np.unique(y, return_inverse=True)
     bagging = BalancedBaggingClassifier(
-        LogisticRegression(solver="lbfgs", multi_class="auto"),
+        LogisticRegression(),
         max_samples=0.5,
         max_features=0.5,
         random_state=1,

imblearn/tests/test_docstring_parameters.py

@@ -11,7 +11,6 @@
 import pytest
 from sklearn.datasets import make_classification
 from sklearn.linear_model import LogisticRegression
-from sklearn.utils import IS_PYPY
 from sklearn.utils._testing import (
     _get_func_name,
     check_docstring_parameters,
@@ -70,7 +69,6 @@
 # Python 3.7
 @pytest.mark.filterwarnings("ignore::FutureWarning")
 @pytest.mark.filterwarnings("ignore::DeprecationWarning")
-@pytest.mark.skipif(IS_PYPY, reason="test segfaults on PyPy")
 def test_docstring_parameters():
     # Test module docstring formatting
 
@@ -154,9 +152,6 @@ def test_tabs():
     for importer, modname, ispkg in walk_packages(
         imblearn.__path__, prefix="imblearn."
     ):
-        if IS_PYPY:
-            continue
-
         # because we don't import
         mod = importlib.import_module(modname)
 

imblearn/tests/test_pipeline.py

@@ -272,7 +272,7 @@ def test_pipeline_methods_anova():
     X = iris.data
     y = iris.target
     # Test with Anova + LogisticRegression
-    clf = LogisticRegression(solver="lbfgs", multi_class="auto")
+    clf = LogisticRegression()
     filter1 = SelectKBest(f_classif, k=2)
     pipe = Pipeline([("anova", filter1), ("logistic", clf)])
     pipe.fit(X, y)
@@ -639,7 +639,7 @@ def test_classes_property():
 
     clf = make_pipeline(
         SelectKBest(k=1),
-        LogisticRegression(solver="lbfgs", multi_class="auto", random_state=0),
+        LogisticRegression(),
     )
     with raises(AttributeError):
         getattr(clf, "classes_")