Update n_init parameter for sklearn 1.2

PGijsbers · PGijsbers · commit 281f2772685c · 2023-02-11T21:22:45.000+01:00
diff --git a/tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py b/tests/test_extensions/test_sklearn_extension/test_sklearn_extension.py
@@ -338,14 +338,15 @@ def test_serialize_model_clustering(self):
                 )
             )
         else:
+            n_init = '"warn"' if LooseVersion(sklearn.__version__) >= "1.2" else "10"
             fixture_parameters = OrderedDict(
                 (
                     ("algorithm", '"lloyd"'),
                     ("copy_x", "true"),
                     ("init", '"k-means++"'),
                     ("max_iter", "300"),
                     ("n_clusters", "8"),
-                    ("n_init", "10"),
+                    ("n_init", n_init),
                     ("random_state", "null"),
                     ("tol", "0.0001"),
                     ("verbose", "0"),
@@ -358,13 +359,13 @@ def test_serialize_model_clustering(self):
         )
         structure = serialization.get_structure("name")
 
-        self.assertEqual(serialization.name, fixture_name)
-        self.assertEqual(serialization.class_name, fixture_name)
-        self.assertEqual(serialization.custom_name, fixture_short_name)
-        self.assertEqual(serialization.description, fixture_description)
-        self.assertEqual(serialization.parameters, fixture_parameters)
-        self.assertEqual(serialization.dependencies, version_fixture)
-        self.assertDictEqual(structure, fixture_structure)
+        assert serialization.name == fixture_name
+        assert serialization.class_name == fixture_name
+        assert serialization.custom_name == fixture_short_name
+        assert serialization.description == fixture_description
+        assert serialization.parameters == fixture_parameters
+        assert serialization.dependencies == version_fixture
+        assert structure == fixture_structure
 
     def test_serialize_model_with_subcomponent(self):
         model = sklearn.ensemble.AdaBoostClassifier(
