chore: fix sklearn 1.5 compatibility (#992)

pyproject.toml

@@ -172,6 +172,7 @@ filterwarnings = [
     "ignore:read_text is deprecated.*:DeprecationWarning",
     "ignore:open_binary is deprecated.*:DeprecationWarning",
     "ignore:pkg_resources is deprecated as an API.*:DeprecationWarning",
+    "ignore:'multi_class' was deprecated in version 1.5 and will be removed in 1.7.*:FutureWarning",
 ]
 
 [tool.semantic_release]

src/concrete/ml/sklearn/base.py

@@ -1691,16 +1691,11 @@ def from_sklearn_model(
         # Extract scikit-learn's initialization parameters
         init_params = sklearn_model.get_params()
 
-        # Remove deprecated parameters
-        deprecated = "deprecated"
-        init_params = {k: v for k, v in init_params.items() if v != deprecated}
-
-        # Ensure compatibility for both sklearn 1.1 and >=1.4
-        # This parameter was removed in 1.4. If this package is installed
+        # Ensure compatibility for both sklearn 1.1 and >=1.5
+        # This parameter was removed in 1.5. If this package is installed
         # with sklearn 1.1 which has it, then remove it when
-        # instantiating the 1.4 API compatible Concrete ML model
+        # instantiating the 1.5 API compatible Concrete ML model
         init_params.pop("normalize", None)
-        init_params.pop("multi_class", None)
 
         # Instantiate the Concrete ML model and update initialization parameters
         # This update is necessary as we currently store scikit-learn attributes in Concrete ML

src/concrete/ml/sklearn/glm.py

@@ -144,7 +144,7 @@ def get_sklearn_params(self, deep: bool = True) -> dict:
 
         # Remove the parameters added by Concrete ML
         params.pop("n_bits", None)
-        # Remove sklearn 1.4 parameter when using sklearn 1.1
+        # Remove sklearn 1.5 parameter when using sklearn 1.1
         if "1.1." in sklearn.__version__:
             params.pop("solver", None)  # pragma: no cover
 

src/concrete/ml/sklearn/linear_model.py

@@ -1637,6 +1637,7 @@ def __init__(
         C=1.0,
         fit_intercept=True,
         intercept_scaling=1,
+        multi_class="auto",
         class_weight=None,
         random_state=None,
         solver="lbfgs",
@@ -1655,6 +1656,7 @@ def __init__(
         self.C = C
         self.fit_intercept = fit_intercept
         self.intercept_scaling = intercept_scaling
+        self.multi_class = multi_class
         self.class_weight = class_weight
         self.random_state = random_state
         self.solver = solver
@@ -1689,6 +1691,7 @@ def dump_dict(self) -> Dict[str, Any]:
         metadata["C"] = self.C
         metadata["fit_intercept"] = self.fit_intercept
         metadata["intercept_scaling"] = self.intercept_scaling
+        metadata["multi_class"] = self.multi_class
         metadata["class_weight"] = self.class_weight
         metadata["random_state"] = self.random_state
         metadata["solver"] = self.solver
@@ -1725,6 +1728,7 @@ def load_dict(cls, metadata: Dict):
         obj.C = metadata["C"]
         obj.fit_intercept = metadata["fit_intercept"]
         obj.intercept_scaling = metadata["intercept_scaling"]
+        obj.multi_class = metadata["multi_class"]
         obj.class_weight = metadata["class_weight"]
         obj.random_state = metadata["random_state"]
         obj.solver = metadata["solver"]

tests/sklearn/test_sklearn_models.py

@@ -2244,7 +2244,7 @@ def test_initialization_variables_and_defaults_match(
     """
     if "1.1." in sklearn.__version__:
         pytest.skip(
-            "Concrete ML currently implements sklearn 1.4 API"
+            "Concrete ML currently implements sklearn 1.5 API"
             f" skipping this test on version {sklearn.__version__}"
         )