[Do NOT merge yet] MAINT: Apply sklearn1.8 deprecation of probability in SVM classifiers

.ci/scripts/setup_sklearn.sh

@@ -29,7 +29,7 @@ if [ "$sklearn_version" == "main" ]; then
     # install sklearn build dependencies
     pip install threadpoolctl joblib scipy
     # install sklearn from main branch of git repo
-    pip install git+https://github.com/scikit-learn/scikit-learn.git@main
+    pip install git+https://github.com/snath-xoc/scikit-learn.git@deprecate_porb_NuSVC
 else
     sed -i.bak -E "s/scikit-learn==[0-9a-zA-Z.]*/scikit-learn==${sklearn_version}.*/" requirements-test.txt
 fi

sklearnex/svm/nusvc.py

@@ -14,18 +14,15 @@
 # limitations under the License.
 # ==============================================================================
 
+import warnings
 from functools import wraps
 
 import numpy as np
 from sklearn.exceptions import NotFittedError
 from sklearn.metrics import accuracy_score
 from sklearn.svm import NuSVC as _sklearn_NuSVC
 from sklearn.utils.metaestimators import available_if
-from sklearn.utils.validation import (
-    _deprecate_positional_args,
-    check_array,
-    check_is_fitted,
-)
+from sklearn.utils.validation import _deprecate_positional_args, check_is_fitted
 
 from daal4py.sklearn._n_jobs_support import control_n_jobs
 from daal4py.sklearn._utils import sklearn_check_version
@@ -56,7 +53,7 @@ def __init__(
         gamma="scale",
         coef0=0.0,
         shrinking=True,
-        probability=False,
+        probability="deprecated" if sklearn_check_version("1.8") else False,
         tol=1e-3,
         cache_size=200,
         class_weight=None,
@@ -218,7 +215,16 @@ def _onedal_fit(self, X, y, sample_weight=None, queue=None):
         self._onedal_estimator = onedal_NuSVC(**onedal_params)
         self._onedal_estimator.fit(X, y, weights, queue=queue)
 
-        if self.probability:
+        probability = self.probability
+        if sklearn_check_version("1.8") and self.probability != "deprecated":
+            warnings.warn(
+                "parameter `probability` will be deprecated in version 1.8, "
+                "use `CalibratedClassifierCV(NuSVC(), ensemble=False)` "
+                "instead of `NuSVC(probability=True)`",
+                FutureWarning,
+            )
+            probability = False
+        if probability:
             self._fit_proba(
                 X,
                 y,

sklearnex/svm/svc.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 # ==============================================================================
 
+import warnings
 from functools import wraps
 
 import numpy as np
@@ -22,11 +23,7 @@
 from sklearn.metrics import accuracy_score
 from sklearn.svm import SVC as _sklearn_SVC
 from sklearn.utils.metaestimators import available_if
-from sklearn.utils.validation import (
-    _deprecate_positional_args,
-    check_array,
-    check_is_fitted,
-)
+from sklearn.utils.validation import _deprecate_positional_args, check_is_fitted
 
 from daal4py.sklearn._n_jobs_support import control_n_jobs
 from daal4py.sklearn._utils import sklearn_check_version
@@ -58,7 +55,7 @@ def __init__(
         gamma="scale",
         coef0=0.0,
         shrinking=True,
-        probability=False,
+        probability="deprecated" if sklearn_check_version("1.8") else False,
         tol=1e-3,
         cache_size=200,
         class_weight=None,
@@ -248,7 +245,16 @@ def _onedal_fit(self, X, y, sample_weight=None, queue=None):
         self._onedal_estimator = onedal_SVC(**onedal_params)
         self._onedal_estimator.fit(X, y, weights, queue=queue)
 
-        if self.probability:
+        probability = self.probability
+        if sklearn_check_version("1.8") and self.probability != "deprecated":
+            warnings.warn(
+                "parameter `probability` will be deprecated in version 1.8, "
+                "use `CalibratedClassifierCV(SVC(), ensemble=False)` "
+                "instead of `SVC(probability=True)`",
+                FutureWarning,
+            )
+            probability = False
+        if probability:
             self._fit_proba(
                 X,
                 y,