MAINT: More adjustments for changes in sklearn1.8 LogisticRegression

daal4py/sklearn/linear_model/logistic_path.py

@@ -102,7 +102,7 @@ def __logistic_regression_path(
 
     # Comment 2025-08-04: this file might have dead code paths from unsupported solvers.
     # It appears to have initially been a copy-paste of scikit-learn with a few additions
-    # for varying levels of offloading to oneDAL, but later on the check above was added that
+    # for varying levels of offloading to oneDAL, but later on a check was added that
     # calls 'lr_path_original' early on when it won't end up offloading anything to oneDAL.
     # Some parts of the file have been selectively updated since the initial copy-paste
     # to reflect newer additions to sklearn, but they are not synch. The rest of the file
@@ -469,6 +469,16 @@ def logistic_regression_path(*args, **kwargs):
             (not sparse.issparse(args[0]), "X is sparse. Sparse input is not supported."),
             (kwargs["sample_weight"] is None, "Sample weights are not supported."),
             (kwargs["class_weight"] is None, "Class weights are not supported."),
+            (
+                kwargs["penalty"]
+                in (["l2", "deprecated"] if sklearn_check_version("1.8") else ["l2"]),
+                "Penalties other than l2 are not supported.",
+            ),
+            (not kwargs["l1_ratio"], "L1 regularization is not supported."),
+            (
+                not (kwargs["solver"] == "newton-cg" and not kwargs["fit_intercept"]),
+                "'newton-cg' solver without intercept is not supported.",
+            ),
         ]
     )
     if not _dal_ready:

doc/sources/algorithms.rst

@@ -74,9 +74,11 @@ Classification
      - All parameters are supported except:
 
        - ``solver`` not in [``'lbfgs'``, ``'newton-cg'``]
-       - ``penalty`` in [``'l1'``, ``'elasticnet'``]
+       - ``l1_ratio`` != ``0``
+       - ``dual`` = ``True``
        - ``sample_weight`` != ``None``
        - ``class_weight`` != ``None``
+       - Solver ``'newton-cg'`` with ``fit_intercept`` = ``False`` is not supported
      - Sparse data is not supported.
 
 Regression
@@ -323,12 +325,10 @@ Classification
        - ``solver`` != `'newton-cg'`
        - ``class_weight`` != `None`
        - ``sample_weight`` != `None`
-       - ``penalty`` != `'l2'`
-       - ``dual`` = `True`
+       - ``dual`` = ``True``
        - ``intercept_scaling`` != `1`
-       - ``multi_class`` = `'multinomial'`
-       - ``warm_start`` = `True`
-       - ``l1_ratio`` != 0 and ``l1_ratio`` != ``None``
+       - ``warm_start`` = ``True``
+       - ``l1_ratio`` != ``0``
        - Only binary classification is supported
      - No limitations
 
@@ -525,12 +525,10 @@ Classification
        - ``solver`` != `'newton-cg'`
        - ``class_weight`` != `None`
        - ``sample_weight`` != `None`
-       - ``penalty`` != `'l2'`
-       - ``dual`` = `True`
+       - ``dual`` = ``True``
        - ``intercept_scaling`` != `1`
-       - ``multi_class`` != `'multinomial'`
-       - ``warm_start`` = `True`
-       - ``l1_ratio`` != `None`
+       - ``warm_start`` = ``True``
+       - ``l1_ratio`` != ``0``
        - Only binary classification is supported
      - No limitations
 

sklearnex/linear_model/logistic_regression.py

@@ -15,7 +15,6 @@
 # ===============================================================================
 
 import logging
-from abc import ABC
 
 from daal4py.sklearn._utils import daal_check_version
 from daal4py.sklearn.linear_model.logistic_path import (
@@ -28,7 +27,7 @@
     from sklearn.linear_model import LogisticRegression as _sklearn_LogisticRegression
     from sklearn.metrics import accuracy_score
     from sklearn.utils.multiclass import type_of_target
-    from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
+    from sklearn.utils.validation import check_is_fitted
 
     from daal4py.sklearn._n_jobs_support import control_n_jobs
     from daal4py.sklearn._utils import sklearn_check_version
@@ -66,11 +65,12 @@ class LogisticRegression(oneDALEstimator, _sklearn_LogisticRegression):
 
             def __init__(
                 self,
-                penalty="l2",
+                penalty="deprecated",
                 *,
+                C=1.0,
+                l1_ratio=0.0,
                 dual=False,
                 tol=1e-4,
-                C=1.0,
                 fit_intercept=True,
                 intercept_scaling=1,
                 class_weight=None,
@@ -80,7 +80,6 @@ def __init__(
                 verbose=0,
                 warm_start=False,
                 n_jobs=None,
-                l1_ratio=None,
             ):
                 super().__init__(
                     penalty=penalty,
@@ -256,7 +255,15 @@ def _onedal_gpu_fit_supported(self, method_name, *data):
             )
             patching_status.and_conditions(
                 [
-                    (self.penalty == "l2", "Only l2 penalty is supported."),
+                    (
+                        self.penalty
+                        in (
+                            ["l2", "deprecated"]
+                            if sklearn_check_version("1.8")
+                            else ["l2"]
+                        ),
+                        "Only l2 penalty is supported.",
+                    ),
                     (self.dual == False, "dual=True is not supported."),
                     (
                         self.intercept_scaling == 1,
@@ -271,7 +278,7 @@ def _onedal_gpu_fit_supported(self, method_name, *data):
                     ),
                     (
                         not self.l1_ratio,
-                        "l1 ratio is not supported.",
+                        "l1 penalty is not supported.",
                     ),
                     (sample_weight is None, "Sample weight is not supported."),
                     (