scikit-learn · jeremiedbb · Jun 29, 2025 · Jun 24, 2025 · Jun 24, 2025 · Jun 24, 2025
diff --git a/sklearn/linear_model/_glm/_newton_solver.py b/sklearn/linear_model/_glm/_newton_solver.py
@@ -14,6 +14,7 @@
 
 from ..._loss.loss import HalfSquaredError
 from ...exceptions import ConvergenceWarning
+from ...utils.fixes import _get_additional_lbfgs_options_dict
 from ...utils.optimize import _check_optimize_result
 from .._linear_loss import LinearModelLoss
 
@@ -187,9 +188,9 @@ def fallback_lbfgs_solve(self, X, y, sample_weight):
             options={
                 "maxiter": max_iter,
                 "maxls": 50,  # default is 20
-                "iprint": self.verbose - 1,
                 "gtol": self.tol,
                 "ftol": 64 * np.finfo(np.float64).eps,
+                **_get_additional_lbfgs_options_dict("iprint", self.verbose - 1),
             },
             args=(X, y, sample_weight, self.l2_reg_strength, self.n_threads),
         )

diff --git a/sklearn/linear_model/_glm/glm.py b/sklearn/linear_model/_glm/glm.py
@@ -21,6 +21,7 @@
 from ...utils import check_array
 from ...utils._openmp_helpers import _openmp_effective_n_threads
 from ...utils._param_validation import Hidden, Interval, StrOptions
+from ...utils.fixes import _get_additional_lbfgs_options_dict
 from ...utils.optimize import _check_optimize_result
 from ...utils.validation import _check_sample_weight, check_is_fitted, validate_data
 from .._linear_loss import LinearModelLoss
@@ -273,12 +274,12 @@ def fit(self, X, y, sample_weight=None):
                 options={
                     "maxiter": self.max_iter,
                     "maxls": 50,  # default is 20
-                    "iprint": self.verbose - 1,
                     "gtol": self.tol,
                     # The constant 64 was found empirically to pass the test suite.
                     # The point is that ftol is very small, but a bit larger than
                     # machine precision for float64, which is the dtype used by lbfgs.
                     "ftol": 64 * np.finfo(float).eps,
+                    **_get_additional_lbfgs_options_dict("iprint", self.verbose - 1),
                 },
                 args=(X, y, sample_weight, l2_reg_strength, n_threads),
             )

diff --git a/sklearn/linear_model/_huber.py b/sklearn/linear_model/_huber.py
@@ -10,6 +10,7 @@
 from ..utils._mask import axis0_safe_slice
 from ..utils._param_validation import Interval
 from ..utils.extmath import safe_sparse_dot
+from ..utils.fixes import _get_additional_lbfgs_options_dict
 from ..utils.optimize import _check_optimize_result
 from ..utils.validation import _check_sample_weight, validate_data
 from ._base import LinearModel
@@ -329,7 +330,11 @@ def fit(self, X, y, sample_weight=None):
             method="L-BFGS-B",
             jac=True,
             args=(X, y, self.epsilon, self.alpha, sample_weight),
-            options={"maxiter": self.max_iter, "gtol": self.tol, "iprint": -1},
+            options={
+                "maxiter": self.max_iter,
+                "gtol": self.tol,
+                **_get_additional_lbfgs_options_dict("iprint", -1),
+            },
             bounds=bounds,
         )
 

diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py
@@ -30,6 +30,7 @@
 )
 from ..utils._param_validation import Hidden, Interval, StrOptions
 from ..utils.extmath import row_norms, softmax
+from ..utils.fixes import _get_additional_lbfgs_options_dict
 from ..utils.metadata_routing import (
     MetadataRouter,
     MethodMapping,
@@ -464,9 +465,9 @@ def _logistic_regression_path(
                 options={
                     "maxiter": max_iter,
                     "maxls": 50,  # default is 20
-                    "iprint": iprint,
                     "gtol": tol,
                     "ftol": 64 * np.finfo(float).eps,
+                    **_get_additional_lbfgs_options_dict("iprint", iprint),
                 },
             )
             n_iter_i = _check_optimize_result(

diff --git a/sklearn/neighbors/_nca.py b/sklearn/neighbors/_nca.py
@@ -25,6 +25,7 @@
 from ..preprocessing import LabelEncoder
 from ..utils._param_validation import Interval, StrOptions
 from ..utils.extmath import softmax
+from ..utils.fixes import _get_additional_lbfgs_options_dict
 from ..utils.multiclass import check_classification_targets
 from ..utils.random import check_random_state
 from ..utils.validation import check_array, check_is_fitted, validate_data
@@ -312,7 +313,10 @@ def fit(self, X, y):
             "jac": True,
             "x0": transformation,
             "tol": self.tol,
-            "options": dict(maxiter=self.max_iter, disp=disp),
+            "options": dict(
+                maxiter=self.max_iter,
+                **_get_additional_lbfgs_options_dict("disp", disp),
+            ),
             "callback": self._callback,
         }
 

diff --git a/sklearn/neural_network/_multilayer_perceptron.py b/sklearn/neural_network/_multilayer_perceptron.py
@@ -31,6 +31,7 @@
 )
 from ..utils._param_validation import Interval, Options, StrOptions
 from ..utils.extmath import safe_sparse_dot
+from ..utils.fixes import _get_additional_lbfgs_options_dict
 from ..utils.metaestimators import available_if
 from ..utils.multiclass import (
     _check_partial_fit_first_call,
@@ -585,8 +586,8 @@ def _fit_lbfgs(
             options={
                 "maxfun": self.max_fun,
                 "maxiter": self.max_iter,
-                "iprint": iprint,
                 "gtol": self.tol,
+                **_get_additional_lbfgs_options_dict("iprint", iprint),
             },
             args=(
                 X,

diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
@@ -394,6 +394,16 @@ def _in_unstable_openblas_configuration():
     return False
 
 
+# TODO: Remove when Scipy 1.15 is the minimum supported version. In scipy 1.15,
+# the internal info details (via 'iprint' and 'disp' options) were dropped,
+# following the LBFGS rewrite from Fortran to C, see
+# https://github.com/scipy/scipy/issues/23186#issuecomment-2987801035. For
+# scipy 1.15, 'iprint' and 'disp' have no effect and for scipy >= 1.16 a
+# DeprecationWarning is emitted.
+def _get_additional_lbfgs_options_dict(key, value):
+    return {} if sp_version >= parse_version("1.15") else {key: value}
+
+
 # TODO(pyarrow): Remove when minimum pyarrow version is 17.0.0
 PYARROW_VERSION_BELOW_17 = False
 try: