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MNT Deprecate n_jobs in LogisticRegression #32742

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Merged
jeremiedbb merged 3 commits into from
Nov 19, 2025
Merged

MNT Deprecate n_jobs in LogisticRegression #32742

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cceee43
MNT Deprecate n_jobs in LogisticRegression
lesteve Nov 19, 2025
99affc9
Remove warning that is not useful anymore since n_jobs never has an e…
lesteve Nov 19, 2025
ec021f4
[azure parallel] trigger CI
lesteve Nov 19, 2025
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23 changes: 13 additions & 10 deletions sklearn/linear_model/_logistic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,6 @@
from numbers import Integral, Real

import numpy as np
from joblib import effective_n_jobs
from scipy import optimize

from sklearn._loss.loss import HalfBinomialLoss, HalfMultinomialLoss
Expand Down Expand Up @@ -894,7 +893,10 @@ class of problems.
*warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.

n_jobs : int, default=None
Not used at the moment.
Does not have any effect.

.. deprecated:: 1.8
`n_jobs` is deprecated in version 1.8 and will be removed in 1.10.

l1_ratio : float, default=None
The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
Expand Down Expand Up @@ -1090,6 +1092,13 @@ def fit(self, X, y, sample_weight=None):
"(penalty={})".format(self.penalty)
)

msg = (
"'n_jobs' has no effect since 1.8 and will be removed in 1.10. "
f"You provided 'n_jobs={self.n_jobs}', please leave it unspecified."
)
if self.n_jobs is not None:
warnings.warn(msg, category=FutureWarning)

if self.penalty == "elasticnet" and self.l1_ratio is None:
raise ValueError("l1_ratio must be specified when penalty is elasticnet.")

Expand Down Expand Up @@ -1139,12 +1148,6 @@ def fit(self, X, y, sample_weight=None):
"value > 1e30 results in a frozen fit. Please choose another "
"solver or rescale the input X."
)
if effective_n_jobs(self.n_jobs) != 1:
warnings.warn(
"'n_jobs' > 1 does not have any effect when"
" 'solver' is set to 'liblinear'. Got 'n_jobs'"
" = {}.".format(effective_n_jobs(self.n_jobs))
)
self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
X,
y,
Expand Down Expand Up @@ -1183,8 +1186,8 @@ def fit(self, X, y, sample_weight=None):
warm_start_coef, self.intercept_[:, np.newaxis], axis=1
)

# TODO: deprecate n_jobs since it's not used anymore and enable multi-threading
# if benchmarks show a positive effect.
# TODO: enable multi-threading if benchmarks show a positive effect,
# see https://github.com/scikit-learn/scikit-learn/issues/32162
n_threads = 1

coefs, _, n_iter = _logistic_regression_path(
Expand Down
25 changes: 10 additions & 15 deletions sklearn/linear_model/tests/test_logistic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@
from sklearn.preprocessing import LabelEncoder, StandardScaler, scale
from sklearn.svm import l1_min_c
from sklearn.utils import compute_class_weight, shuffle
from sklearn.utils._testing import ignore_warnings, skip_if_no_parallel
from sklearn.utils._testing import ignore_warnings
from sklearn.utils.fixes import _IS_32BIT, COO_CONTAINERS, CSR_CONTAINERS

pytestmark = pytest.mark.filterwarnings(
Expand Down Expand Up @@ -119,20 +119,6 @@ def __call__(self, model, X, y, sample_weight=None):
assert mock_scorer.calls == 1


@skip_if_no_parallel
def test_lr_liblinear_warning():
X, y = make_classification(random_state=0)

lr = LogisticRegression(solver="liblinear", n_jobs=2)
warning_message = (
"'n_jobs' > 1 does not have any effect when"
" 'solver' is set to 'liblinear'. Got 'n_jobs'"
" = 2."
)
with pytest.warns(UserWarning, match=warning_message):
lr.fit(X, y)


@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_predict_3_classes(csr_container):
check_predictions(LogisticRegression(C=10), X, Y2)
Expand Down Expand Up @@ -2619,3 +2605,12 @@ def test_logisticregressioncv_warns_with_use_legacy_attributes():
msg = "The default value of use_legacy_attributes will change from True"
with pytest.warns(FutureWarning, match=msg):
lr.fit(X, y)


# TODO(1.10): remove this test when n_jobs gets removed
def test_logisticregression_warns_with_n_jobs():
X, y = make_classification(n_classes=3, n_samples=50, n_informative=6)
lr = LogisticRegression(n_jobs=1)
msg = "'n_jobs' has no effect"
with pytest.warns(FutureWarning, match=msg):
lr.fit(X, y)
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