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Deprecate penalty='none' in MBSGDClassifier/MBSGDRegressor/SGD#6926

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rapids-bot[bot] merged 6 commits intorapidsai:branch-25.08from
jcrist:mbsgd-fixups
Jun 25, 2025
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Deprecate penalty='none' in MBSGDClassifier/MBSGDRegressor/SGD#6926
rapids-bot[bot] merged 6 commits intorapidsai:branch-25.08from
jcrist:mbsgd-fixups

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@jcrist jcrist commented Jun 24, 2025

We'd already deprecated penalty='none' in favor of penalty=None for other estimators, just not these. Switching to penalty=None everywhere is more uniform and also improves compatibility with sklearn.

Also:

  • Un-xfails some MBSGDClassifier tests that don't need to be xfailed (and haven't for a while)
  • Fixes the LogisticRegression docstring on penalty to match the valid options

jcrist added 3 commits June 24, 2025 07:46
We'd already done this for other linear estimators, MBSGD* just got
missed.
No need to xfail these.
@jcrist jcrist self-assigned this Jun 24, 2025
@jcrist jcrist requested a review from a team as a code owner June 24, 2025 14:57
@jcrist jcrist added the Cython / Python Cython or Python issue label Jun 24, 2025
@jcrist jcrist requested review from dantegd and vyasr June 24, 2025 14:57
@jcrist jcrist added improvement Improvement / enhancement to an existing function non-breaking Non-breaking change labels Jun 24, 2025
@jcrist jcrist requested a review from betatim June 25, 2025 02:03
Comment thread python/cuml/cuml/linear_model/mbsgd_classifier.py Outdated
Comment thread python/cuml/cuml/linear_model/mbsgd_regressor.py Outdated
Comment thread python/cuml/cuml/solvers/sgd.pyx Outdated
Comment thread python/cuml/cuml/linear_model/mbsgd_classifier.py Outdated
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A few typos and a suggestion to make the pytest.warns more specific. Otherwise LGTM

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jcrist commented Jun 25, 2025

/merge

@csadorf csadorf added breaking Breaking change and removed non-breaking Non-breaking change labels Jun 25, 2025
@rapids-bot rapids-bot Bot merged commit b864362 into rapidsai:branch-25.08 Jun 25, 2025
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@jcrist jcrist deleted the mbsgd-fixups branch June 25, 2025 18:59
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4 participants