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MAINT: Deselect logistic regression cases that fail from small numeric differences #2757

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Merged
merged 3 commits into from
Nov 4, 2025
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MAINT: Deselect logistic regression cases that fail from small numeric differences #2757

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5db1752
deselect cases that fail due to expected small numeric differences
david-cortes-intel Oct 29, 2025
3133e7e
deduplicate
david-cortes-intel Oct 30, 2025
dcee7e6
Update deselected_tests.yaml
ethanglaser Nov 4, 2025
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4 changes: 3 additions & 1 deletion deselected_tests.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -295,7 +295,7 @@ deselected_tests:
- ensemble/tests/test_bagging.py::test_estimators_samples >=1.4
- ensemble/tests/test_voting.py::test_sample_weight >=1.4
- svm/tests/test_svm.py::test_auto_weight >=1.4
- tests/test_calibration.py::test_calibrated_classifier_cv_double_sample_weights_equivalence >=1.4
- tests/test_calibration.py::test_calibrated_classifier_cv_double_sample_weights_equivalence >=1
- tests/test_calibration.py::test_calibrated_classifier_cv_zeros_sample_weights_equivalence >=1.4
- tests/test_common.py::test_estimators[LogisticRegression()-check_sample_weights_invariance(kind=ones)] >=1.4
- tests/test_common.py::test_estimators[LogisticRegression()-check_sample_weights_invariance(kind=zeros)] >=1.4
Expand Down Expand Up @@ -374,6 +374,8 @@ deselected_tests:
- ensemble/tests/test_voting.py::test_sample_weight >=1.0,<1.1
- tests/test_multioutput.py::test_multiclass_multioutput_estimator_predict_proba
- model_selection/tests/test_search.py::test_search_cv_sample_weight_equivalence[estimator0]
- tests/test_common.py::test_estimators[StackingClassifier(estimators=[('est1',LogisticRegression(C=0.1)),('est2',LogisticRegression(C=1))])-check_sample_weights_invariance(kind=ones)] <1.1
- tests/test_common.py::test_estimators[StackingClassifier(estimators=[('est1',LogisticRegression(C=0.1)),('est2',LogisticRegression(C=1))])-check_sample_weights_invariance(kind=zeros)] <1.1

# Scikit-learn does not constraint multinomial logistic intercepts to sum to zero.
# Softmax function is invariant to additions by a constant, so even though the numbers
Expand Down
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