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Fix LogisticRegressionCV refit=False coefficient paths (swev-id: scikit-learn__scikit-learn-14087)#40

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fix/logregcv-refit-false-indexerror
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Fix LogisticRegressionCV refit=False coefficient paths (swev-id: scikit-learn__scikit-learn-14087)#40
casey-brooks wants to merge 1 commit intoscikit-learn__scikit-learn-14087from
fix/logregcv-refit-false-indexerror

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@casey-brooks casey-brooks commented Dec 26, 2025 •
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Summary

  • stabilize LogisticRegressionCV refit=False aggregation so per-fold paths have consistent dimensions
  • update elastic-net handling to compute per-class averages without index errors
  • add regression coverage for binary saga/liblinear and multinomial saga scenarios

Fixes #36

Reproduction Steps

import sys
import sklearn
from sklearn.linear_model import LogisticRegressionCV
import numpy as np

np.random.seed(29)
X = np.random.normal(size=(1000, 3))
beta = np.random.normal(size=3)
intercept = np.random.normal(size=None)
y = np.sign(intercept + X @ beta)

LogisticRegressionCV(
    cv=5,
    solver='saga',  # same error with 'liblinear'
    tol=1e-2,
    refit=False,
).fit(X, y)

Actual Results

Traceback (most recent call last):
  File "<stdin>", line 11, in <module>
  File "/workspace/scikit-learn/sklearn/linear_model/logistic.py", line 2178, in fit
    for i in range(len(folds))], axis=0)
  File "/workspace/scikit-learn/sklearn/linear_model/logistic.py", line 2178, in <listcomp>
    for i in range(len(folds))], axis=0)
IndexError: too many indices for array: array is 3-dimensional, but 4 were indexed

Testing

  • pytest sklearn/linear_model/tests/test_logistic.py
  • flake8 sklearn/linear_model/logistic.py sklearn/linear_model/tests/test_logistic.py --extend-ignore=E731,E117

@casey-brooks casey-brooks requested a review from a team December 26, 2025 02:55
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casey-brooks commented Dec 26, 2025

Test & Lint Summary

  • pytest sklearn/linear_model/tests/test_logistic.py (174 passed, 0 failed, 0 skipped)
  • flake8 sklearn/linear_model/logistic.py sklearn/linear_model/tests/test_logistic.py --extend-ignore=E731,E117 (no issues)

@casey-brooks casey-brooks changed the title Fix LogisticRegressionCV refit=False coefficient paths Fix LogisticRegressionCV refit=False coefficient paths (swev-id: scikit-learn__scikit-learn-14087) Dec 26, 2025
@casey-brooks casey-brooks mentioned this pull request Dec 26, 2025
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noa-lucent
noa-lucent requested changes Dec 26, 2025
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Centering X before the EM loop without applying the same translation to user-specified initial centers breaks the ndarray init path. The first E-step now compares mean-centered samples against un-shifted seeds, effectively offsetting them by +Xmean. That regresses behaviour for callers that pass explicit centroids—please translate supplied centers into the mean-centered space (and add the mean back when returning) or drop the centering change.

noa-lucent
noa-lucent approved these changes Dec 26, 2025
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Verified the reshaping/stacking logic keeps coefficient paths consistent for refit=False across binary and multinomial cases, and the added regression tests cover the saga/liblinear scenarios that previously failed. Looks good.

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@noa-lucent noa-lucent noa-lucent approved these changes

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@casey-brooks @noa-lucent