PRF Ridge - centering trick for cholesky solver - #34793
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β¦ to svd
use_no_center_cholesky matched on solver in ("auto", "cholesky") but didn't
check the array namespace. With array API dispatch to a non-numpy namespace,
solver="auto" silently resolves to "svd" instead of "cholesky" (see
resolve_solver), which needs X to actually be centered. This left X
uncentered while running svd, causing
test_cross_val_predict_array_api_compliance[...-Ridge] failures on
array_api_strict and torch in CI.
cakedev0
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Aug 26, 2026
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| elif is_X_sparse and fit_intercept: | ||
| # when `fit_intercept=True`, `mean_variance_axis` will be called on X | ||
| # and it requires csr/csc format | ||
| return ["csr", "csc"] |
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Note to reviewers: this addition is needed because I now pass check_input=False to _preprocess_data which would have done this logic.
cakedev0
marked this pull request as ready for review
August 27, 2026 06:55
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cc @antoinebaker, I was told you're the specialist of this kind of tricks π |
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Reference Issues/PRs
None, I started investigated for a speed gain while comparing speed/profiles with the scikit-learn-intelex (oneDAL) implementation.
What does this implement/fix? Explain your changes.
check_array(and to avoid a copy in the algebraic centering Cholesky path).Those relatively simple changes provide a speedups ranging from ~1.2x to 2x.
AI usage disclosure
I used AI assistance for:
Benchmarks
Benchmarks are run through the PR: probabl-ai/scikit-learn-benchmarks#31 (see bot comments with links to dashboards)
The benchmark cases are the same than the ones used to compare with scikit-learn-intelex, they are a mix of synthetic and real-world datasets.
On the intel-laptop, one case show a regression but it's a benchmark artifact: this case uses the dual path, which is not affected by this PR. And if you look at the granular timings, you'll see in both branches (main and this one) oscillate a lot.