PRF Mitigation of poor scalability of HGB - #34912
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Non regression test for the OverflowError fixed in 68e1aae: find_best_split only populated n_subsampled_features entries of the split_infos buffer, but scanned all n_allowed_features when picking the best split, reading uninitialized memory whenever max_features < 1. Co-Authored-By: Claude Sonnet 5 <[email protected]>
…positive_heuristics
cakedev0
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Sep 9, 2026
| "HistGradientBoosting", | ||
| [HistGradientBoostingClassifier, HistGradientBoostingRegressor], | ||
| ) | ||
| def test_max_features_less_than_one_does_not_crash(HistGradientBoosting): |
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Closed in favor of #34935 |
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WIP: still cleaning the diff, fixing tests, ...
Finally, I have something that has a uniformly positive impact on the 8 scenarios I'm testing:
(x Libgomp & libomp for each of those cases)
Reference Issues/PRs
Implements item 4 partially, and item 5 of #34764 (comment)
And I'd say it also closes #14306. Though this issues does have some interesting idea that are not implemented here, especially parallelism over block of samples x features for the histogram building. Still, benchmarks show HGB does continue to scale until 16-32 threads for many medium/big datasets (as long as active wait is enabled). I personally would consider that good enough ^^
What does this implement/fix? Explain your changes.
AI usage disclosure: mostly not
Benchmarks
probabl-ai/scikit-learn-benchmarks#74
Except one case regressing on the GNR that is probably due to numa placement (see probabl-ai/scikit-learn-benchmarks#80), no regression further than ~5% and some very neat improvements (especially on the GNR).
You'll see the no-active-wait set-ups still scale very counter-productively in many cases, I plan to try tackling that in a follow-up PR. Current plan:
set_kmp_block_timeto actually enable active wait (maybe dangerous if inside outer parallelism)For the scalability on machines like the GNR, it's still not perfect but I don't think it's worth improving more than that.