PRF: Optimize GB line search regression (RFC: is it worth it?) #4
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cakedev0 wants to merge 4 commits intooptim/gbt_update_terminal_regionsfrom
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PRF: Optimize GB line search regression (RFC: is it worth it?) #4cakedev0 wants to merge 4 commits intooptim/gbt_update_terminal_regionsfrom
cakedev0 wants to merge 4 commits intooptim/gbt_update_terminal_regionsfrom
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❌ Linting issuesThis PR is introducing linting issues. Here's a summary of the issues. Note that you can avoid having linting issues by enabling You can see the details of the linting issues under the
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Reference Issues/PRs
Follow-up from scikit-learn#32911
What does this implement/fix? Explain your changes.
loss.fit_intercept_only_by_idxfor AE, Pinball and Huber losses._weighted_quantile_by_idxthat I implemented insklearn/utils/stats.py, alongside_weighted_percentile: for now it lacks the optionaverage=True/False.loss.fit_intercept_only_by_idxinstead of the O(n^2) loop pattern in_update_terminal_regionsfor those lossesI looked into using this in HGB too, but I don't think it'll be useful.
AI usage disclosure
Almost fully generated by Copilot. At first glance, it looks fairly good honestly.
I used AI assistance for:
Seed-up
Good: 140s -> 4s on this "highest-speedup" example:
For AE, the speed-up is 60s -> 4s.
Is the increased code complexity worth the speed-up (for what is mostly an edge-case)?
I don't know.
The new code from this PR is a relatively nice piece code, and could maybe be used in other places. But it's quite a lot of code too...