MAINT | ENH Change default value of subsample + allow for all strategies in KBinsDiscretizer - #26424
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ping @glemaitre |
thomasjpfan
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I like how the code is simplified by supporting all strategies. Although, enabling subsampling for all strategies is backward incompatible. The safer path is to go through another deprecation cycle but for the other strategies.
| .. versionadded:: 0.24 | ||
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| subsample : int or None (default='warn') | ||
| subsample : int or None, default=200_000 |
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Now that subsample is enabled for all strategies, we need to adjust the docstring to not focus on quantile.
It's true that Alternatively, what do you think about setting the default to "auto" which would be 200000 for "quantile" and |
I was thinking of leaving it as "warn" which sets I am leaning toward (+0.5) going through another deprecation cycle for the other strategies. |
I chose this path. I was not very happy to introduce an unnecessary breaking change. |
Co-authored-by: Guillaume Lemaitre <[email protected]>
thomasjpfan
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Thank you for the update! LGTM
…BinsDiscretizer (scikit-learn#26424) Co-authored-by: Guillaume Lemaitre <[email protected]>
The value is meant to change to 20000 for the 1.3 release.
When implementing the change I faced an error because we used to not support subsampling for other strategies than "quantile". This is an issue because we are setting the default to use subsampling.
Looking at this, I don't see any reason not to support subsampling for the "kmeans" and "uniform" strategies, especially since we set the default value very high. Note that there was no test for the behavior of subsampling, so I added a simple one to check that the bin edges a somewhat close the the ones obtained without subsampling. I propose to now support subsampling for all strategies.