microsoft
[python-package] enable early stopping automatically in scikit-learn interface (fixes #3313)
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Until we have a chance to review, you can improve the chances of this being merged by addressing any CI failures you see. You can safely ignore failing R-package jobs, there are some known issues with those (fixed in #5807 ).
Hey @jameslamb thanks for picking this up. I have started to take a look at the ci failures, I think I can solve most of them easily.
There is one check for which I need some input.
I see that one of the tests checks that the init parameters for the sklearn API and the Dask API have the same arguments (see this one). Early stopping is not available yet for the Dask API, so I do not see how can we easily iron that out. Shall I add some more exceptions to that specific test?
Shall I add some more exceptions to that specific test?
@ClaudioSalvatoreArcidiacono no, please do not do that. This test you've linked to is there exactly to catch such deviations.
If you absolutely need to add arguments to the constructors of the scikit-learn estimators in this project, add the same arguments in the same order to the Dask estimators, with default values of None or something, and raise NotImplementedError in the Dask interface when any non-None values are passed to those arguments.
For what it's worth, I am not totally convinced yet that we should take on the exact same interface as scikit-learn, (especially the large added complexity of this new validation_set_split_strategy argument). #3313 was primarily about whether or not to enable early stopping by default... not explicitly about changing the signature of the lightgbm.sklearn estimators to match HistGradientBoostingClassifier.
If you are committed to getting the CI passing here I'm willing to consider it, but just want to set the right expectation that I expect you to also explain specifically the benefit of adding all this new complexity.
By the way, it looks like you are not signing your commits with an email address tied to your GitHub account.
See https://github.com/microsoft/LightGBM/pull/5532#issuecomment-1274032708 and the comments linked from it for an explanation of what I mean by that and an explanation of how to fix it.
It has been about 6 weeks since I last provided a review on this PR, and there has not been any activity on it since then.
I'm closing this, assuming it's been abandoned. To make it clear to others onterested in this feature that they shouldn't be waiting on this PR.
@ClaudioSalvatoreArcidiacono if you have time in the future to work with maintainers here, we'd welcome future contributions.
Hey @jameslamb, I did not have much time to take a look at this lately, I should be more available now and in the coming weeks.
If you also have some time to help me reviewing it I can pick this PR up again.
Regarding your previous comments, thanks for the heads up on signing commits, I will sign the next commits as you mentioned.
About the complexity of the proposed implementation, I am definitely open for feedback from the maintainers and I am willing to change the proposed implementation if necessary.
In the proposed implementation I tried to stick to what is written in the FAQ:
The appropriate splitting strategy depends on the task and domain of the data, information that a modeler has but which LightGBM as a general-purpose tool does not.
So, in the proposed implementation I tried to find a common ground between convenience of activating early stopping using only init params and customisability of the splitting strategy.
Just to set the right expectation...I personally will not be able to look at this for at least another week, and I think it's unlikely it'll make it into LightGBM 4.0 (#5952).
I'm sorry, but this is quite complex and will require a significant investment of time to review. Some questions I'll be looking to answer when I start reviewing this:
- what does "Scikit-learn like interface" mean, precisely?
- does it mean you've implemented exactly the same interface as
HistGradientBoostingClassifierandHistGradientBoostingRegressor? If so can you please link to code and docs showing that? - and more broadly, why does this need to change ANYTHING about the public interface of
lightgbm? And couldn't the existing mechanisms used insidelightgbm.cv()be used instead of adding all this new code for splitting? e.g. https://github.com/microsoft/LightGBM/blob/9f78cceee4911dd56f4635dfd36d4482363db5aa/python-package/lightgbm/engine.py#L562-L564
- does it mean you've implemented exactly the same interface as
- what has to happen to make these changes consistent with how
lightgbmcurrently works? For example...- what happens when
early_stopping_roundsis passed to the estimator constructor via**kwargsandn_iter_no_changeis set to a non-default value in the constructor... which value wins? - What happens if
early_stopping=Trueis passed butvalid_setsare also passed to.fit()? Does that disable the automatic splitting and just use the provided validation sets?
- what happens when
Hey @jameslamb, no problem. Thanks for being transparent on your availability on this PR and for your feedback. I followed it and I made the PR easier to review now.
I have removed the functionality to use a custom splitter and I made the changes much smaller.
what does "Scikit-learn like interface" mean, precisely?
- does it mean you've implemented exactly the same interface as
HistGradientBoostingClassifierandHistGradientBoostingRegressor? If so can you please link to code and docs showing that?
I have now implemented the same interface as HistGradientBoostingClassifier and HistGradientBoostingRegressor.
- and more broadly, why does this need to change ANYTHING about the public interface of
lightgbm? And couldn't the existing mechanisms used insidelightgbm.cv()be used instead of adding all this new code for splitting? e.g. https://github.com/microsoft/LightGBM/blob/9f78cceee4911dd56f4635dfd36d4482363db5aa/python-package/lightgbm/engine.py#L562-L564
In this implementation I tried to reuse the splitting function used in lightgbm.cv(). Thanks for the tip.
what has to happen to make these changes consistent with how
lightgbmcurrently works? For example...
- what happens when
early_stopping_roundsis passed to the estimator constructor via**kwargsandn_iter_no_changeis set to a non-default value in the constructor... which value wins?
Good observation, I think it is indeed needed to rename the arguments so that they will be more consistent with LightGBM naming conventions.
- What happens if
early_stopping=Trueis passed butvalid_setsare also passed to.fit()? Does that disable the automatic splitting and just use the provided validation sets?
Correct.
Hey @jameslamb thanks a lot for your review. I am still interested in solving this issue, but I will be on holidays for the next two weeks. I will take a look at your comments once I am back.
No problem, thanks for letting us know! I'll be happy to continue working on this whenever you have time.
Hey @jameslamb, Thanks again for your review comments! I was aware of the aliases mechanism but I did not fully understand how it worked. Your comment really helped me in understanding them.
I have worked on your feedback and I think now the PR is in good shape.
In this implementation I tried to stick to the scikit-learn interface of HistGradientBoostingClassifier, so the parameter early_stopping is 'auto' by default.
Since we are not adding early_stopping_round to the public interface, the default value for early_stopping_round is somewhere else in the code, I think it would be better to create a constant in the sklearn.py file where we set the default value, happy to hear alternative solutions from you.
Lastly, we should still mention somewhere in the documentation that early stopping is enabled by default in the scikit-learn interface of LightGBM and also we should mention how to forcefully disable it. Where would you suggest to add it?
Lastly, we should still mention somewhere in the documentation that early stopping is enabled by default in the scikit-learn interface of LightGBM and also we should mention how to forcefully disable it. Where would you suggest to add it?
I think here would be appropriate:
https://github.com/microsoft/LightGBM/blob/921479b99fb5b691801e0e794f2196a94ea17d79/docs/Python-Intro.rst#L223
Lastly, we should still mention somewhere in the documentation that early stopping is enabled by default in the scikit-learn interface of LightGBM and also we should mention how to forcefully disable it. Where would you suggest to add it?
I think here would be appropriate:
https://github.com/microsoft/LightGBM/blob/921479b99fb5b691801e0e794f2196a94ea17d79/docs/Python-Intro.rst#L223
Thanks, I have added something there. Shall we also mention something here?
Hey @jameslamb, I think the PR is ready for a second look, please let me know if there are some Changes you would like me to do :)
Thanks for returning to this.
@jmoralez could you take the next round of reviews on this?
The original issue (#3313) requested the possibility of doing early stopping with the scikit-learn API by specifying arguments in the constructor, not automatically performing early stopping. I understand that's what HistGradientBoosting(Classifier|Regressor) do, but I think we could also consider doing what GradientBoosting(Classifier|Regressor) do, where the default is not to do it but having the arguments in the init signature to support it. Otherwise this would be a silently breaking change (I know we would list it as a breaking change but it could confuse people who don't read the release notes).
I would like to have this clear before reviewing, since we would be doing many things behind the user's back (automatically enabling early stopping for >10,000 rows, stratified if classification, setting the number of folds, shuffling). I would be more comfortable if it was an explicit decision (like setting early_stopping_rounds>0 in the params for example).
Hey @jmoralez, thanks for your comment. I agree with you, my preference would be for having early stopping off by default and to have it on only when it is explicitly set by an init parameter. This was also my original implementation. I have changed it due to this comment from @jameslamb.
Could the two of you maybe agree on what would you like to see as the default behaviour :)?
What are your thoughts on this (enabling early stopping by default) @borchero?
What are your thoughts on this (enabling early stopping by default) @borchero?
Sorry, I only saw this comment now 🫣 I wouldn't enable it by default as (1) it is not integral to use for boosted trees and (2) early stopping is not supported for all boosting strategies (I recently learnt that dart does not support it) which might cause confusion.
