RFC: Breaking Changes for Version 2

A while ago we talked about the possibility of a version 2 with breaking changes. Specifically, this came up in the context of SLEP006: metadata routing, but there are other things we have wanted to break which we can do all in a single release. In this issue we can talk about the possibility of such a release, and maybe a few things we'd like to include in it.

I'll try to summarize the challenges of keeping backward compatibility (BC) in the context of SLEP006:

- `MetaEstimator(Estimator()).fit(X, y, sample_weight)` will raise according to SLEP006 since `sample_weight` is not requested by any object. For backward compatibility, during the transition period, it'll only warn and assume `sample_weight` is requested by `Estimator()`.
- What should the behavior of `GridSearchCV(Estimator(), scorer=scorer).fit(X, y, sample_weight)` be during the transition period? If we keep backward compatibility, it should warn (whatever the warning be), route `sample_weight` to `Estimator`, but not to the `scorer`. And that's only the case because we're keeping BC. From the user's perspective, it's very weird.
- `Pipeline` ATM in certain methods like `fit`, `fit_transform`, `predict`, routes `*_params` to the `fit/etc` of the last estimator only. And `decision_function` and `transform`, `score_samples` have no routing at all. Keeping this BC, proves to be challenging, and a bit nasty. ref: #24270
- In quite a few meta-estimators, we check if the sub-estimator has `sample_weight` in the signature of `fit`, and we pass the given sample weight to it if that's the case, and not if that's not the case. With routing, we would have a much better idea of when to pass sample weight and when not, but if we're keeping BC, it's really challenging to see if we should forward sample weights or not. Both AdaBoost (#24026) and Bagging (#24250) have been very challenging, to the point that we might end up having to check if a sub-estimator is a meta-estimator itself or not, and check signature of sub-estimator's `fit`, and check the given routing info to keep BC.

I'm pretty sure there are other issues we haven't foreseen, and they'll come up as we work on this project. We thought it'd be easy once we get to this point, but BC has proven to make it very tricky and has stalled the progress.

All of the above is adding substantial complexity which is probably error prone, and buggy. We have had to spend quite a bit of time every time we go back to review those bits to understand why we've done what we've done. Even as a temporary solution they're not very maintainable.

My proposal here is to have a version 2 which is not BC with previous releases in certain ways, and target a few breaking changes for it. We can aim to have the release in October 2023 or April/May 2024 as the version 2.

cc @scikit-learn/core-devs

COMMENT:
I agree with Tim's point that we should strive to have scikit-learn "make it easy/simple to use ML correctly". I am not 100% sure what is the correct way to handle `sample_weight` in complex pipelines (with transformers that accept sample weights), scorers (e.g. #25906) or meta estimators but this is probably something we should improve.

However I acknowledge that fixing the `sample_weight` bugs everywhere might take time and I don't think we should constrain us to fix all the consistency issues of `sample_weight` handling in scikit-learn before making it possible to implement the other use cases that SLEP6 would enable (e.g. routing nested CV groups or other side metadata for fairness-constrained learning or fairness evaluation by dedicated scorers).

So here is an idea: what about merging a partial integration of SLEP6 to route any metadata except `sample_weight`? This way we would have:
- no breaking changes for the first release that ships SLEP6;
- no extra API complexity for the most common scikit-learn usage cases;
- but we could still release an API extension that allows the new use cases (nested group-aware CV and fairness evaluation) that are currently impossible to implement with the current scikit-learn API.

Then in parallel we fix the `sample_weight` bugs for scorers and meta-estimators. And once we have a clear consensus of what should be the default `sample_weight` routing logic for pipelines (maybe the one described by @jeremiedbb above), then we can discuss how to extend the metadata router to make it possible for the use to customize `sample_weight` routing away from the default routing strategy.

Labels: RFC, Breaking Change