[MNT] Raise version bound for scikit-learn 1.6

[MatthewMiddlehurst]

What does this implement/fix? Explain your changes.

Raises the sklearn version bound and fixes broken tests.

This version introduces a new tags system used by sklearn. We have our own tag system, but I have integrated this somewhat so sklearn functions which use tags their should still work as intended.

The classification, regression and clustering base now extend the respective sklearn mixin. Just inheriting these is more maintainable than implementing our own methods for their tags and keeping up with changes to methods which use them IMO ( i.e. one of the breakages was is_classifier which now uses these tags instead of self._estimator_type).

Any other comments?

Currently, there are some expected results failures due to a bug with trees, see scikit-learn/scikit-learn#30557. I probably won't merge this until 1.6.1 so we dont have to swap this again unless someone really needs it.

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[MatthewMiddlehurst]

@baraline I think other than expected results the only thing is the shapelet visualiser i.e. https://github.com/aeon-toolkit/aeon/actions/runs/12699462793/job/35400155079?pr=2486#step:8:925, not sure what would cause this. Can take a look if you dont have time but could just be a small thing.

[baraline]

Will check during lunch time, doesnt look to bad

[baraline]

@MatthewMiddlehurst I don't seem to be able to reproduce the bug locally somehow? I don't see how the changes could cause it. I'm using

    python: 3.10.13 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:24:38) [MSC v.1916 64 bit (AMD64)]
executable: C:\Users\antoine\anaconda3\envs\aeon_dev\python.exe
   machine: Windows-10-10.0.22631-SP0
Python dependencies:
         aeon: 1.0.0
          pip: 23.3.1
   setuptools: 68.0.0
 scikit-learn: 1.2.2
        numpy: 1.24.4
        numba: 0.58.1
        scipy: 1.11.4
       pandas: 2.0.3

[MatthewMiddlehurst]

@baraline need scikit-learn 1.6.0

[baraline]

@MatthewMiddlehurst The issue is that the coef_ argument of RidgeClassifierCV is of shape (n_features) instead of shape (n_targets,n_features) as it should be (it's (1, n_features) according to docs in binary case) after fitting any shapelet classifier using it as classifier, so this could be an sklearn bug ? A simple fix on our side would be to check for at least 2D array on line 716 of _shapelets.py visualization file

[TonyBagnall]

lgtm

[baraline]

Fixed the shapelet visualisation part (nvm some notebook failure still happens), but it seems there is other failures with expected results for multiple estimators, is 1.6 supposed to change any behaviour ? I don't see any change on highlights or docs between 1.5 and 1.6.

Edit : for some linear models, the coef_ argument changed in 1.6, see scikit-learn/scikit-learn@2f8f9f3