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Pydata Work Prioritization #145

@RNKuhns

Description

@RNKuhns

This is a meta issue to prioritize our work for Pydata Seattle 2023.

High-level tasks for the presentation:

High-level tasks for new features/maintenance ahead of Pydata Seattle 2023. These are divided into "must", "should" and "nice-to-have":

Must:

  • sktime and skbase initial integration is completed. Targeting mid-April release of sktime v0.18.0
  • Create "example" repository that includes a stylized package that uses skbase (will also be useful for additional testing of our lookup, testing and validation functionality too)
  • Local (class) configuration interface
  • [ENH] Refactor skbase.base._meta and add unit tests #106

Should

  • [ENH] Implements sklearn style pretty printing directly in skbase #150
  • Add persistance (re-work of sktime persistance approach to make sure it works more generally)
  • Upgrade documentation, with emphasis on landing page
  • Redo governance section of documentation
  • Create functionality roadmap (borrow from HackMD and design document and add other ideas)
  • Improve documentation of the skbase.testing module

Nice-to-have

  • [ENH] Add global config interface #149
  • Example showing how to use skbase to create scikit-learn compliant class (need to investigate if this is possible. If their checks are "duck-typed" then it should be)

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