Meet sklearn contrib conventions

[quentinhaenn]

Need some work to meet sklearn contrib conventions.

Each bullet point will be addressed in a specific issue, not necessarily in this order :

• change project's name from radius-clustering to sklearn-contrib-radius-clustering on PyPI
• add some issue and PR templates
• Review default values of all estimators to meet requirements and justify them
• Add lint and format in GitHub workflows
• Check if pyproject.toml is valid and still correct
• think about using meson for building extensions (maybe some help will be required here)
• Add Code of Conduct and Contributing guidelines
• Rework wheel build workflows, from actual to "dispatch" and "on tag" or similar, to only build and push new versions pushed on main branch
• Rework Readme
  • watch for opening an example in binder
  • Add badges for coverage, build status and maybe a DOI from zenodo or software heritage in actual version
  • New examples, reworked benchmarks, review dependencies

@adrinjalali, I think I covered the most issues to address, can you please tell me if I am missing something ?

[adrinjalali]

They all sounds like improvements, except that I wouldn't insist on this point:

change project's name from radius-clustering to sklearn-contrib-radius-clustering on PyPI

I don't think you need to change the name. Feel free to have the name as you wish.

[quentinhaenn]

Oh ok, I wrote it because it is indicated in Project Maintenance Guidelines as :

Project name on pypi is sklearn-contrib-project-name (e.g., sklearn-contrib-lightning).

I downgrade priority on this one then, we'll think about project's name with lab team later 😄