Usability Improvements

[jeremyrcoyle]

A range of inter-related usability improvements:

• SuperLearner3 implementation that matches arguments from SuperLearner package. Not yet feature complete. This may be removed if it is too difficult to support both SuperLearner learners and sl3 learners in the same function call in a user-friendly way.
• Tasks have defined outcome_types, which are guessed from the data if not specified.
• Support for binomial and multinomial outcomes types for some learners
• Support for offsets
• Learners have properties that mark support for various features (e.g. certain outcome types, weights, offsets, etc).
• sl3_list_learners lists learners and allows searching list by property sets
• New learners:
  • Lrnr_glmnet - glmnet
  • Lrnr_solnp - optimize arbitrary loss function + metalearner function combinations
• make_learner function to construct learners
• Implemented args_to_list and call_with_args to simplify learner parameter handling
• Overhauled all learners to implement new features where appropriate
• Learner fits respond to coef properly if their internal fit objects do
• Benchmarking script
• Further tests

[nhejazi]

Awesome! Looks like this will resolve #38 and #14 -- any other issues that I'm missing?

[jeremyrcoyle]

There's still some missing features to implement before I'm happy marking #38 done. Still trying to decide if it makes sense to go through all the trouble to match the legacy syntax.

In addition to #14, this resolves #40, #24, #22, and #5

[osofr]

Fantastic!

[nhejazi]

This all looks great to me! I've tried to do a bit of elementary testing/playing and everything that I've tried (though limited) works well. Thanks for all the hard work on this @jeremyrcoyle!