Banded Ridge TRFs

[vinaysraghavan]

Reference Issues/PRs

What does this implement/fix? Explain your changes.

Adds BandedTRF class for modeling features using TRFs trained with banded ridge regression.
Includes examples showing 1. That banded regularization outperforms standard TRFs by determining regularization separately for each feature and 2. BandedTRFs are dependent on the order of features used

Any other comments?

[gavinmischler]

Pretty useful functionality! Just a few comments, and we should try to get the doc-render working

[codecov]

Codecov Report

❌ Patch coverage is 91.39785% with 16 lines in your changes missing coverage. Please review.
✅ Project coverage is 74.41%. Comparing base (8be3445) to head (c84e256).
⚠️ Report is 50 commits behind head on main.

Files with missing lines	Patch %	Lines
naplib/encoding/banded_trf.py	90.53%	16 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #222      +/-   ##
==========================================
+ Coverage   74.03%   74.41%   +0.37%     
==========================================
  Files          58       60       +2     
  Lines        4287     4718     +431     
==========================================
+ Hits         3174     3511     +337     
- Misses       1113     1207      +94     

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[gavinmischler]

Sorry, last things I didn't realize before, otherwise I think it's ready!

[gavinmischler]

Sorry, forgot to comment this earlier, but in general it's better if arguments to functions like this which take data also specify all the other args. See TRF.fit() or TRF.predict() for an example. This is helpful because it explicitly shows all the columns of data that are required by the model, otherwise it's unclear what the data should look like. And all args should be kwargs, so data=None is the default. Then, the user can call the function by passing data, or by passing each arg separately, or a combination. This is the purpose of the _parse_outstruct_args function.

This should be done for this and for predict()