bug fixes, implementation tweaks, and additional distances

[yugeji]

PR Checklist

• Referenced issue is linked
• If you've fixed a bug or added code that should be tested, add tests!
• Documentation in docs is updated

Description of changes

Technical details

Additional context

[codecov]

Codecov Report

Attention: 20 lines in your changes are missing coverage. Please review.

Comparison is base (0879d41) 62.92% compared to head (128eac2) 0.00%.

❗ Current head 128eac2 differs from pull request most recent head 4b5584f. Consider uploading reports for the commit 4b5584f to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #397       +/-   ##
==========================================
- Coverage   62.92%   0.00%   -62.93%     
==========================================
  Files          41      40        -1     
  Lines        4637    4900      +263     
==========================================
- Hits         2918       0     -2918     
- Misses       1719    4900     +3181     

Files	Coverage Δ
pertpy/tools/_distances/_distances.py	0.00% <0.00%> (-84.22%)	⬇️

... and 46 files with indirect coverage changes

[yugeji]

The tests are failing with ValueError: Found array with 0 sample(s) (shape=(0, 50)) while a minimum of 1 is required by LogisticRegression. when running for the classifier-based distances and I have no idea why because if either X or Y were zero I think the other distances should fail too?

No worries about the NotImplementedError.

[Zethson]

Fixed the above error. Now running into ValueError: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 'c'

sklearn throws an error if it only has to predict a single class because well - it's too easy! So we need to find a way to add another class.

        train = np.vstack([train, train])
        label = label + ["p"]

Before fitting would fix it