Label balancing and symmetric behaviors for training classifiers

[SkepticRaven]

Adds:

• label downsampling routine for training classifiers with balanced labels
• GUI checkbox for optionally conducting the balanced training
• balanced training value stored in both trained classifier + project
• random subsetting is hit by random_seed
• bugfix on storing selected gui option for social features

[SkepticRaven]

Current TODO:

• Update all calls of classifier.train to now include new uses_balance parameter

Right now, this option only really works within the gui. classify.py will fail.

[SkepticRaven]

Adding in another new feature: symmetric behaviors (where left-right features can be swapped)

• Checkbox for enabling this feature in the classifier
• Symmetric behaviors will essentially double the training dataset size and force the classifier to rely on asymmetric features less (due to L-R swapping for the doubling)

In addition to that, a massive rework was done to enable this feature to be added

• Lots of new class functions to get feature names without an instantiated class object
• Storing a new value into saved training data
  • min_pose_version: minimum required pose version for the classifier (currently not used in checks, but might be useful)
• New all-k-fold button for running the maximum number of k-fold cross validations in the project
  • Helps with reporting better averages between multiple trainings when a video may be an outlier in performance (but useful for training)
• Added std reporting for k-fold cross validation

[dahhei]

I believe that I can't test these changes on my local install of JABS unless the update-reqs is merged first. There is a version from commit-head 49c9b10 on the HPC that I will use to test these updates. 😃

[dahhei]

Cayson initially raised this issue so I tested out the all k-fold cross-validation and it appears as though the top 10 feature labels are incorrect. I trained a Drinking classifier which typically has the distance to Lixit features present. This one does not. The all k-fold does seem to work otherwise though.

[dahhei]

It would be nice if the cross validation iteration value included a "cross validation iteration 14/n", with n being the total number that it will test. 🤠

[SkepticRaven]

Lower pose version projects aren't getting their feature set correctly propagated to the new functions. On a pose_v2 project:

Traceback (most recent call last):
  File "/behavior-classifier/src/ui/training_thread.py", line 56, in run
    features, group_mapping = self._project.get_labeled_features(
  File "/behavior-classifier/src/project/project.py", line 663, in get_labeled_features
    column_names = features.get_feature_column_names(
  File "/behavior-classifier/src/feature_extraction/features.py", line 539, in get_feature_column_names
    column_names = self.get_feature_name_vector(pose_version=self._pose_version, use_social=use_social, extended_features=self._extended_features)
  File "/behavior-classifier/src/feature_extraction/features.py", line 494, in get_feature_name_vector
    assert pose_version >= 5
AssertionError

[SkepticRaven]

The commits above are a rebase merge with main (based on the other recent merge into main: https://github.com/KumarLabJax/JABS-behavior-classifier/pull/27)

[SkepticRaven]

The feature rewrite to propagate names alongside values fixed the pose_v2 bug. This branch should be ready to merge (after updating the appropriate version numbers)!