Jacob Stevens-Haas

Oh damn, sorry, I thought I responded a while ago. TL; DR they should be consistent, but mine aren't int target/feature format - user has to flatten them to whichever...

Note to self: * pre-empted by work on #459 * Tests fail because using post-3.8 typing syntax.

I think this is because `BaseDifferentiation` subclasses don't all handle the `axis` kwarg correctly, e.g. ```python ps.SINDyDerivative(kind="kalman")(X, t, axis=1) # TypeError: __init__() got an unexpected keyword argument 'axis' ps.FiniteDifference()(X, t)...

@himkwtn here's another slightly-mathy issue if you want a meatier issue

The question of how to disallow library bagging of constrained optimizers is a thorn. It can be handled in type checking if we parametrize the `BaseOptimizer` type based upon whether...

@MalachiteWind, FYSA. I'm going to show plots and mention this to you (since I added `predict()` to our experiments), so I wanted to drop you a reference to this issue.

Current thinking: If `score` is to be used for model selection, we want all bad models to eventually perform worse than good models, given enough data. # Current system >...

Actually this is a little bit confusing. Currently identifying the feature names for the model requires `feature_library.fit()`. Not sure how this is working right now That may be worth changing,...

> I'm not really sure there's much to do here, these are valid fixes, but all at the user/downstream project level related to installations which are due to a default...

Reopening - we want to be able to implement a longer term fix once pytest/lazy-fixture issue solved.