Optimizer parameters validation improvement

[himkwtn]

Right now, parameters validation is done in the constructor as follows https://github.com/dynamicslab/pysindy/blob/3e8a4455fd4de046225344dda9b516d0d013ee36/pysindy/optimizers/sr3.py#L138-L163

According to the scikit-learn documents, validations should be done in the fit method because if we call set_params, it will bypass the validation in the constructor.

Reproducing code example:

from pysindy.optimizers import SR3
opt = SR3(threshold=-1)
# raises "ValueError: threshold cannot be negative"

from pysindy.optimizers import SR3
opt = SR3()
opt.set_params(threshold=-1)
# no error

[Jacob-Stevens-Haas]

This is a good point, although fairly low impact, since I believe set_params() is only used in gridsearch, which is not done much with SINDy, and we can cheat by doing the same validation in set_params() that we do in __init__(). That said, this is something we want, and the way to do this is:

• [ ] list out all classes that break the scikit-learn API in differentiation, feature library, and optimizer
• [ ] extract all validation into a helper function, refactored to the end of __init__()
• [ ] Modify tests for bad argument combinations to require __init__ to pass, and then
• [ ] move the validation from __init__ to fit()