sklearn exposes a dictionary mapping valid strings for something like a pairwise distance ["euclidean", "l1", etc.] to the corresponding function. Makes it super easy to test if something is a valid key, and get all available options if you are exploring. See here.
Could be useful to have for hyppo too, basically would expose this as a dictionary called TESTS* or something similar.
*note, IMO might be more confusing to call this INDEPENDENCE_TESTS because they can also be used for k-sample, but that's just my opinion
sklearn exposes a dictionary mapping valid strings for something like a pairwise distance ["euclidean", "l1", etc.] to the corresponding function. Makes it super easy to test if something is a valid key, and get all available options if you are exploring. See here.
Could be useful to have for hyppo too, basically would expose this as a dictionary called
TESTS
* or something similar.*note, IMO might be more confusing to call this
INDEPENDENCE_TESTS
because they can also be used for k-sample, but that's just my opinion