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A minor issue with the current Transformer hierarchy is that most of the user-facing classes don't have to define explicit __init__ methods. This isn't a problem functionally, but it makes it hard for users to figure out what the initialization arguments are--and this can be quite important (e.g., in the case of arguments like api_key). Even though it's redundant, we should probably insist on every public-facing Transformer class explicitly implementing __init__ with all the main arguments visible, even if all the method does is turn right around and pass everything up to the superclass.
The text was updated successfully, but these errors were encountered:
I agree with this issue as well, it just will be tedious to do everything. This also leaves us open to interpreting what "main arguments" are, but I suspect we'll just have to use our judgment per transformer. Do the LibrosaFeatureExtractors look good for this pattern?
Yeah I think we probably need to do the librosa Extractors this way too. But there's no rush on this--it's not a high priority, and we can chip away at it as time allows. It was mainly salient to me because I'm close to pushing a big PR that adds Sphinx docs, and the autodocs don't help very much when the args are basically just **kwargs.
A minor issue with the current
Transformer
hierarchy is that most of the user-facing classes don't have to define explicit__init__
methods. This isn't a problem functionally, but it makes it hard for users to figure out what the initialization arguments are--and this can be quite important (e.g., in the case of arguments likeapi_key
). Even though it's redundant, we should probably insist on every public-facingTransformer
class explicitly implementing__init__
with all the main arguments visible, even if all the method does is turn right around and pass everything up to the superclass.The text was updated successfully, but these errors were encountered: