I really don't think Py2.x should be our concern anymore. The kind of spaghetti code you mention is somewhat the nature of supporting multiple versions of a dependency library. We do have similar code in our code base which deals with different versions of our own dependencies.
On Wed, Dec 4, 2019 at 11:06 AM Trevor Stephens <trev.steph...@gmail.com> wrote: > Makes sense Joel, wasn't mentioned in the docs, so was a bit strange. > Still feels a bit weird but I'm sure I'll adapt_in and thrive_out. > > Downstream projectwise, I'm happy to bounce my dependencies up whenever > necessary. Always nice to support old versions of sklearn, but not at the > expense of spaghetti code from my persepctive, whatever that's worth. > > Might be a bit more prickly for projects still trying to support Py2.x > though? > > On Wed, Dec 4, 2019 at 8:53 PM Joel Nothman <joel.noth...@gmail.com> > wrote: > >> We are looking to have n_features_out_ for transformers. This naming >> makes the difference explicit. >> >> I would like to see some guidance on how an estimator implementation >> (e.g. in scikit-learn-contrib) is advised to maintain compatibility with >> Scikit-learn pre- and post- SLEP010. >> >> That is, we want to encourage developers to take advantage of >> super()._validate_data(X, y), but we also don't want to force them to set a >> minimal Scikit-learn >= 0.23 dependency (or do we?). What's the recommended >> way to do implement fit and predict in such an implementation? >> >> Is it to >> (a) not use _validate_data until the minimal dependency is reached? >> (b) implement a patched BaseEstimator in the library which inherits from >> Scikit-learn's BaseEstimator and adds _validate_data? >> (c) something else? >> >> >> Joel >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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