As far as I remember, one idea is to have a deprecation cycle with a FutureWarning on check estimator to give third party developers implement the new API.
On Wed, Dec 4, 2019 at 10:52 AM 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 >
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