[scikit-learn] Vote on SLEP010: n_features_in_ attribute

[Joel Nothman]

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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20191204/52eb1f81/attachment-0001.html>