[scikit-learn] Vote on SLEP010: n_features_in_ attribute
adrin.jalali at gmail.com
Wed Dec 4 04:59:07 EST 2019
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.nothman at 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?
> scikit-learn mailing list
> scikit-learn at python.org
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