On 12/4/19 5:05 AM, Trevor Stephens 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.
Indeed, and as Joel said, we'll have n_features_out_ added soon.
Having both is quite helpful in many situations.
The naming is also meant to be analogous to future "feature_names_in_"
and "feature_names_out_
attribute. Right now we have "get_feature_names()", which actually
refers to the output features.
That's a whole lot of new attributes, but after quite a lot of
deliberation that's the solution we came up with,
as there were major flaws in all other proposals.
The SLEP for that is being rewritten right now.
There's some conversation in
https://github.com/scikit-learn/enhancement_proposals/pull/18 but the
document doesn't reflect the current consensus.
Andy
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
<mailto: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
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