On Mon, Aug 24, 2015 at 06:02:19PM -0400, Andreas Mueller wrote: > I think the real solution is to provide backward-compatible ``__getattr__`` > and > ``__setattr_``.
It's a lot of work to support this and do QA. I am not sure we want to add this to our plate. I would personnally rather support PMML I/O, as it has greater value and is probably on the same order of complexity. Anyhow, all this is for after 1.0. G > Theano seems able to do that (at least that is what I was told). > It is unclear weather we want to do this. If we want to do this, we probably > only want it post 1.0 > On 08/19/2015 02:35 AM, Joel Nothman wrote: > Frequently the suggestion of supporting PMML or similar is raised, but > it's > not clear whether such models would be importable in to scikit-learn, or > how to translate scikit-learn transformation pipelines into its notation > without going mad, etc. Still, even a library of exporters for individual > components would be welcome, IMO, if someone wanted to construct it. > On 19 August 2015 at 15:08, Sebastian Raschka <se.rasc...@gmail.com> > wrote: > Oh wow, thanks for the link, I just skimmed over the code, but this is > an interesting idea snd looks like the sort of thing that would make > my > life easier in future. I will dig into that! That’s great, thanks! > > On Aug 19, 2015, at 12:58 AM, Stefan van der Walt < > stef...@berkeley.edu> wrote: > > On 2015-08-18 21:37:41, Sebastian Raschka <se.rasc...@gmail.com> > > wrote: > >> I think for “simple” linear models, it would be not a bad idea > >> to save the weight coefficients in a log file or so. Here, I > >> think that your model is really not that dependent on the > >> changes in the scikit-learn code base (for example, imagine that > >> you trained a model 10 years ago and published the results in a > >> research paper, and today, someone asked you about this > >> model). I mean, you know all about how a logistic regression, > >> SVM etc. works, in the worst case you just use those weights to > >> make the prediction on new data — I think in a typical “model > >> persistence” case you don’t “update” your model anyways so > >> “efficiency” would not be that big of a deal in a typical “worst > >> case use case”. > > Agreed—this is exactly the type of use case I want to support. > > Pickling won't work here, but using HDF5 like MNE does would > > probably be close to ideal (thanks to Chris Holdgraf for the > > heads-up): > > https://github.com/mne-tools/mne-python/blob/master/mne/_hdf5.py > > Stéfan > > ------------------------------------------------------------------------------ > > _______________________________________________ > > Scikit-learn-general mailing list > > Scikit-learn-general@lists.sourceforge.net > > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Gael Varoquaux Researcher, INRIA Parietal NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
