I think the real solution is to provide backward-compatible
``__getattr__`` and ``__setattr_``.
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
<mailto: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 <mailto:stef...@berkeley.edu>> wrote:
>
> On 2015-08-18 21:37:41, Sebastian Raschka <se.rasc...@gmail.com
<mailto: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
>
>
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