Dear Joel,
Thank you for taking the time to answer my email. I didn't see the PR on
this topic, thanks for pointing me to that. I can see your points with
regards to the get_params() method and it might be better if I write
more serialization code on my side (although for example
RandomizedSearchCV also returns a lot of parameters one would not
consider searching over).
Nevertheless, I still think it would be a good idea to have distribution
objects in scikit-learn since some common use cases cannot be easily
handled with scipy.stats (see my last email for examples).
Best regards,
Matthias
On 07.05.2016 14:41, Joel Nothman wrote:
On 7 May 2016 at 19:12, Matthias Feurer
<feur...@informatik.uni-freiburg.de
<mailto:feur...@informatik.uni-freiburg.de>> wrote:
1. Return the fit and predict time in `grid_scores_`
This has been proposed for many years as part of an overhaul of
grid_scores_. The latest attempt is currently underway at
https://github.com/scikit-learn/scikit-learn/pull/6697, and has a good
chance of being merged.
2. Add distribution objects to scikit-learn which have get_params and
set_params attributes
Your use of get_params to perform serialisation is certainly not what
get_params is designed for, though I understand your use of it that
way... as long as all your parameters are either primitives or objects
supporting get_params. However, this is not by design. Further,
param_distributions is a dict whose values are scipy.stats rvs;
get_params currently does not traverse dicts, so this is already
unfamiliar territory requiring a lot of design, even once we were
convinced that this were a valuable use-case, which I am not certain of.
3. Add get_params and set_params to CV objects
get_params and set_params are intended to allow programmatic search
over those parameter settings. This is not often what one does with
the parameters of CV splitting methods, but I acknowledge that
supporting this would not be difficult. Still, if serialisation is the
purpose of this, it's not really the point.
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