Hi Andy,
Having distributions objects would be useful for several reasons:
1. Having a uniform way to programatically access the parameters of all
kinds of distribution objects. Currently, I could parse the 'args' item
in 'distribution.__dict__'. I don't know how important this is for
others, though.
2. Having a helpful __repr__. Currently, printing a distribution does
not even tell the kind of distribution:
uniform = scipy.stats.uniform(3,5)
print(uniform)
<scipy.stats._distn_infrastructure.rv_frozen object at 0x7f1a61657898>
3. Some useful distributions aren't easily possible with scipy.stats.
Can you please give me examples for:
* tuning the number of layers and the number of hidden neurons of
the MLPClassifier?
* tuning C and gamma of SVC on a log scale between 2^12 and 2^12?
I couldn't find appropriate objects in scipy.stats and ended up defining
my own.
Best,
Matthias
to have a useful representation of distribution __repr__), and finally
to have distributions
On 08.05.2016 23:49, Andreas Mueller wrote:
Hi Matthias.
Can you explain this point again?
Is it about the bad __repr__ ?
Thanks,
Andy
On 05/07/2016 08:56 AM, Matthias Feurer wrote:
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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