Shouldn't these be improvements to scipy, rather than live in scikit-learn?
Gaël On Mon, May 09, 2016 at 09:47:59AM +0200, Matthias Feurer wrote: > 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> 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. > > ------------------------------------------------------------------------------ > Find and fix application performance issues faster with > Applications Manager > Applications Manager provides deep performance insights into > multiple tiers of > your business applications. It resolves application problems > quickly and > reduces your MTTR. Get your free trial! > https://ad.doubleclick.net/ddm/clk/302982198;130105516;z > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > ------------------------------------------------------------------------------ > Find and fix application performance issues faster with Applications > Manager > Applications Manager provides deep performance insights into multiple > tiers of > your business applications. 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