I'll definitely try it out! Thanks for all the work. On Sun, Nov 2, 2014 at 9:17 PM, Robert McGibbon <[email protected]> wrote:
> The feature set is pretty similar to spearmint. We have found that the MOE > GP package is much more robust than the code in spearmint though (it was > open sourced by yelp and is used in production there). > > In contrast to hyperopt, osprey is a little bit more geared towards ML, in > that ideas about cross validation and datasets are baked in. It also > doesn't require you setup your own mongo server. > > Both of those packages are great tools. > > -Robert > > On Sunday, November 2, 2014, federico vaggi <[email protected]> > wrote: > >> Looks neat, but how does it differ from hyperopt or spearmint? >> >> On Fri, Oct 31, 2014 at 11:46 PM, Robert McGibbon <[email protected]> >> wrote: >> >>> Hey, >>> >>> I started working on a project for hyperparmeter optimization of sklearn >>> models. >>> The package is here: https://github.com/rmcgibbo/osprey. It's designed >>> to be easy >>> to run in parallel on clusters with minimal setup. For search >>> strategies, it supports >>> Gaussian process expected improvement using the MOE >>> <https://github.com/yelp/moe> package, as well as >>> random search and hyperopt's TPEs. The code is apache licensed. It's >>> still pretty >>> beta, but if anyone here is interested I'd encourage you to check it out >>> and post >>> any issues you have on github. >>> >>> -Robert >>> >>> >>> ------------------------------------------------------------------------------ >>> >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> [email protected] >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >>> >>> >> > > ------------------------------------------------------------------------------ > > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
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