On Thu, Jan 30, 2014 at 07:53:16PM -0500, Frédéric Bastien wrote: > I have a question on those type of algo for hyper parameter > optimization. With a grid search, we can run all jobs in parallel. But > I have the impression that those algo remove that possibility. Is > there there way to sample many starting configuration with those algo?
As others have answered, in theory it is possible. You need a producer/consumer pattern in which you asynchronously spawn jobs that fit and test a model, and when you retrieve the results, you update the Bayesian optimizer which gives you another set of test points to try. The parallel computing pattern is much more involved than those that joblib supports. We want to evolve joblib to be more flexible, but we want to do this while keeping its robustness and its simplicity. Thus there is a lot of work on this side. Hyperopt implements all these patterns, and more, but with fairly involved code and more dependencies. Gaël ------------------------------------------------------------------------------ WatchGuard Dimension instantly turns raw network data into actionable security intelligence. It gives you real-time visual feedback on key security issues and trends. Skip the complicated setup - simply import a virtual appliance and go from zero to informed in seconds. http://pubads.g.doubleclick.net/gampad/clk?id=123612991&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
