sigopt-examples

We compare to some standard convex optimization techniques implemented in scipy here [1]. We have comparisons to some other Bayesian methods here [2]. I'm happy to answer any questions! [1]: https://github.com/sigopt/sigopt-examples/blob/master/ipytho... [2]: http://arxiv.org/abs/1603.09441

The methods in scipy.optimize are great if your function is serial, cheap, deterministic, and convex. Many ML models and real world problems don't fit into this context though. We have a Jupyter notebook showing how SigOpt compares to several scipy.optimize methods as well as standard methods like grid/random on a simple non-convex problem here [1]. These results only get more striking as the dimensionality increases. [1]: https://github.com/sigopt/sigopt-examples/blob/master/ipytho...

> ALS implementation in MLLib still requires ratings in RDD and hasn't moved over to ML/DataFrames yet. ALS has been available in ML/DataFrames since 1.3.0, per the documentation. (http://spark.apache.org/docs/latest/api/scala/index.html#org...) > Edit: Looks like the original comment was edited, but this post does in fact use the built in MLlib cross validation methods. I edited the comment to correct my error that ALS was not mentioned as being native to Spark. However, for Hyperparameter cross validation, I looked at the code in the repository (https://github.com/sigopt/sigopt-examples/blob/master/spark/...), and while the Spark CrossValidation function is imported at the beginning of the Scala file (from ML, not MLlib), it is not used in the code, in favor of SigOpt. I'm confused.