When I would like to find good configurations for log distributional variables

[nabenabe0928]

Hi, I would like to optimize the configuration which includes some variables which should be sampled after converted into log scale.

I checked your codes and found there I could choose 4 types of variable: "Continuous", "Ordinal", "Integer", "U"(categorical). I would like to make the points clear.

In the former hyperopt or Optuna offered by PFN, I can choose the distributions from "Uniform", "Discrete", "LogUniform", "Categorical". In the HpBandSter, how can I handle with these kind of variables??

I'm looking forward to hearing from you.

shuhei

[sfalkner]

Hello, The definition of the parameters is done with the ConfigSpace package. There is only limited documentation for that, but your case is rather easy: The Continuous and Integer parameters have an additional argument called log. If you set that to true, the optimizer will assume a uniform prior in log-space, which is what you want, I think. Hope that helps!

[mfeurer]

Thanks to @PhMueller we now have "real" docs for the ConfigSpace at https://automl.github.io/ConfigSpace/master/