This repository contains the source code for the paper "Bayesian Optimization over Hybrid Spaces" which will be presented at upcoming Thirty-eighth ICML'21 conference.
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By default, data is stored in
../EXPERIMENTS. Directory can be changed inconfig.py -
The command-line arguments are described below: --n_eval : The number of evaluations --objective : ['coco'] (example on how to create new objective can be seen in experiments/test_functions/mixed_integer.py) --problem_id : applicable only for 'coco' and 'nn_ml_datasets' domain --path : A path to the directory of the experiment to be continued.(Only need when you want to resume an experiment)
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Example run
python main.py --objective coco --problem_id bbob-mixint_f001_i01_d10 --n_eval 180 -
There are 7 benchmarks used in the paper. For using synthetic benchmark, please see instructions in coco suite. For robot pushing benchmark, please see the original description in Ensemble-Bayesian-Optimization.
The discrete part of the code is built upon the source code provided by the COMBO authors. We thank them for their code and have added appropriate licenses.