MetaBO - Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

This is the source code accompanying the paper Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization by Volpp et al., ICLR 2020. The paper can be found here. The code allows to reproduce the results from the paper and to train neural acquisition functions on new problems.

Purpose of the project

This software is a research prototype, solely developed for and published as part of the publication cited above. It will neither be maintained nor monitored in any way.

Installation

Clone this repository and run

conda env create -f environment.yml
conda activate metabo

to create and activate a new conda environment named "metabo" with all python packages required to run the experiments.

Run the code

We provide:

• Scripts to reproduce the results presented in the paper. These scripts are named evaluate_metabo_<experiment_name>.py. They load pre-trained network weights stored in /metabo/iclr2020/<experiment_name> to reproduce the results without the need of re-training neural acquisition functions. To run these scripts, execute

python evaluate_metabo_<experiment_name>.py

• Scripts to re-train the aforementioned neural acquisition functions. These scripts are named train_metabo_<experiment_name>.py. To run these scripts, execute

python train_metabo_<experiment_name>.py

License

"Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization" is open-sourced under the APGL-3.0 license. See the LICENSE file for details.