The code for the paper "Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity".

1. Linux machine (experiments were run on Ubuntu 18.04.5 LTS and Ubuntu 20.04.3 LTS)
2. Python 3.7

In the main directory, run the following command to install the required libraries.

pip install -r requirements.txt

The experiment scripts are found in the experiments directory, and may be run with the following commands in the main directory. Change the desired distribution distances and acquisitions within the files using the divergences and acquisitions variables.

Random functions from GP prior:

python experiments/rand_func_bigexp.py with default

Plant maximum leaf area:

python experiments/plant_bigexp.py with default

Wind power dataset:

python experiments/wind_bigexp.py with default

COVID-19 test allocation:

python experiments/covid_bigexp.py with default

Computation time:

python experiments/timing.py with default

python experiments/pareto.py with default

The plotting scripts are found in the metrics directory, and may be run with the following commands in the main directory. Each script requires that the corresponding experiments (with seed = 0, 1, ..., num_seeds for the robust regret experiments) have completed. The plots will then be found in the runs directory. Change the desired distribution distances and acquisitions within the files using the divergences and acquisitions variables.

Random functions from GP prior:

python metrics/rand_func_results.py with default

Plant maximum leaf area:

python metrics/plant_results.py with default

Wind power dataset:

python metrics/wind_results.py with default

COVID-19 test allocation:

python metrics/covid_results.py with default

Computation time:

python metrics/timing_results.py with default

python metrics/pareto_results.py with default