This repository contains the code and data used for the experiments presented in the paper Imprecise Acquisitions in Bayesian Optimization. All identifying information (e.g., author names, affiliations, and institution-specific details) has been removed to preserve anonymity.
This repository includes implementations, experiment scripts, and evaluation routines for the methods described in the paper.
The experiments can be reproduced using the provided configurations and instructions below.
- Python >= 3.10
- Required dependencies are given in the
requirements.txtfile in the repository.
You can install all dependencies via:
pip install -r requirements.txtTo reproduce the main experiments:
python experiments/bbob_experimenter.py The results will be stored in a database using the py_experimenter. To this end, a database connection needs to be specified in the config files in
experiments/configs
For further information, please consider the py_experimenter documentation https://tornede.github.io/py_experimenter/
To evaluate the methods and create the plots from the paper, use:
python experiments/plot_results_ucb.pyEvaluation metrics and visualizations will be stored in ../figs.
- All references to institutions, datasets with identifiable origins, or personal identifiers have been removed or replaced with placeholders.
- This repository is intended solely for anonymous peer review and will be updated with attribution upon acceptance.