This project contains the code associated to the following paper:
"Constrained Causal Bayesian Optimization" by Aglietti Virginia, Alan Malek, Ira Ktena, and Silvia Chiappa. International Conference on Machine Learning. PMLR, 2023.
The code requires python3.10 and python3.10-dev.
To install the package and the necessary requirements you can run (run these
commands from the directory that you wish to clone ccbo into):
git clone https://github.com/deepmind/ccbo.git
python3.10 -m venv ccbo_venv
source ccbo_venv/bin/activate
python3.10 -m pip install --upgrade pip
pip install -r ./ccbo/requirements.txtThe algorithm can be run via the script run_experiment.py using the command
python -m ccbo.experiments.run_optimization where a config file can be
specified using the flag --config. A notebook run_experiment.ipynb is
also provided to allow comparing cCBO and the other methods in the paper.
Experiment configurations are provided in experiments/.
Please cite the ICML paper referenced above. The BibTex is:
@inproceedings{aglietti2023constrained
title={Constrained Causal Bayesian Optimization},
author={Aglietti, Virginia and Malek, Alan and Ktena, Ira and Chiappa, Silvia},
booktitle={International Conference on Machine Learning},
year={2023},
}
Copyright 2023 DeepMind Technologies Limited
All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0
All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode
Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.
This is not an official Google product.
