Heteroscedastic Bayesian Optimisation uisng the most likely heteroscedatic Gaussian Process as the surrogate model. Implements the approach from "Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation" available at [https://iopscience.iop.org/article/10.1088/2632-2153/ac298c/meta]
Experiment scripts may be found in the BayesOpt/bayesopt_experiments directory.
We recommend using a conda environment
conda create -n hetbo python==3.7
conda activate hetbo
conda install matplotlib numpy pytest scikit-learn pandas
conda install scipy==1.1.0
conda install -c conda-forge rdkit
To reproduce the experiments in the paper using the default values of the hyperparameters
python toy_sin_noise.py
python toy_branin_hoo.py
python toy_soil.py
python freesolv.py
python synthetic_func_experiments.py
python gamma_experiments.py
To experiment with different hyperparameter settings
python toy_sin_noise.py --penalty 1 --aleatoric_weight 1
--random_trials 50 --bayes_opt_iters 5
To adapt the algorithm to your own dataset follow the toy_soil.py template making use of the dataloder as
per your task requirements.
If you find this code useful please consider citing the following paper [https://iopscience.iop.org/article/10.1088/2632-2153/ac298c/meta]
@article{griffiths2021achieving,
title={Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation},
author={Griffiths, Ryan-Rhys and Aldrick, Alexander A and Garcia-Ortegon, Miguel and Lalchand, Vidhi and others},
journal={Machine Learning: Science and Technology},
volume={3},
number={1},
pages={015004},
year={2021},
publisher={IOP Publishing}
}