HEBO: Heteroscedastic Evolutionary Bayesian Optimization

HEBO is a Bayesian optimization algorithm developed by Huawei Noah's Ark lab. It supports mixed-discrete parameter and several types of underlying probabilistic models.

For more information: Cowen-Rivers, A.I., et al. 2022. HEBO: pushing the limits of sample-efficient hyper-parameter optimisation. Journal of Artificial Intelligence Research, 74, pp.1269-1349, DOI: 10.1613/jair.1.13643

Installation

pip install sb-arch-opt[hebo]

Usage

API Reference

The get_hebo_optimizer function can be used to get an interface object for running the optimization. The hebo object also has an ask-tell interface if needed.

from sb_arch_opt.algo.hebo_interface import get_hebo_optimizer

problem = ...  # Subclass of ArchOptProblemBase

# Get the interface and optimization loop
hebo = get_hebo_optimizer(problem, n_init=100, seed=42)

# Run the optimization loop
hebo.optimize(n_infill=50)

# Extract data as a pymoo Population object
pop = hebo.pop