This is the code associated with the paper "Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization."
Please cite our work if you find it useful.
@inproceedings{daulton2022pr,
title={Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization},
author={Samuel Daulton and Xingchen Wan and and David Eriksson and Maximilian Balandat and Michael A. Osborne and Eytan Bakshy},
booktitle={Advances in Neural Information Processing Systems 35},
year={2022}
}
From the base bo_pr directory run:
pip install -e .
The code is structured in three parts.
- The utilities for constructing the acquisition functions and other helper methods are defined in
discrete_mixed_bo/. - The experiments are found in and ran from within
experiments/. Themain.pyis used to run the experiments, and the experiment configurations are found in theconfig.jsonfile of each sub-directory.
The individual experiment outputs were left out to avoid inflating the file size.
To run a basic benchmark based on the config.json file in experiments/<experiment_name> using <algorithm>:
cd experiments
python main.py <experiment_name> <algorithm> <seed>
The code refers to the algorithms using the following labels:
algorithms = [
("sobol", "Sobol"),
("cont_optim__round_after__ei", "Cont. Relax."),
("pr__ei", "PR"),
("exact_round__fin_diff__ei", "Exact Round"),
("exact_round__ste__ei", "Exact Round + STE"),
("cont_optim__round_after__ts", "Cont. Relax. + TS"),
("pr__ts", "PR + TS"),
("exact_round__fin_diff__ts", "Exact Round + TS"),
("exact_round__ste__ts", "Exact Round + TS + STE"),
("cont_optim__round_after__ucb", "Cont. Relax. + UCB"
("pr__ucb", "PR + UCB"),
("exact_round__fin_diff__ucb", "Exact Round + UCB"),
("exact_round__ste__ucb", "Exact Round + UCB + STE"),
("cont_optim__round_after__ehvi", "Cont. Relax."),
("pr__ehvi", "PR"),
("exact_round__fin_diff__ehvi", "Exact Round"),
("exact_round__ste__ehvi", "Exact Round + STE"),
("cont_optim__round_after__nehvi-1", ""Cont. Relax. + TS"),
("pr__nehvi-1", "PR + TS"),
("exact_round__fin_diff__nehvi-1", "Exact Round + TS"),
("exact_round__ste__nehvi-1","Exact Round + TS + STE"),
]
These algorithms can be modified by additional arguments in the config.json file such as use_trust_region and a "acqf_kwargs" dictionary containing the pair"pr_use_analytic": true.
Each folder under experiments/ corresponds to the experiments in the paper according to the following mapping:
experiments = {
"ackley13": "Ackley",
"cco": "Cellular Network",
"chemistry": "Chemical Reaction",
"mixed_int_f1": "Mixed Int F1",
"mixed_oil": "Oil Sorbent",
"rosenbrock10_scaled": "Rosenbrock",
"svm": "SVM",
"welded_beam": "Welded Beam",
}
Additional folder provide the configurations for the trust region variants (simply adding "use_trust_region": true) and analytic PR where feasible simply adding ("acqf_kwargs": {"pr_use_analytic": true}). Note: this code can heavily exploit a GPU if available.
This repository is MIT licensed, as found in the LICENSE file.
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