qpots 1.0.6

A BoTorch wrapper for solving multiobjective optimization problems with an implementation of the qPOTS algorithm.

qPOTS: Batch Pareto Optimal Thompson Sampling

This repository contains the code for qPOTS, a multi-objective Bayesian optimization algorithm. Read the paper on arXiv: here.

This repository is maintained by the Computational Complex Engineered Systems Design Laboratory (CSDL) at Penn State.

Read the documentation.

Cite the paper:

@article{renganathan2023qpots,
  title={qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling},
  author={Renganathan, S Ashwin and Carlson, Kade E},
  journal={arXiv preprint arXiv:2310.15788},
  year={2023}
}

Installing qPOTS

To install qPOTS with pip, run the following command in a terminal:

pip install qPOTS

To build from source, clone the repository and run pip in the top-level directory:

git clone https://github.com/csdlpsu/qpots
cd qpots
pip install .

This will install all of the necessary dependencies except for the MATLAB Engine, which is only needed for TS-EMO. To install the MATLAB Engine, follow the instructions at this link: Install MATLAB Engine for Python.

Note: The MATLAB Engine is only required if you plan on using TS-EMO and must be installed for Python>=3.10 and the corresponding MATLAB version on your machine (MATLAB installation required). The BoTorch implementation of the other acquisition functions (including qPOTS) only requires Python>=3.10 and the dependencies automatically installed by pip.

Quick Start

A quick demonstration of qPOTS is below. This code can be run to test your qPOTS installation.

For more thorough demonstrations on how qPOTS should be used, please see the examples/ directory.

import torch
import warnings
import time
from botorch.utils.transforms import unnormalize

warnings.filterwarnings('ignore')
device = torch.device("cpu")

from qpots.acquisition import Acquisition
from qpots.model_object import ModelObject
from qpots.function import Function
from qpots.utils.utils import expected_hypervolume

args = dict(
    {
        "ntrain": 20,
        "iters": 50,
        "reps": 20,
        "q": 1,
        "wd": ".",
        "ref_point": torch.tensor([-300.0, -18.0]),
        "dim": 2,
        "nobj": 2,
        "ncons": 0,
        "nystrom": 0,
        "nychoice": "pareto",
        "ngen": 10,
    }
)

tf = Function('branincurrin', dim=args["dim"], nobj=args["nobj"])
f = tf.evaluate
bounds = tf.get_bounds()

torch.manual_seed(1023)

train_x = torch.rand([args["ntrain"], args["dim"]], dtype=torch.float64)
train_y = f(unnormalize(train_x, bounds))

gps = ModelObject(train_x=train_x, train_y=train_y, bounds=bounds, nobj=args["nobj"], ncons=0, device=device)
gps.fit_gp()

acq = Acquisition(tf, gps, device=device, q=args["q"])

for i in range(args["iters"]):
    t1 = time.time()
    newx = acq.qpots(bounds, i, **args)
    t2 = time.time()

    newy = f(unnormalize(newx.reshape(-1, args["dim"]), bounds))
    hv, _ = expected_hypervolume(gps, ref_point=args['ref_point'])

    print(f"Iteration: {i}, New candidate: {newx}, Time: {t2 - t1}, HV: {hv}")

    train_x = torch.row_stack([train_x, newx.view(-1, args["dim"])])
    train_y = torch.row_stack([train_y, newy])
    gps = ModelObject(train_x, train_y, bounds, args["nobj"], args["ncons"], device=device)
    gps.fit_gp()

This code prints the results to the terminal. If this works, then congratulations, you have successfully installed qPOTS!