qpots 1.0.5

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.

Installing qPOTS

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

pip install qPOTS

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.

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 .

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!