pyGPGO: Bayesian optimization for Python

pyGPGO is a simple and modular Python (>3.5) package for Bayesian optimization. It supports:

  • Different surrogate models: Gaussian Processes, Student-t Processes, Random Forests, Gradient Boosting Machines.
  • Type II Maximum-Likelihood of covariance function hyperparameters.
  • MCMC sampling for full-Bayesian inference of hyperparameters (via pyMC3).
  • Integrated acquisition functions

Check us out on Github.

pyGPGO uses other well-known packages of the Python scientific ecosystem as dependencies:

  • numpy
  • scipy
  • joblib
  • scikit-learn
  • pyMC3
  • theano

These are automatically taken care for in the requirements file.

What is Bayesian Optimization?

Bayesian optimization is a framework that is useful in several scenarios:

  • Your objective function has no closed-form.
  • No access to gradients
  • In presence of noise
  • It may be expensive to evaluate.

The bayesian optimization framework uses a surrogate model to approximate the objective function and chooses to optimize it according to some acquisition function. This framework gives a lot of freedom to the user in terms of optimization choices:

  • Surrogate model choice
  • Covariance function choice
  • Acquisition function behaviour
  • Hyperparameter treatment

pyGPGO provides an extensive range of choices in each of the previous points, in a modular way. We recommend checking [Shahriari2016] for an in-depth review of the framework if you’re interested.

How do I get started with pyGPGO?

Install the latest stable release from pyPI:

pip install pyGPGO

or if you’re feeling adventurous, install the latest devel version from the Github repository:

pip install git+

pyGPGO is straightforward to use, we only need to specify:

  • A function to optimize according to some parameters.
  • A dictionary defining parameters, their type and bounds.
  • A surrogate model, such as a Gaussian Process, from the surrogates module. Some surrogate models require defining a covariance function, with hyperparameters. (from the covfunc module)
  • An acquisition strategy, from the acquisition module.
  • A GPGO instance, from the GPGO module

A simple example can be checked below:

import numpy as np
from pyGPGO.covfunc import squaredExponential
from pyGPGO.acquisition import Acquisition
from pyGPGO.surrogates.GaussianProcess import GaussianProcess
from pyGPGO.GPGO import GPGO

def f(x):
    return (np.sin(x))

sexp = squaredExponential()
gp = GaussianProcess(sexp)
acq = Acquisition(mode='ExpectedImprovement')
param = {'x': ('cont', [0, 2 * np.pi])}

gpgo = GPGO(gp, acq, f, param)

There are a couple of tutorials to help get you started on the tutorials folder.

For a full list of features with explanations check our Features section

pyGPGO is not the only package for bayesian optimization in Python, other excellent alternatives exist. For an in-depth comparison of the features offered by pyGPGO compared to other sofware, check the following section:


[Shahriari2016]Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & De Freitas, N. (2016). Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE.

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