Skip to content
A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github
doc
emukit
integration_tests
notebooks
requirements
tests
.gitattributes
.gitignore
.travis.yml
CHANGELOG.md
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
MANIFEST.in
NOTICE
README.md
readthedocs.yml
setup.cfg
setup.py

README.md

Emukit

Master Branch Build Status | Documentation Status | Tests Coverage | GitHub License

Website | Documentation | Contribution Guide

Emukit is a highly adaptable Python toolkit for enriching decision making under uncertainty. This is particularly pertinent to complex systems where data is scarce or difficult to acquire. In these scenarios, propagating well-calibrated uncertainty estimates within a design loop or computational pipeline ensures that constrained resources are used effectively.

The main features currently available in Emukit are:

  • Multi-fidelity emulation: build surrogate models when data is obtained from multiple information sources that have different fidelity and/or cost;
  • Bayesian optimisation: optimise physical experiments and tune parameters of machine learning algorithms;
  • Experimental design/Active learning: design the most informative experiments and perform active learning with machine learning models;
  • Sensitivity analysis: analyse the influence of inputs on the outputs of a given system;
  • Bayesian quadrature: efficiently compute the integrals of functions that are expensive to evaluate.

Emukit is agnostic to the underlying modelling framework, which means you can use any tool of your choice in the Python ecosystem to build the machine learning model, and still be able to use Emukit.

Installation

To install emukit, simply run

pip install emukit

For other install options, see our documentation.

Dependencies / Prerequisites

Emukit's primary dependencies are Numpy and GPy. See requirements.

Getting started

For examples see our tutorial notebooks.

Documentation

To learn more about Emukit, refer to our documentation.

To learn about emulation as a concept, check out the Emukit playground project.

License

Emukit is licensed under Apache 2.0. Please refer to LICENSE and NOTICE for further license information.

You can’t perform that action at this time.