Skip to content
forked from marpulli/emukit

A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

License

Notifications You must be signed in to change notification settings

bouhlelma/emukit

 
 

Repository files navigation

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

Currently only installation from sources is supported.

Dependencies / Prerequisites

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

Install from sources

To install Emukit from source, create a local folder where you would like to put Emukit source code, and run following commands:

git clone https://github.com/amzn/Emukit.git
cd Emukit
python setup.py install

Alternatively you can run

pip install git+https://github.com/amzn/Emukit.git

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.

About

A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

Resources

License

Code of conduct

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%