{ "info": { "author": "", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "# Emukit\n\n[![Master Branch Build Status](https://travis-ci.org/amzn/emukit.svg?branch=master)](https://travis-ci.org/amzn/emukit) |\n[![Documentation Status](https://readthedocs.org/projects/emukit/badge/?version=latest)](https://emukit.readthedocs.io/en/latest/?badge=latest) |\n[![Tests Coverage](https://codecov.io/gh/amzn/emukit/branch/master/graph/badge.svg)](https://codecov.io/gh/amzn/emukit) |\n[![GitHub License](https://img.shields.io/github/license/amzn/emukit.svg)](https://github.com/amzn/emukit/blob/master/LICENSE)\n\n[Website](https://amzn.github.io/emukit/) |\n[Documentation](https://emukit.readthedocs.io/) |\n[Contribution Guide](CONTRIBUTING.md)\n\nEmukit 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.\n\nThe main features currently available in Emukit are:\n\n* **Multi-fidelity emulation:** build surrogate models when data is obtained from multiple information sources that have different fidelity and/or cost;\n* **Bayesian optimisation:** optimise physical experiments and tune parameters of machine learning algorithms;\n* **Experimental design/Active learning:** design the most informative experiments and perform active learning with machine learning models;\n* **Sensitivity analysis:** analyse the influence of inputs on the outputs of a given system;\n* **Bayesian quadrature:** efficiently compute the integrals of functions that are expensive to evaluate.\n\nEmukit 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.\n\n## Installation\n\nTo install emukit, simply run\n```\npip install emukit\n```\n\nFor other install options, see our [documentation](https://emukit.readthedocs.io/en/latest/installation.html).\n\n### Dependencies / Prerequisites\nEmukit's primary dependencies are Numpy and GPy.\nSee [requirements](requirements/requirements.txt).\n\n## Getting started\nFor examples see our [tutorial notebooks](http://nbviewer.jupyter.org/github/amzn/emukit/blob/master/notebooks/index.ipynb).\n\n## Documentation\nTo learn more about Emukit, refer to our [documentation](https://emukit.readthedocs.io).\n\nTo learn about emulation as a concept, check out the [Emukit playground](https://github.com/amzn/Emukit-playground) project.\n\n## License\n\nEmukit is licensed under Apache 2.0. 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