{ "info": { "author": "Felix Berkenkamp", "author_email": "befelix@inf.ethz.ch", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5" ], "description": "====================================\nSafeOpt - Safe Bayesian Optimization\n====================================\n\n.. image:: https://travis-ci.org/befelix/SafeOpt.svg?branch=master\n :target: https://travis-ci.org/befelix/SafeOpt\n :alt: Build Status\n.. image:: https://readthedocs.org/projects/safeopt/badge/?version=latest\n :target: http://safeopt.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\nThis code implements an adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1]_, [2]_. It also provides a more scalable implementation based on [3]_ as well as an implementation for the original algorithm in [4]_.\nThe code can be used to automatically optimize a performance measures subject to a safety constraint by adapting parameters.\nThe prefered way of citing this code is by referring to [1] or [2].\n\n.. image:: http://img.youtube.com/vi/GiqNQdzc5TI/0.jpg\n :target: http://www.youtube.com/watch?feature=player_embedded&v=GiqNQdzc5TI\n :alt: Youtube video\n\n.. [1] F. Berkenkamp, A. P. Schoellig, A. Krause,\n `Safe Controller Optimization for Quadrotors with Gaussian Processes `_\n in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 491-496.\n\n.. [2] F. Berkenkamp, A. Krause, A. P. Schoellig,\n `Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics `_,\n ArXiv, 2016, arXiv:1602.04450 [cs.RO].\n\n.. [3] Rikky R.P.R. Duivenvoorden, Felix Berkenkamp, Nicolas Carion, Andreas Krause, Angela P. Schoellig,\n `Constrained Bayesian optimization with Particle Swarms for Safe Adaptive Controller Tuning `_,\n in Proc. of the IFAC (International Federation of Automatic Control) World Congress, 2017.\n\n.. [4] Y. Sui, A. Gotovos, J. W. Burdick, and A. Krause,\n `Safe exploration for optimization with Gaussian processes `_\n in Proc. of the International Conference on Machine Learning (ICML), 2015, pp. 997\u20131005.\n\n\nInstallation\n------------\nThe easiest way to install the necessary python libraries is by installing pip (e.g. ``apt-get install python-pip`` on Ubuntu) and running\n\n``pip install safeopt``\n\nAlternatively you can clone the repository and install it using\n\n``python setup.py install``\n\nUsage\n-----\n\n*The easiest way to get familiar with the library is to run the interactive example ipython notebooks!*\n\nMake sure that the ``ipywidgets`` module is installed. All functions and classes are documented on `Read The Docs `_.\n\n\nLicense\n-------\n\nThe code is licenced under the MIT license and free to use by anyone without any restrictions.\n\n\n", "description_content_type": null, "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/befelix/SafeOpt", "keywords": "Bayesian optimization,Safety", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "safeopt", "package_url": "https://pypi.org/project/safeopt/", "platform": "", "project_url": "https://pypi.org/project/safeopt/", "project_urls": { "Homepage": "https://github.com/befelix/SafeOpt" }, "release_url": "https://pypi.org/project/safeopt/0.15/", "requires_dist": [ "GPy (>=0.8)", "future", "matplotlib (>=1.3)", "numpy (>=1.7)", "scipy (>=0.12)" ], "requires_python": "", "summary": "Safe Bayesian optimization", "version": "0.15" }, "last_serial": 3132516, "releases": { "0.14": [ { "comment_text": "", "digests": { "md5": "1241663379faa919d0d302e7d03e7605", "sha256": "bc8d179796d1ca4873f77241899e572217a2d464f4f35e9919ecf1359198d44f" }, "downloads": -1, "filename": "safeopt-0.14-py2.py3-none-any.whl", "has_sig": false, "md5_digest": "1241663379faa919d0d302e7d03e7605", "packagetype": "bdist_wheel", "python_version": "py2.py3", "requires_python": null, "size": 21760, "upload_time": "2017-08-15T08:56:24", "url": "https://files.pythonhosted.org/packages/3d/b3/626c836955b60523aac372d2c676710b77b7353be41a2064a6cdc6609080/safeopt-0.14-py2.py3-none-any.whl" } ], "0.15": [ { "comment_text": "", "digests": { "md5": "db75c4cf56664221962b313c1360a149", "sha256": "f4f4bbd78b2fc1a0b64bff0b5a5cb09751c7e4722d78ccb1debfb01925ddb416" }, "downloads": -1, "filename": "safeopt-0.15-py2.py3-none-any.whl", "has_sig": false, "md5_digest": "db75c4cf56664221962b313c1360a149", "packagetype": "bdist_wheel", "python_version": "py2.py3", "requires_python": null, "size": 20199, "upload_time": "2017-08-29T16:43:26", "url": "https://files.pythonhosted.org/packages/16/85/a463ab8fce6ece47175d89211ba0b24410c24ca1ba482b3d9fe8de19f70d/safeopt-0.15-py2.py3-none-any.whl" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "db75c4cf56664221962b313c1360a149", "sha256": "f4f4bbd78b2fc1a0b64bff0b5a5cb09751c7e4722d78ccb1debfb01925ddb416" }, "downloads": -1, "filename": "safeopt-0.15-py2.py3-none-any.whl", "has_sig": false, "md5_digest": "db75c4cf56664221962b313c1360a149", "packagetype": "bdist_wheel", "python_version": "py2.py3", "requires_python": null, "size": 20199, "upload_time": "2017-08-29T16:43:26", "url": "https://files.pythonhosted.org/packages/16/85/a463ab8fce6ece47175d89211ba0b24410c24ca1ba482b3d9fe8de19f70d/safeopt-0.15-py2.py3-none-any.whl" } ] }