|Logo|
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Scikit-Optimize
===============
Scikit-Optimize, or ``skopt``, is a simple and efficient library to
minimize (very) expensive and noisy black-box functions. It implements
several methods for sequential model-based optimization. ``skopt`` aims
to be accessible and easy to use in many contexts.
The library is built on top of NumPy, SciPy and Scikit-Learn.
We do not perform gradient-based optimization. For gradient-based
optimization algorithms look at
``scipy.optimize``
`here <http://docs.scipy.org/doc/scipy/reference/optimize.html>`_.
.. figure:: https://github.com/scikit-optimize/scikit-optimize/blob/master/media/bo-objective.png
:alt: Approximated objective
Approximated objective function after 50 iterations of ``gp_minimize``.
Plot made using ``skopt.plots.plot_objective``.
Important links
---------------
- Static documentation - `Static
documentation <https://scikit-optimize.github.io/>`__
- Example notebooks - can be found in examples_.
- Issue tracker -
https://github.com/scikit-optimize/scikit-optimize/issues
- Releases - https://pypi.python.org/pypi/scikit-optimize
Install
-------
scikit-optimize requires
* Python >= 3.6
* NumPy (>= 1.13.3)
* SciPy (>= 0.19.1)
* joblib (>= 0.11)
* scikit-learn >= 0.20
* matplotlib >= 2.0.0
You can install the latest release with:
::
pip install scikit-optimize
This installs an essential version of scikit-optimize. To install scikit-optimize
with plotting functionality, you can instead do:
::
pip install 'scikit-optimize[plots]'
This will install matplotlib along with scikit-optimize.
In addition there is a `conda-forge <https://conda-forge.org/>`_ package
of scikit-optimize:
::
conda install -c conda-forge scikit-optimize
Using conda-forge is probably the easiest way to install scikit-optimize on
Windows.
Getting started
---------------
Find the minimum of the noisy function ``f(x)`` over the range
``-2 < x < 2`` with ``skopt``:
.. code:: python
import numpy as np
from skopt import gp_minimize
def f(x):
return (np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) +
np.random.randn() * 0.1)
res = gp_minimize(f, [(-2.0, 2.0)])
For more control over the optimization loop you can use the ``skopt.Optimizer``
class:
.. code:: python
from skopt import Optimizer
opt = Optimizer([(-2.0, 2.0)])
for i in range(20):
suggested = opt.ask()
y = f(suggested)
opt.tell(suggested, y)
print('iteration:', i, suggested, y)
Read our `introduction to bayesian
optimization <https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html>`__
and the other examples_.
Development
-----------
The library is still experimental and under heavy development. Checkout
the `next
milestone <https://github.com/scikit-optimize/scikit-optimize/milestones>`__
for the plans for the next release or look at some `easy
issues <https://github.com/scikit-optimize/scikit-optimize/issues?q=is%3Aissue+is%3Aopen+label%3AEasy>`__
to get started contributing.
The development version can be installed through:
::
git clone https://github.com/scikit-optimize/scikit-optimize.git
cd scikit-optimize
pip install -e.
Run all tests by executing ``pytest`` in the top level directory.
To only run the subset of tests with short run time, you can use ``pytest -m 'fast_test'`` (``pytest -m 'slow_test'`` is also possible). To exclude all slow running tests try ``pytest -m 'not slow_test'``.
This is implemented using pytest `attributes <https://docs.pytest.org/en/latest/mark.html>`__. If a tests runs longer than 1 second, it is marked as slow, else as fast.
All contributors are welcome!
Making a Release
~~~~~~~~~~~~~~~~
The release procedure is almost completely automated. By tagging a new release
travis will build all required packages and push them to PyPI. To make a release
create a new issue and work through the following checklist:
* update the version tag in ``__init__.py``
* update the version tag mentioned in the README
* check if the dependencies in ``setup.py`` are valid or need unpinning
* check that the ``doc/whats_new/v0.X.rst`` is up to date
* did the last build of master succeed?
* create a `new release <https://github.com/scikit-optimize/scikit-optimize/releases>`__
* ping `conda-forge <https://github.com/conda-forge/scikit-optimize-feedstock>`__
Before making a release we usually create a release candidate. If the next
release is v0.X then the release candidate should be tagged v0.Xrc1 in
``__init__.py``. Mark a release candidate as a "pre-release"
on GitHub when you tag it.
Commercial support
------------------
Feel free to `get in touch <mailto:tim@wildtreetech.com>`_ if you need commercial
support or would like to sponsor development. Resources go towards paying
for additional work by seasoned engineers and researchers.
Made possible by
----------------
The scikit-optimize project was made possible with the support of
.. image:: https://avatars1.githubusercontent.com/u/18165687?v=4&s=128
:alt: Wild Tree Tech
:target: http://wildtreetech.com
.. image:: https://i.imgur.com/lgxboT5.jpg
:alt: NYU Center for Data Science
:target: https://cds.nyu.edu/
.. image:: https://i.imgur.com/V1VSIvj.jpg
:alt: NSF
:target: https://www.nsf.gov
.. image:: https://i.imgur.com/3enQ6S8.jpg
:alt: Northrop Grumman
:target: http://www.northropgrumman.com/Pages/default.aspx
If your employer allows you to work on scikit-optimize during the day and would like
recognition, feel free to add them to the "Made possible by" list.
.. |pypi| image:: https://img.shields.io/pypi/v/scikit-optimize.svg
:target: https://pypi.python.org/pypi/scikit-optimize
.. |conda| image:: https://anaconda.org/conda-forge/scikit-optimize/badges/version.svg
:target: https://anaconda.org/conda-forge/scikit-optimize
.. |Travis Status| image:: https://travis-ci.org/scikit-optimize/scikit-optimize.svg?branch=master
:target: https://travis-ci.org/scikit-optimize/scikit-optimize
.. |CircleCI Status| image:: https://circleci.com/gh/scikit-optimize/scikit-optimize/tree/master.svg?style=shield&circle-token=:circle-token
:target: https://circleci.com/gh/scikit-optimize/scikit-optimize
.. |Logo| image:: https://avatars2.githubusercontent.com/u/18578550?v=4&s=80
.. |binder| image:: https://mybinder.org/badge.svg
:target: https://mybinder.org/v2/gh/scikit-optimize/scikit-optimize/master?filepath=examples
.. |gitter| image:: https://badges.gitter.im/scikit-optimize/scikit-optimize.svg
:target: https://gitter.im/scikit-optimize/Lobby
.. |Zenodo DOI| image:: https://zenodo.org/badge/54340642.svg
:target: https://zenodo.org/badge/latestdoi/54340642
.. _examples: https://scikit-optimize.github.io/stable/auto_examples/index.html
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"description": "\r\n|Logo|\r\n\r\n|pypi| |conda| |Travis Status| |CircleCI Status| |binder| |gitter| |Zenodo DOI|\r\n\r\nScikit-Optimize\r\n===============\r\n\r\nScikit-Optimize, or ``skopt``, is a simple and efficient library to\r\nminimize (very) expensive and noisy black-box functions. It implements\r\nseveral methods for sequential model-based optimization. ``skopt`` aims\r\nto be accessible and easy to use in many contexts.\r\n\r\nThe library is built on top of NumPy, SciPy and Scikit-Learn.\r\n\r\nWe do not perform gradient-based optimization. For gradient-based\r\noptimization algorithms look at\r\n``scipy.optimize``\r\n`here <http://docs.scipy.org/doc/scipy/reference/optimize.html>`_.\r\n\r\n.. figure:: https://github.com/scikit-optimize/scikit-optimize/blob/master/media/bo-objective.png\r\n :alt: Approximated objective\r\n\r\nApproximated objective function after 50 iterations of ``gp_minimize``.\r\nPlot made using ``skopt.plots.plot_objective``.\r\n\r\nImportant links\r\n---------------\r\n\r\n- Static documentation - `Static\r\n documentation <https://scikit-optimize.github.io/>`__\r\n- Example notebooks - can be found in examples_.\r\n- Issue tracker -\r\n https://github.com/scikit-optimize/scikit-optimize/issues\r\n- Releases - https://pypi.python.org/pypi/scikit-optimize\r\n\r\nInstall\r\n-------\r\n\r\nscikit-optimize requires\r\n\r\n* Python >= 3.6\r\n* NumPy (>= 1.13.3)\r\n* SciPy (>= 0.19.1)\r\n* joblib (>= 0.11)\r\n* scikit-learn >= 0.20\r\n* matplotlib >= 2.0.0\r\n\r\nYou can install the latest release with:\r\n::\r\n\r\n pip install scikit-optimize\r\n\r\nThis installs an essential version of scikit-optimize. To install scikit-optimize\r\nwith plotting functionality, you can instead do:\r\n::\r\n\r\n pip install 'scikit-optimize[plots]'\r\n\r\nThis will install matplotlib along with scikit-optimize.\r\n\r\nIn addition there is a `conda-forge <https://conda-forge.org/>`_ package\r\nof scikit-optimize:\r\n::\r\n\r\n conda install -c conda-forge scikit-optimize\r\n\r\nUsing conda-forge is probably the easiest way to install scikit-optimize on\r\nWindows.\r\n\r\n\r\nGetting started\r\n---------------\r\n\r\nFind the minimum of the noisy function ``f(x)`` over the range\r\n``-2 < x < 2`` with ``skopt``:\r\n\r\n.. code:: python\r\n\r\n import numpy as np\r\n from skopt import gp_minimize\r\n\r\n def f(x):\r\n return (np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) +\r\n np.random.randn() * 0.1)\r\n\r\n res = gp_minimize(f, [(-2.0, 2.0)])\r\n\r\n\r\nFor more control over the optimization loop you can use the ``skopt.Optimizer``\r\nclass:\r\n\r\n.. code:: python\r\n\r\n from skopt import Optimizer\r\n\r\n opt = Optimizer([(-2.0, 2.0)])\r\n\r\n for i in range(20):\r\n suggested = opt.ask()\r\n y = f(suggested)\r\n opt.tell(suggested, y)\r\n print('iteration:', i, suggested, y)\r\n\r\n\r\nRead our `introduction to bayesian\r\noptimization <https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html>`__\r\nand the other examples_.\r\n\r\n\r\nDevelopment\r\n-----------\r\n\r\nThe library is still experimental and under heavy development. Checkout\r\nthe `next\r\nmilestone <https://github.com/scikit-optimize/scikit-optimize/milestones>`__\r\nfor the plans for the next release or look at some `easy\r\nissues <https://github.com/scikit-optimize/scikit-optimize/issues?q=is%3Aissue+is%3Aopen+label%3AEasy>`__\r\nto get started contributing.\r\n\r\nThe development version can be installed through:\r\n\r\n::\r\n\r\n git clone https://github.com/scikit-optimize/scikit-optimize.git\r\n cd scikit-optimize\r\n pip install -e.\r\n\r\nRun all tests by executing ``pytest`` in the top level directory.\r\n\r\nTo only run the subset of tests with short run time, you can use ``pytest -m 'fast_test'`` (``pytest -m 'slow_test'`` is also possible). To exclude all slow running tests try ``pytest -m 'not slow_test'``.\r\n\r\nThis is implemented using pytest `attributes <https://docs.pytest.org/en/latest/mark.html>`__. If a tests runs longer than 1 second, it is marked as slow, else as fast.\r\n\r\nAll contributors are welcome!\r\n\r\n\r\nMaking a Release\r\n~~~~~~~~~~~~~~~~\r\n\r\nThe release procedure is almost completely automated. By tagging a new release\r\ntravis will build all required packages and push them to PyPI. To make a release\r\ncreate a new issue and work through the following checklist:\r\n\r\n* update the version tag in ``__init__.py``\r\n* update the version tag mentioned in the README\r\n* check if the dependencies in ``setup.py`` are valid or need unpinning\r\n* check that the ``doc/whats_new/v0.X.rst`` is up to date\r\n* did the last build of master succeed?\r\n* create a `new release <https://github.com/scikit-optimize/scikit-optimize/releases>`__\r\n* ping `conda-forge <https://github.com/conda-forge/scikit-optimize-feedstock>`__\r\n\r\nBefore making a release we usually create a release candidate. If the next\r\nrelease is v0.X then the release candidate should be tagged v0.Xrc1 in\r\n``__init__.py``. Mark a release candidate as a \"pre-release\"\r\non GitHub when you tag it.\r\n\r\n\r\nCommercial support\r\n------------------\r\n\r\nFeel free to `get in touch <mailto:tim@wildtreetech.com>`_ if you need commercial\r\nsupport or would like to sponsor development. Resources go towards paying\r\nfor additional work by seasoned engineers and researchers.\r\n\r\n\r\nMade possible by\r\n----------------\r\n\r\nThe scikit-optimize project was made possible with the support of\r\n\r\n.. image:: https://avatars1.githubusercontent.com/u/18165687?v=4&s=128\r\n :alt: Wild Tree Tech\r\n :target: http://wildtreetech.com\r\n\r\n.. image:: https://i.imgur.com/lgxboT5.jpg\r\n :alt: NYU Center for Data Science\r\n :target: https://cds.nyu.edu/\r\n\r\n.. image:: https://i.imgur.com/V1VSIvj.jpg\r\n :alt: NSF\r\n :target: https://www.nsf.gov\r\n\r\n.. image:: https://i.imgur.com/3enQ6S8.jpg\r\n :alt: Northrop Grumman\r\n :target: http://www.northropgrumman.com/Pages/default.aspx\r\n\r\nIf your employer allows you to work on scikit-optimize during the day and would like\r\nrecognition, feel free to add them to the \"Made possible by\" list.\r\n\r\n\r\n.. |pypi| image:: https://img.shields.io/pypi/v/scikit-optimize.svg\r\n :target: https://pypi.python.org/pypi/scikit-optimize\r\n.. |conda| image:: https://anaconda.org/conda-forge/scikit-optimize/badges/version.svg\r\n :target: https://anaconda.org/conda-forge/scikit-optimize\r\n.. |Travis Status| image:: https://travis-ci.org/scikit-optimize/scikit-optimize.svg?branch=master\r\n :target: https://travis-ci.org/scikit-optimize/scikit-optimize\r\n.. |CircleCI Status| image:: https://circleci.com/gh/scikit-optimize/scikit-optimize/tree/master.svg?style=shield&circle-token=:circle-token\r\n :target: https://circleci.com/gh/scikit-optimize/scikit-optimize\r\n.. |Logo| image:: https://avatars2.githubusercontent.com/u/18578550?v=4&s=80\r\n.. |binder| image:: https://mybinder.org/badge.svg\r\n :target: https://mybinder.org/v2/gh/scikit-optimize/scikit-optimize/master?filepath=examples\r\n.. |gitter| image:: https://badges.gitter.im/scikit-optimize/scikit-optimize.svg\r\n :target: https://gitter.im/scikit-optimize/Lobby\r\n.. |Zenodo DOI| image:: https://zenodo.org/badge/54340642.svg\r\n :target: https://zenodo.org/badge/latestdoi/54340642\r\n.. _examples: https://scikit-optimize.github.io/stable/auto_examples/index.html\r\n",
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