Metadata-Version: 2.1
Name: scikit-optimize-w
Version: 2020.1.2
Summary: Sequential model-based optimization toolbox.
Home-page: https://github.com/mimba/scikit-optimize
Author: The scikit-optimize contributors
License: BSD 3-clause
Description: 
        |Logo|
        
        |pypi| |conda| |Travis Status| |CircleCI Status| |binder| |gitter| |Zenodo DOI|
        
        Scikit-Optimize-W
        =================
        
        Scikit-Optimize-W is a fork of Scikit-Optimize or ``skopt`` which 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
        -------
        
        You can install the latest release with:
        ::
        
            pip install scikit-optimize-w
        
        This installs an essential version of scikit-optimize. To install scikit-optimize
        with plotting functionality, you can instead do:
        ::
        
            pip install 'scikit-optimize-w[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-w
        
        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/mimba/scikit-optimize.git
            cd scikit-optimize-w
            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/mimba/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
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Provides-Extra: plots
