Metadata-Version: 2.0
Name: Optunity
Version: 1.1.0
Summary: Optimization routines for hyperparameter tuning.
Home-page: http://www.optunity.net
Author: Marc Claesen
Author-email: marc.claesen@esat.kuleuven.be
License: LICENSE.txt
Keywords: machine learning,parameter tuning,hyperparameter optimization,meta-optimization,direct search,model selection,particle swarm optimization
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4

.. image:: logo/logo.png
    :alt: Optunity
    :align: left

.. image:: https://travis-ci.org/claesenm/optunity.svg?branch=master
    :target: https://travis-ci.org/claesenm/optunity
    :align: right

.. image:: https://readthedocs.org/projects/optunity/badge/?version=latest
    :alt: Documentation Status
    :scale: 100%
    :target: https://readthedocs.org/projects/optunity/

.. image:: https://img.shields.io/pypi/dm/Optunity.svg           
    :target: https://pypi.python.org/pypi/optunity

.. image:: https://img.shields.io/pypi/v/Optunity.svg            
    :target: https://pypi.python.org/pypi/optunity


=========

Optunity is a library containing various optimizers for hyperparameter tuning.
Hyperparameter tuning is a recurrent problem in many machine learning tasks,
both supervised and unsupervised. Tuning examples include optimizing 
regularization or kernel parameters.

>From an optimization point of view, the tuning problem can be considered as 
follows: the objective function is non-convex, non-differentiable and 
typically expensive to evaluate.

This package provides several distinct approaches to solve such problems including 
some helpful facilities such as cross-validation and a plethora of score functions.

The Optunity library is implemented in Python and allows straightforward
integration in other machine learning environments, including R and MATLAB.

If you have any comments, suggestions you can get in touch with us at gitter:

.. image:: https://badges.gitter.im/Join%20Chat.svg
   :alt: Join the chat at https://gitter.im/claesenm/optunity
   :target: https://gitter.im/claesenm/optunity?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge

To get started with Optunity on Linux, issue the following commands::

    git clone https://github.com/claesenm/optunity.git
    echo "export PYTHONPATH=$PYTHONPATH:$(pwd)/optunity" >> ~/.bashrc

Afterwards, importing ``optunity`` should work in Python::

    #!/usr/bin/env python
    import optunity

Optunity is developed at the STADIUS lab of the dept. of electrical engineering
at KU Leuven (ESAT). Optunity is free software, using a BSD license.

For more information, please refer to the following pages:
http://www.optunity.net

Contributors
============

The main contributors to Optunity are:

* Marc Claesen: framework design & implementation, communication infrastructure,
  MATLAB wrapper and all solvers.

* Jaak Simm: R wrapper.



