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.This package provides several distinct approaches to solve such problems including some helpful facilities such as cross-validation and a plethora of score functions.
From an optimization point of view, the tuning problem can be considered as follows: the objective function is non-convex, non-smooth and typically expensive to evaluate. Tuning examples include optimizing regularization or kernel parameters.
The Optunity library is implemented in Python and allows straightforward integration in other machine learning environments. Optunity is currently also supported in |wrapper-r|, |wrapper-matlab|, |wrapper-octave| and Java through Jython.
Optunity is free software, using a BSD-style license.
As a simple example of Optunity's features, the code below demonstrates how to tune an SVM with RBF kernel using Optunity and scikit-learn.
This involves optimizing the hyperparameters
For more examples, please see our |examples|.
Issue the following commands to get started on Linux:
git clone https://github.com/claesenm/optunity.git export PYTHONPATH=$PYTHONPATH:$(pwd)/optunity/
optunity should work in Python:
python -c 'import optunity'
For a proper installation, run the following:
python optunity/setup.py install
or, if you have pip:
pip install optunity
Installation may require superuser priviliges.
Optunity is developed at the STADIUS lab of the dept. of electrical engineering at KU Leuven (ESAT). The main contributors to Optunity are:
- Python package
- framework design & implementation
- solver implementation
- communication protocol design & implementation
- MATLAB wrapper
- Octave wrapper
- communication protocol design
- R wrapper
- code examples