Project details for Optunity

Logo Optunity 1.1.0

by claesenm - July 19, 2015, 12:23:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Optunity provides a variety of solvers for hyperparameter tuning problems. The software offers a diverse set of solvers to optimize hyperparameters.

The first major release of Optunity (stable). For documentation, please refer to Optunity is compatible with Python 2.7 and above.

The following features are available:

Wide variety of solvers:

  • particle swarm optimization
  • Nelder-Mead
  • grid search
  • random search
  • Sobol sequences
  • CMA-ES (requires DEAP and NumPy)
  • TPE (requires Hyperopt and NumPy)

Generic cross-validation functionality:

  • support for strata and clusters
  • folds are reusable for multiple learning algorithm/solver combinations

Various quality metrics for models (score/loss functions).

Univariate domain constraints on hyperparameters.

Support for parallel objective function evaluations.

Support for structured search spaces.

This release provides Optunity functionality in the following environments: * MATLAB R Octave

Changes to previous version:

The following features have been added:

  • new solvers
  • tree of Parzen estimators (requires Hyperopt)
  • Sobol sequences
  • Octave wrapper
  • support for structured search spaces, which can be nested
  • improved cross-validation routines to return more detailed results
  • most Python examples are now available as notebooks
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Agnostic
Tags: Optimization, Hyperparameter Tuning
Archive: download here


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