Description:

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 http://docs.optunity.net. 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:

This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.

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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