A hyperparameter optimization and meta-learning toolbox for convenient and fast prototyping of machine-learning models.
- Very simple API
- Thoroughly tested code base
- Compatible with any python machine-learning framework
- Utilize state of the art optimization techniques like:
- Simulated annealing
- Evolution strategy
- Bayesian optimization
- High performance: Optimizer time is neglectable for most models
- Choose from a variety of different optimization extensions to improve the optimization
|Optimization Techniques||Tested and Supported Packages||Optimization Extentions|
The most recent version of Hyperactive is available on PyPi:
pip install hyperactive
The following algorithms are of my own design and, to my knowledge, do not yet exist in the technical literature. If any of these algorithms still exist I ask you to share it with me in an issue.
A combination between simulated annealing and random search.
Inspired by hyperband optimization.