apsis - Framework for Automated Optimization of Machine Learning Hyper Parameters
This provides a tool for researchers and practitioners to automate hyperparameter tuning, but it is incremental as it combines existing methods into a unified framework.
The paper introduces apsis, a flexible Python framework for hyperparameter optimization that includes random search and Bayesian optimization, designed to be adaptable to any machine learning code and compatible with common frameworks like scikit-learn.
The apsis toolkit presented in this paper provides a flexible framework for hyperparameter optimization and includes both random search and a bayesian optimizer. It is implemented in Python and its architecture features adaptability to any desired machine learning code. It can easily be used with common Python ML frameworks such as scikit-learn. Published under the MIT License other researchers are heavily encouraged to check out the code, contribute or raise any suggestions. The code can be found at github.com/FrederikDiehl/apsis.