  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.
