Description

A flexible hyper-parameter optimization library. Most hyper-parameter optimization libraries impose three main restrictions : they control the optimization loop they force the points to be represented by vectors the priors are very restricted, e.g gaussian, uniform or discrete uniform the goal of fluentopt is to provide hyper-parameter optimization library where : the optimization loop is controlled by the user (but we will provide also helpers). the points can be represented by a python dictionary to express conditionals rather than just a vector. The dictionaries can also support strings, varying length lists and special objects like 'None'. the priors of hyper-parameters are not restricted to some pre-defined probability distributions. Users will just provide samplers as a python function, that is, a function that takes a seed and returns a python dictionary.