Overview

The package parameterspace is used to define parameter spaces consisting of mixed types (continuous, integer, categorical) with conditionalities and priors. It allows for easy specification of the parameters and their dependencies.

The ParameterSpace object can then be used to sample random configurations from the prior and convert any valid configuration into a numerical representation. This numerical representation has the following properties:

• it results in a numpy ndarray of type numpy.float64
• transformed representation between 0 and 1 (uniform) including integers, ordinal and categorical parameters
• inactive parameters are masked as numpy.nan values

This allows to easily use optimizers that expect continuous domains to be used on more complicated problems because ParameterSpace can convert any numerical vector representation inside the unit hypercube into a valid configuration. The function might not be smooth, but for robust methods (like genetic algorithms/evolutionary strategies) this might still be valuable.

The package can be installed from PyPi with:

pip install parameterspace

ConfigSpace Compatibility

In case you are used to working with ConfigSpace or for other reasons have space definitions in the ConfigSpace format around, you can convert them into ParameterSpace instances with ease. Just note that any colons : in the ConfigSpace parameter names will be converted to underscores _.

import json
from parameterspace.configspace_utils import parameterspace_from_configspace_dict

with open("config_space.json", "r") as fh:
  cs = json.load(fh)

ps = parameterspace_from_configspace_dict(cs)