HyperParameterSampling Class

Abstract base class for all hyperparameter sampling algorithms.

This class encapsulates the hyperparameter space, the sampling method, and additional properties for derived sampling classes: BayesianParameterSampling, GridParameterSampling, and RandomParameterSampling.

Initialize HyperParameterSampling.

Constructor

HyperParameterSampling(sampling_method_name, parameter_space, properties=None, supported_distributions=None, distributions_validators=None)

Name	Description
sampling_method_name Required	str The name of the sampling method.
parameter_space Required	dict A dictionary containing each parameter and its distribution.
properties	dict A dictionary with additional properties for the algorithm. Default value: None
supported_distributions	set[str] A list of the supported distribution methods. The default None indicates all distributions are supported as described in module parameter_expressions. Default value: None
sampling_method_name Required	str The name of the sampling method.
parameter_space Required	dict A dictionary containing each parameter and its distribution.
properties Required	dict A dictionary with additional properties for the algorithm.
supported_distributions Required	set[str] A list of the supported distribution methods. The default of None indicates all distributions are supported as described in module parameter_expressions.
distributions_validators	dict A dictionary that maps a distribution name to a function that validates if it is a valid distribution for the sampling method used. The default None indicates that no particular validators are needed. Default value: None

to_json

Return JSON representing the hyperparameter sampling object.

Return JSON representing the hyperparameter sampling object.

to_json()

Type	Description
str	JSON formatted sampling policy.