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.
- Inheritance
-
HyperParameterSampling
Constructor
HyperParameterSampling(sampling_method_name, parameter_space, properties=None, supported_distributions=None, distributions_validators=None)
Parameters
Name | Description |
---|---|
sampling_method_name
Required
|
The name of the sampling method. |
parameter_space
Required
|
A dictionary containing each parameter and its distribution. |
properties
|
A dictionary with additional properties for the algorithm. Default value: None
|
supported_distributions
|
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
|
The name of the sampling method. |
parameter_space
Required
|
A dictionary containing each parameter and its distribution. |
properties
Required
|
A dictionary with additional properties for the algorithm. |
supported_distributions
Required
|
A list of the supported distribution methods. The default of None indicates all distributions are supported as described in module parameter_expressions. |
distributions_validators
|
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
|
Methods
to_json |
Return JSON representing the hyperparameter sampling object. |
to_json
Return JSON representing the hyperparameter sampling object.
to_json()
Returns
Type | Description |
---|---|
JSON formatted sampling policy. |