tuner#

Base Tuner class.

Module Contents#

Base Tuner class.

class evalml.tuners.tuner.Tuner(pipeline_hyperparameter_ranges, random_seed=0)[source]#

Base Tuner class.

Tuners implement different strategies for sampling from a search space. They’re used in EvalML to search the space of pipeline hyperparameters.

Parameters

pipeline_hyperparameter_ranges (dict) – a set of hyperparameter ranges corresponding to a pipeline’s parameters.
random_seed (int) – The random state. Defaults to 0.

Methods

`add`	Register a set of hyperparameters with the score obtained from training a pipeline with those hyperparameters.
`get_starting_parameters`	Gets the starting parameters given the pipeline hyperparameter range.
`is_search_space_exhausted`	Optional. If possible search space for tuner is finite, this method indicates whether or not all possible parameters have been scored.
`propose`	Returns a suggested set of parameters to train and score a pipeline with, based off the search space dimensions and prior samples.

abstract add(self, pipeline_parameters, score)[source]#

Parameters

pipeline_parameters (dict) – a dict of the parameters used to evaluate a pipeline
score (float) – the score obtained by evaluating the pipeline with the provided parameters

Returns

None

get_starting_parameters(self, hyperparameter_ranges, random_seed=0)[source]#

Gets the starting parameters given the pipeline hyperparameter range.

Parameters

hyperparameter_ranges (dict) – The custom hyperparameter ranges passed in during search. Used to determine the starting parameters.
random_seed (int) – The random seed to use. Defaults to 0.

Returns

The starting parameters, randomly chosen, to initialize a pipeline with.

Return type

dict

is_search_space_exhausted(self)[source]#

Optional. If possible search space for tuner is finite, this method indicates whether or not all possible parameters have been scored.

Returns: Returns true if all possible parameters in a search space has been scored.
Return type: bool

abstract propose(self)[source]#

Returns a suggested set of parameters to train and score a pipeline with, based off the search space dimensions and prior samples.