tuner

Module Contents

Classes Summary

Tuner

Defines API for base Tuner classes.

Contents

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

Defines API for base Tuner classes.

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.

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]

Register a set of hyperparameters with the score obtained from training a pipeline with those hyperparameters.

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

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.

Returns

Proposed pipeline parameters

Return type

dict