skopt_tuner¶

Module Contents¶

Bayesian Optimizer.

class evalml.tuners.skopt_tuner.SKOptTuner(pipeline_hyperparameter_ranges, random_seed=0)[source]¶

Bayesian Optimizer.

Parameters

pipeline_hyperparameter_ranges (dict) – A set of hyperparameter ranges corresponding to a pipeline’s parameters.
random_seed (int) – The seed for the random number generator. Defaults to 0.

Methods

`add`	Add score to sample.
`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.

add(self, pipeline_parameters, score)[source]¶

Add score to sample.

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)¶

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

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.

random_search_tuner tuner