skopt_tuner¶
Bayesian Optimizer.
Module Contents¶
Classes Summary¶
Bayesian Optimizer. |
Contents¶
-
evalml.tuners.skopt_tuner.logger¶
-
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 score to sample.
Optional. If possible search space for tuner is finite, this method indicates whether or not all possible parameters have been scored.
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
- Raises
Exception – If skopt tuner errors.
ParameterError – If skopt receives invalid parameters.
-
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