evalml.tuners.RandomSearchTuner

class evalml.tuners.RandomSearchTuner(pipeline_hyperparameter_ranges, random_state=0, with_replacement=False, replacement_max_attempts=10)

Random Search Optimizer.

Example

>>> tuner = RandomSearchTuner({'My Component': {'param a': [0.0, 10.0], 'param b': ['a', 'b', 'c']}}, random_state=42)
>>> proposal = tuner.propose()
>>> assert proposal.keys() == {'My Component'}
>>> assert proposal['My Component'] == {'param a': 3.7454011884736254, 'param b': 'c'}

Methods

__init__	Sets up check for duplication if needed.
add	Not applicable to random search tuner as generated parameters are not dependent on scores of previous parameters.
is_search_space_exhausted	Checks if it is possible to generate a set of valid parameters.
propose	Generate a unique set of parameters.

evalml.tuners.GridSearchTuner.propose evalml.tuners.RandomSearchTuner.__init__