Source code for evalml.tuners.random_search_tuner

from skopt import Space

from evalml.tuners import NoParamsException, Tuner
from evalml.utils import get_random_state

[docs]class RandomSearchTuner(Tuner): """Random Search Optimizer. Example: >>> tuner = RandomSearchTuner({'My Component': {'param a': [0.0, 10.0], 'param b': ['a', 'b', 'c']}}, random_seed=42) >>> proposal = tuner.propose() >>> assert proposal.keys() == {'My Component'} >>> assert proposal['My Component'] == {'param a': 3.7454011884736254, 'param b': 'c'} """
[docs] def __init__(self, pipeline_hyperparameter_ranges, random_seed=0, with_replacement=False, replacement_max_attempts=10): """ Sets up check for duplication if needed. Arguments: pipeline_hyperparameter_ranges (dict): a set of hyperparameter ranges corresponding to a pipeline's parameters random_state (int): Unused in this class. Defaults to 0. with_replacement (bool): If false, only unique hyperparameters will be shown replacement_max_attempts (int): The maximum number of tries to get a unique set of random parameters. Only used if tuner is initalized with with_replacement=True random_seed (int): Seed for random number generator. Defaults to 0. """ super().__init__(pipeline_hyperparameter_ranges, random_seed=random_seed) self._space = Space(self._search_space_ranges) self._random_state = get_random_state(random_seed) self._with_replacement = with_replacement self._replacement_max_attempts = replacement_max_attempts self._used_parameters = set() self._used_parameters.add(()) self.curr_params = None
[docs] def add(self, pipeline_parameters, score): """Not applicable to random search tuner as generated parameters are not dependent on scores of previous parameters. Arguments: 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 """ pass
def _get_sample(self): return tuple(self._space.rvs(random_state=self._random_state)[0])
[docs] def propose(self): """Generate a unique set of parameters. If tuner was initialized with ``with_replacement=True`` and the tuner is unable to generate a unique set of parameters after ``replacement_max_attempts`` tries, then ``NoParamsException`` is raised. Returns: dict: Proposed pipeline parameters """ if not len(self._search_space_ranges): return self._convert_to_pipeline_parameters({}) if self._with_replacement: return self._convert_to_pipeline_parameters(self._get_sample()) elif not self.curr_params: self.is_search_space_exhausted() params = self.curr_params self.curr_params = None return self._convert_to_pipeline_parameters(params)
[docs] def is_search_space_exhausted(self): """Checks if it is possible to generate a set of valid parameters. Stores generated parameters in ``self.curr_params`` to be returned by ``propose()``. Raises: NoParamsException: If a search space is exhausted, then this exception is thrown. Returns: bool: If no more valid parameters exists in the search space, return false. """ if self._with_replacement: return False else: curr_params = () attempts = 0 while curr_params in self._used_parameters: if attempts >= self._replacement_max_attempts: raise NoParamsException("Cannot create a unique set of unexplored parameters. Try expanding the search space.") return True attempts += 1 curr_params = self._get_sample() self._used_parameters.add(curr_params) self.curr_params = curr_params return False