Source code for evalml.tuners.grid_search_tuner

import itertools

from skopt.space import Integer, Real

from .tuner import Tuner
from .tuner_exceptions import NoParamsException


[docs]class GridSearchTuner(Tuner): """Grid Search Optimizer. Example: >>> tuner = GridSearchTuner({'My Component': {'param a': [0.0, 10.0], 'param b': ['a', 'b', 'c']}}, n_points=5) >>> proposal = tuner.propose() >>> assert proposal.keys() == {'My Component'} >>> assert proposal['My Component'] == {'param a': 0.0, 'param b': 'a'} """
[docs] def __init__(self, pipeline_hyperparameter_ranges, n_points=10, random_seed=0): """Generate all of the possible points to search for in the grid Arguments: pipeline_hyperparameter_ranges (dict): a set of hyperparameter ranges corresponding to a pipeline's parameters n_points (int): The number of points to sample from along each dimension defined in the ``space`` argument random_seed (int): Seed for random number generator. Unused in this class, defaults to 0. """ super().__init__(pipeline_hyperparameter_ranges, random_seed=random_seed) raw_dimensions = list() for dimension in self._search_space_ranges: # Categorical dimension if isinstance(dimension, list): range_values = dimension elif isinstance(dimension, (Real, Integer, tuple)): if ( isinstance(dimension, (tuple)) and isinstance(dimension[0], (int, float)) and isinstance(dimension[1], (int, float)) ): if dimension[1] > dimension[0]: low = dimension[0] high = dimension[1] else: error_text = "Upper bound must be greater than lower bound. Parameter lower bound is {0} and upper bound is {1}" error_text = error_text.format(dimension[0], dimension[1]) raise ValueError(error_text) else: low = dimension.low high = dimension.high delta = (high - low) / (n_points - 1) if isinstance(dimension, Integer): range_values = [int((x * delta) + low) for x in range(n_points)] else: range_values = [(x * delta) + low for x in range(n_points)] raw_dimensions.append(range_values) self._grid_points = itertools.product(*raw_dimensions) self.curr_params = None
[docs] def add(self, pipeline_parameters, score): """Not applicable to grid 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
[docs] def propose(self): """Returns parameters from _grid_points iterations If all possible combinations of parameters have been scored, then ``NoParamsException`` is raised. Returns: dict: proposed pipeline parameters """ if 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. """ try: self.curr_params = next(self._grid_points) return False except StopIteration: raise NoParamsException( "Grid search has exhausted all possible parameters." ) return True