Source code for optuna.samplers._grid

import collections
import itertools
import random
from typing import Any
from typing import cast
from typing import Dict
from typing import List
from typing import Mapping
from typing import Optional
from typing import Sequence
from typing import Union
import warnings

from optuna.distributions import BaseDistribution
from optuna.logging import get_logger
from optuna.samplers import BaseSampler
from import Study
from optuna.trial import FrozenTrial
from optuna.trial import TrialState

GridValueType = Union[str, float, int, bool, None]
SortableParamValueSequenceType = Union[List[str], List[float], List[int], List[bool]]

_logger = get_logger(__name__)

[docs]class GridSampler(BaseSampler): """Sampler using grid search. With :class:`~optuna.samplers.GridSampler`, the trials suggest all combinations of parameters in the given search space during the study. Example: .. testcode:: import optuna def objective(trial): x = trial.suggest_float("x", -100, 100) y = trial.suggest_int("y", -100, 100) return x ** 2 + y ** 2 search_space = {"x": [-50, 0, 50], "y": [-99, 0, 99]} study = optuna.create_study(sampler=optuna.samplers.GridSampler(search_space)) study.optimize(objective) Note: :class:`~optuna.samplers.GridSampler` automatically stops the optimization if all combinations in the passed ``search_space`` have already been evaluated, internally invoking the :func:`` method. Note: :class:`~optuna.samplers.GridSampler` does not take care of a parameter's quantization specified by discrete suggest methods but just samples one of values specified in the search space. E.g., in the following code snippet, either of ``-0.5`` or ``0.5`` is sampled as ``x`` instead of an integer point. .. testcode:: import optuna def objective(trial): # The following suggest method specifies integer points between -5 and 5. x = trial.suggest_float("x", -5, 5, step=1) return x ** 2 # Non-int points are specified in the grid. search_space = {"x": [-0.5, 0.5]} study = optuna.create_study(sampler=optuna.samplers.GridSampler(search_space)) study.optimize(objective, n_trials=2) Note: A parameter configuration in the grid is not considered finished until its trial is finished. Therefore, during distributed optimization where trials run concurrently, different workers will occasionally suggest the same parameter configuration. The total number of actual trials may therefore exceed the size of the grid. Note: The grid is randomly shuffled and the order in which parameter configurations are suggested may vary. This is to reduce duplicate suggestions during distributed optimization. Args: search_space: A dictionary whose key and value are a parameter name and the corresponding candidates of values, respectively. """ def __init__(self, search_space: Mapping[str, Sequence[GridValueType]]) -> None: for param_name, param_values in search_space.items(): for value in param_values: self._check_value(param_name, value) self._search_space = collections.OrderedDict() for param_name, param_values in sorted(search_space.items(), key=lambda x: x[0]): param_values = cast(SortableParamValueSequenceType, param_values) self._search_space[param_name] = sorted(param_values) self._all_grids = list(itertools.product(*self._search_space.values())) self._param_names = sorted(search_space.keys()) self._n_min_trials = len(self._all_grids)
[docs] def infer_relative_search_space( self, study: Study, trial: FrozenTrial ) -> Dict[str, BaseDistribution]: return {}
[docs] def sample_relative( self, study: Study, trial: FrozenTrial, search_space: Dict[str, BaseDistribution] ) -> Dict[str, Any]: # Instead of returning param values, GridSampler puts the target grid id as a system attr, # and the values are returned from `sample_independent`. This is because the distribution # object is hard to get at the beginning of trial, while we need the access to the object # to validate the sampled value. target_grids = self._get_unvisited_grid_ids(study) if len(target_grids) == 0: # This case may occur with distributed optimization or trial queue. If there is no # target grid, `GridSampler` evaluates a visited, duplicated point with the current # trial. After that, the optimization stops. _logger.warning( "`GridSampler` is re-evaluating a configuration because the grid has been " "exhausted. This may happen due to a timing issue during distributed optimization " "or when re-running optimizations on already finished studies." ) # One of all grids is randomly picked up in this case. target_grids = list(range(len(self._all_grids))) # In distributed optimization, multiple workers may simultaneously pick up the same grid. # To make the conflict less frequent, the grid is chosen randomly. grid_id = random.choice(target_grids) study._storage.set_trial_system_attr(trial._trial_id, "search_space", self._search_space) study._storage.set_trial_system_attr(trial._trial_id, "grid_id", grid_id) return {}
[docs] def sample_independent( self, study: Study, trial: FrozenTrial, param_name: str, param_distribution: BaseDistribution, ) -> Any: if param_name not in self._search_space: message = "The parameter name, {}, is not found in the given grid.".format(param_name) raise ValueError(message) # TODO(c-bata): Reduce the number of duplicated evaluations on multiple workers. # Current selection logic may evaluate the same parameters multiple times. # See for details. grid_id = trial.system_attrs["grid_id"] param_value = self._all_grids[grid_id][self._param_names.index(param_name)] contains = param_distribution._contains(param_distribution.to_internal_repr(param_value)) if not contains: warnings.warn( f"The value `{param_value}` is out of range of the parameter `{param_name}`. " f"The value will be used but the actual distribution is: `{param_distribution}`." ) return param_value
[docs] def after_trial( self, study: Study, trial: FrozenTrial, state: TrialState, values: Optional[Sequence[float]], ) -> None: target_grids = self._get_unvisited_grid_ids(study) if len(target_grids) == 0: study.stop() elif len(target_grids) == 1: grid_id = study._storage.get_trial_system_attrs(trial._trial_id)["grid_id"] if grid_id == target_grids[0]: study.stop()
@staticmethod def _check_value(param_name: str, param_value: Any) -> None: if param_value is None or isinstance(param_value, (str, int, float, bool)): return raise ValueError( "{} contains a value with the type of {}, which is not supported by " "`GridSampler`. Please make sure a value is `str`, `int`, `float`, `bool`" " or `None`.".format(param_name, type(param_value)) ) def _get_unvisited_grid_ids(self, study: Study) -> List[int]: # List up unvisited grids based on already finished ones. visited_grids = [] running_grids = [] # We directly query the storage to get trials here instead of `study.get_trials`, # since some pruners such as `HyperbandPruner` use the study transformed # to filter trials. See for details. trials = study._storage.get_all_trials(study._study_id, deepcopy=False) for t in trials: if "grid_id" in t.system_attrs and self._same_search_space( t.system_attrs["search_space"] ): if t.state.is_finished(): visited_grids.append(t.system_attrs["grid_id"]) elif t.state == TrialState.RUNNING: running_grids.append(t.system_attrs["grid_id"]) unvisited_grids = set(range(self._n_min_trials)) - set(visited_grids) - set(running_grids) # If evaluations for all grids have been started, return grids that have not yet finished # because all grids should be evaluated before stopping the optimization. if len(unvisited_grids) == 0: unvisited_grids = set(range(self._n_min_trials)) - set(visited_grids) return list(unvisited_grids) def _same_search_space(self, search_space: Mapping[str, Sequence[GridValueType]]) -> bool: if set(search_space.keys()) != set(self._search_space.keys()): return False for param_name in search_space.keys(): if len(search_space[param_name]) != len(self._search_space[param_name]): return False param_values = cast(SortableParamValueSequenceType, search_space[param_name]) for i, param_value in enumerate(sorted(param_values)): if param_value != self._search_space[param_name][i]: return False return True