from __future__ import annotations
import copy
from typing import Dict
from typing import Tuple
from typing import TYPE_CHECKING
import optuna
from optuna.distributions import BaseDistribution
if TYPE_CHECKING:
from optuna.study import Study
def _calculate(
trials: list[optuna.trial.FrozenTrial],
include_pruned: bool = False,
search_space: Dict[str, BaseDistribution] | None = None,
cached_trial_number: int = -1,
) -> Tuple[Dict[str, BaseDistribution] | None, int]:
states_of_interest = [
optuna.trial.TrialState.COMPLETE,
optuna.trial.TrialState.WAITING,
optuna.trial.TrialState.RUNNING,
]
if include_pruned:
states_of_interest.append(optuna.trial.TrialState.PRUNED)
trials_of_interest = [trial for trial in trials if trial.state in states_of_interest]
next_cached_trial_number = (
trials_of_interest[-1].number + 1 if len(trials_of_interest) > 0 else -1
)
for trial in reversed(trials_of_interest):
if cached_trial_number > trial.number:
break
if not trial.state.is_finished():
next_cached_trial_number = trial.number
continue
if search_space is None:
search_space = copy.copy(trial.distributions)
continue
search_space = {
name: distribution
for name, distribution in search_space.items()
if trial.distributions.get(name) == distribution
}
return search_space, next_cached_trial_number
[docs]
class IntersectionSearchSpace:
"""A class to calculate the intersection search space of a :class:`~optuna.study.Study`.
Intersection search space contains the intersection of parameter distributions that have been
suggested in the completed trials of the study so far.
If there are multiple parameters that have the same name but different distributions,
neither is included in the resulting search space
(i.e., the parameters with dynamic value ranges are excluded).
Note that an instance of this class is supposed to be used for only one study.
If different studies are passed to
:func:`~optuna.search_space.IntersectionSearchSpace.calculate`,
a :obj:`ValueError` is raised.
Args:
include_pruned:
Whether pruned trials should be included in the search space.
"""
def __init__(self, include_pruned: bool = False) -> None:
self._cached_trial_number: int = -1
self._search_space: Dict[str, BaseDistribution] | None = None
self._study_id: int | None = None
self._include_pruned = include_pruned
[docs]
def calculate(self, study: Study) -> Dict[str, BaseDistribution]:
"""Returns the intersection search space of the :class:`~optuna.study.Study`.
Args:
study:
A study with completed trials. The same study must be passed for one instance
of this class through its lifetime.
Returns:
A dictionary containing the parameter names and parameter's distributions sorted by
parameter names.
"""
if self._study_id is None:
self._study_id = study._study_id
else:
# Note that the check below is meaningless when `InMemoryStorage` is used
# because `InMemoryStorage.create_new_study` always returns the same study ID.
if self._study_id != study._study_id:
raise ValueError("`IntersectionSearchSpace` cannot handle multiple studies.")
self._search_space, self._cached_trial_number = _calculate(
study.get_trials(deepcopy=False),
self._include_pruned,
self._search_space,
self._cached_trial_number,
)
search_space = self._search_space or {}
search_space = dict(sorted(search_space.items(), key=lambda x: x[0]))
return copy.deepcopy(search_space)
[docs]
def intersection_search_space(
trials: list[optuna.trial.FrozenTrial],
include_pruned: bool = False,
) -> Dict[str, BaseDistribution]:
"""Return the intersection search space of the given trials.
Intersection search space contains the intersection of parameter distributions that have been
suggested in the completed trials of the study so far.
If there are multiple parameters that have the same name but different distributions,
neither is included in the resulting search space
(i.e., the parameters with dynamic value ranges are excluded).
.. note::
:class:`~optuna.search_space.IntersectionSearchSpace` provides the same functionality with
a much faster way. Please consider using it if you want to reduce execution time
as much as possible.
Args:
trials:
A list of trials.
include_pruned:
Whether pruned trials should be included in the search space.
Returns:
A dictionary containing the parameter names and parameter's distributions sorted by
parameter names.
"""
search_space, _ = _calculate(trials, include_pruned)
search_space = search_space or {}
search_space = dict(sorted(search_space.items(), key=lambda x: x[0]))
return search_space