from collections import OrderedDict
import copy
from typing import Dict
from typing import Optional
import optuna
from optuna.distributions import BaseDistribution
from optuna.study import BaseStudy
[docs]class IntersectionSearchSpace(object):
"""A class to calculate the intersection search space of a :class:`~optuna.study.BaseStudy`.
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.samplers.IntersectionSearchSpace.calculate`,
a :obj:`ValueError` is raised.
"""
[docs] def __init__(self) -> None:
self._cursor = -1 # type: int
self._search_space = None # type: Optional[Dict[str, BaseDistribution]]
self._study_id = None # type: Optional[int]
[docs] def calculate(
self, study: BaseStudy, ordered_dict: bool = False
) -> Dict[str, BaseDistribution]:
"""Returns the intersection search space of the :class:`~optuna.study.BaseStudy`.
Args:
study:
A study with completed trials.
ordered_dict:
A boolean flag determining the return type.
If :obj:`False`, the returned object will be a :obj:`dict`.
If :obj:`True`, the returned object will be an :obj:`collections.OrderedDict`
sorted by keys, i.e. parameter names.
Returns:
A dictionary containing the parameter names and parameter's distributions.
"""
if self._study_id is None:
self._study_id = study._study_id
else:
# Note that the check below is meaningless when `InMemortyStorage` is used
# because `InMemortyStorage.create_new_study` always returns the same study ID.
if self._study_id != study._study_id:
raise ValueError("`IntersectionSearchSpace` cannot handle multiple studies.")
next_cursor = self._cursor
for trial in reversed(study._storage.get_all_trials(study._study_id, deepcopy=False)):
if self._cursor > trial.number:
break
if not trial.state.is_finished():
next_cursor = trial.number
if trial.state != optuna.trial.TrialState.COMPLETE:
continue
if self._search_space is None:
self._search_space = copy.copy(trial.distributions)
continue
delete_list = []
for param_name, param_distribution in self._search_space.items():
if param_name not in trial.distributions:
delete_list.append(param_name)
elif trial.distributions[param_name] != param_distribution:
delete_list.append(param_name)
for param_name in delete_list:
del self._search_space[param_name]
self._cursor = next_cursor
search_space = self._search_space or {}
if ordered_dict:
search_space = OrderedDict(sorted(search_space.items(), key=lambda x: x[0]))
return copy.deepcopy(search_space)
[docs]def intersection_search_space(
study: BaseStudy, ordered_dict: bool = False
) -> Dict[str, BaseDistribution]:
"""Return the intersection search space of the :class:`~optuna.study.BaseStudy`.
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.samplers.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:
study:
A study with completed trials.
ordered_dict:
A boolean flag determining the return type.
If :obj:`False`, the returned object will be a :obj:`dict`.
If :obj:`True`, the returned object will be an :obj:`collections.OrderedDict` sorted by
keys, i.e. parameter names.
Returns:
A dictionary containing the parameter names and parameter's distributions.
"""
return IntersectionSearchSpace().calculate(study, ordered_dict=ordered_dict)