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()
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)))
study.stop()
elif len(target_grids) == 1:
# When there is only one target grid, optimization stops after the current trial
# finishes.
study.stop()
# 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 {}
