def infer_relative_search_space(self, study, trial):
# type: (InTrialStudy, FrozenTrial) -> Dict[str, BaseDistribution]
search_space = {}
for name, distribution in samplers.intersection_search_space(study).items():
if distribution.single():
if not isinstance(distribution, distributions.CategoricalDistribution):
# `skopt` cannot handle non-categorical distributions that contain just
# a single value, so we skip this distribution.
#
# Note that `Trial` takes care of this distribution during suggestion.
continue
search_space[name] = distribution
return search_space
def _get_distributions(
study: Study,
params: Optional[List[str]]) -> Dict[str, BaseDistribution]:
_check_evaluate_args(study, params)
if params is None:
return intersection_search_space(study, ordered_dict=True)
# New temporary required to pass mypy. Seems like a bug.
params_not_none = params
assert params_not_none is not None
# Compute the search space based on the subset of trials containing all parameters.
distributions = None
for trial in study.trials:
if trial.state != TrialState.COMPLETE:
continue
trial_distributions = trial.distributions
if not all(name in trial_distributions for name in params_not_none):
continue
if distributions is None:
distributions = dict(
filter(
lambda name_and_distribution: name_and_distribution[0] in
params_not_none,
trial_distributions.items(),
))
continue
if any(trial_distributions[name] != distribution
for name, distribution in distributions.items()):
raise ValueError(
"Parameters importances cannot be assessed with dynamic search spaces if "
"parameters are specified. Specified parameters: {}.".format(
params))
assert distributions is not None # Required to pass mypy.
distributions = OrderedDict(
sorted(distributions.items(),
key=lambda name_and_distribution: name_and_distribution[0]))
return distributions
def test_intersection_search_space() -> None:
search_space = IntersectionSearchSpace()
study = create_study()
# No trial.
assert search_space.calculate(study) == {}
assert search_space.calculate(study) == intersection_search_space(study)
# First trial.
study.optimize(
lambda t: t.suggest_float("y", -3, 3) + t.suggest_int("x", 0, 10),
n_trials=1)
assert search_space.calculate(study) == {
"x": IntUniformDistribution(low=0, high=10),
"y": UniformDistribution(low=-3, high=3),
}
assert search_space.calculate(study) == intersection_search_space(study)
# Returning sorted `OrderedDict` instead of `dict`.
assert search_space.calculate(study, ordered_dict=True) == OrderedDict([
("x", IntUniformDistribution(low=0, high=10)),
("y", UniformDistribution(low=-3, high=3)),
])
assert search_space.calculate(
study,
ordered_dict=True) == intersection_search_space(study,
ordered_dict=True)
# Second trial (only 'y' parameter is suggested in this trial).
study.optimize(lambda t: t.suggest_float("y", -3, 3), n_trials=1)
assert search_space.calculate(study) == {
"y": UniformDistribution(low=-3, high=3)
}
assert search_space.calculate(study) == intersection_search_space(study)
# Failed or pruned trials are not considered in the calculation of
# an intersection search space.
def objective(trial: Trial, exception: Exception) -> float:
trial.suggest_float("z", 0, 1)
raise exception
study.optimize(lambda t: objective(t, RuntimeError()),
n_trials=1,
catch=(RuntimeError, ))
study.optimize(lambda t: objective(t, TrialPruned()), n_trials=1)
assert search_space.calculate(study) == {
"y": UniformDistribution(low=-3, high=3)
}
assert search_space.calculate(study) == intersection_search_space(study)
# If two parameters have the same name but different distributions,
# those are regarded as different parameters.
study.optimize(lambda t: t.suggest_float("y", -1, 1), n_trials=1)
assert search_space.calculate(study) == {}
assert search_space.calculate(study) == intersection_search_space(study)
# The search space remains empty once it is empty.
study.optimize(
lambda t: t.suggest_float("y", -3, 3) + t.suggest_int("x", 0, 10),
n_trials=1)
assert search_space.calculate(study) == {}
assert search_space.calculate(study) == intersection_search_space(study)