from __future__ import annotations
from typing import Any
from typing import Dict
from typing import Optional
from typing import Sequence
from typing import TYPE_CHECKING
import warnings
from optuna._experimental import experimental_class
from optuna.distributions import BaseDistribution
from optuna.samplers import BaseSampler
from optuna.trial import FrozenTrial
from optuna.trial import TrialState
if TYPE_CHECKING:
from optuna.study import Study
[docs]
@experimental_class("2.4.0")
class PartialFixedSampler(BaseSampler):
"""Sampler with partially fixed parameters.
Example:
After several steps of optimization, you can fix the value of ``y`` and re-optimize it.
.. testcode::
import optuna
def objective(trial):
x = trial.suggest_float("x", -1, 1)
y = trial.suggest_int("y", -1, 1)
return x**2 + y
study = optuna.create_study()
study.optimize(objective, n_trials=10)
best_params = study.best_params
fixed_params = {"y": best_params["y"]}
partial_sampler = optuna.samplers.PartialFixedSampler(fixed_params, study.sampler)
study.sampler = partial_sampler
study.optimize(objective, n_trials=10)
Args:
fixed_params:
A dictionary of parameters to be fixed.
base_sampler:
A sampler which samples unfixed parameters.
"""
def __init__(self, fixed_params: Dict[str, Any], base_sampler: BaseSampler) -> None:
self._fixed_params = fixed_params
self._base_sampler = base_sampler
[docs]
def reseed_rng(self) -> None:
self._base_sampler.reseed_rng()
[docs]
def infer_relative_search_space(
self, study: Study, trial: FrozenTrial
) -> Dict[str, BaseDistribution]:
search_space = self._base_sampler.infer_relative_search_space(study, trial)
# Remove fixed params from relative search space to return fixed values.
for param_name in self._fixed_params.keys():
if param_name in search_space:
del search_space[param_name]
return search_space
[docs]
def sample_relative(
self,
study: Study,
trial: FrozenTrial,
search_space: Dict[str, BaseDistribution],
) -> Dict[str, Any]:
# Fixed params are never sampled here.
return self._base_sampler.sample_relative(study, trial, search_space)
[docs]
def sample_independent(
self,
study: Study,
trial: FrozenTrial,
param_name: str,
param_distribution: BaseDistribution,
) -> Any:
if param_name not in self._fixed_params:
# Unfixed params are sampled here.
return self._base_sampler.sample_independent(
study, trial, param_name, param_distribution
)
else:
# Fixed params are sampled here.
# Check if a parameter value is contained in the range of this distribution.
param_value = self._fixed_params[param_name]
param_value_in_internal_repr = param_distribution.to_internal_repr(param_value)
contained = param_distribution._contains(param_value_in_internal_repr)
if not contained:
warnings.warn(
f"Fixed parameter '{param_name}' with value {param_value} is out of range "
f"for distribution {param_distribution}."
)
return param_value
[docs]
def before_trial(self, study: Study, trial: FrozenTrial) -> None:
self._base_sampler.before_trial(study, trial)
[docs]
def after_trial(
self,
study: Study,
trial: FrozenTrial,
state: TrialState,
values: Optional[Sequence[float]],
) -> None:
self._base_sampler.after_trial(study, trial, state, values)