Source code for optuna.samplers._partial_fixed

from typing import Any
from typing import Dict
from typing import Optional
from typing import Sequence
import warnings

from optuna._experimental import experimental_class
from optuna.distributions import BaseDistribution
from optuna.samplers import BaseSampler
from import Study
from optuna.trial import FrozenTrial
from optuna.trial import TrialState

[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)