Source code for optuna.samplers._random

from typing import Any
from typing import Dict
from typing import Optional

import numpy

from optuna import distributions
from optuna._transform import _SearchSpaceTransform
from optuna.distributions import BaseDistribution
from optuna.samplers import BaseSampler
from optuna.study import Study
from optuna.trial import FrozenTrial


[docs]class RandomSampler(BaseSampler): """Sampler using random sampling. This sampler is based on *independent sampling*. See also :class:`~optuna.samplers.BaseSampler` for more details of 'independent sampling'. Example: .. testcode:: import optuna from optuna.samplers import RandomSampler def objective(trial): x = trial.suggest_float("x", -5, 5) return x**2 study = optuna.create_study(sampler=RandomSampler()) study.optimize(objective, n_trials=10) Args: seed: Seed for random number generator. """ def __init__(self, seed: Optional[int] = None) -> None: self._rng = numpy.random.RandomState(seed)
[docs] def reseed_rng(self) -> None: self._rng.seed()
[docs] def infer_relative_search_space( self, study: Study, trial: FrozenTrial ) -> Dict[str, BaseDistribution]: return {}
[docs] def sample_relative( self, study: Study, trial: FrozenTrial, search_space: Dict[str, BaseDistribution] ) -> Dict[str, Any]: return {}
[docs] def sample_independent( self, study: Study, trial: FrozenTrial, param_name: str, param_distribution: distributions.BaseDistribution, ) -> Any: search_space = {param_name: param_distribution} trans = _SearchSpaceTransform(search_space) trans_params = self._rng.uniform(trans.bounds[:, 0], trans.bounds[:, 1]) return trans.untransform(trans_params)[param_name]