from typing import Any
from typing import Dict
from typing import Optional
import numpy
from optuna import distributions
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_uniform("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 = numpy.random.RandomState()
[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:
if isinstance(param_distribution, distributions.UniformDistribution):
return self._rng.uniform(param_distribution.low, param_distribution.high)
elif isinstance(param_distribution, distributions.LogUniformDistribution):
log_low = numpy.log(param_distribution.low)
log_high = numpy.log(param_distribution.high)
return float(numpy.exp(self._rng.uniform(log_low, log_high)))
elif isinstance(param_distribution, distributions.DiscreteUniformDistribution):
q = param_distribution.q
r = param_distribution.high - param_distribution.low
# [low, high] is shifted to [0, r] to align sampled values at regular intervals.
low = 0 - 0.5 * q
high = r + 0.5 * q
s = self._rng.uniform(low, high)
v = numpy.round(s / q) * q + param_distribution.low
# v may slightly exceed range due to round-off errors.
return float(min(max(v, param_distribution.low), param_distribution.high))
elif isinstance(param_distribution, distributions.IntUniformDistribution):
# [low, high] is shifted to [0, r] to align sampled values at regular intervals.
r = (param_distribution.high - param_distribution.low) / param_distribution.step
# numpy.random.randint includes low but excludes high.
s = self._rng.randint(0, r + 1)
v = s * param_distribution.step + param_distribution.low
return int(v)
elif isinstance(param_distribution, distributions.IntLogUniformDistribution):
log_low = numpy.log(param_distribution.low - 0.5)
log_high = numpy.log(param_distribution.high + 0.5)
s = numpy.exp(self._rng.uniform(log_low, log_high))
v = numpy.round(s)
return int(min(max(v, param_distribution.low), param_distribution.high))
elif isinstance(param_distribution, distributions.CategoricalDistribution):
choices = param_distribution.choices
index = self._rng.randint(0, len(choices))
return choices[index]
else:
raise NotImplementedError