Source code for optuna.samplers._base

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

import numpy as np

from optuna.distributions import BaseDistribution
from optuna.study import Study
from optuna.trial import FrozenTrial
from optuna.trial import TrialState


[docs]class BaseSampler(abc.ABC): """Base class for samplers. Optuna combines two types of sampling strategies, which are called *relative sampling* and *independent sampling*. *The relative sampling* determines values of multiple parameters simultaneously so that sampling algorithms can use relationship between parameters (e.g., correlation). Target parameters of the relative sampling are described in a relative search space, which is determined by :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`. *The independent sampling* determines a value of a single parameter without considering any relationship between parameters. Target parameters of the independent sampling are the parameters not described in the relative search space. More specifically, parameters are sampled by the following procedure. At the beginning of a trial, :meth:`~optuna.samplers.BaseSampler.infer_relative_search_space` is called to determine the relative search space for the trial. During the execution of the objective function, :meth:`~optuna.samplers.BaseSampler.sample_relative` is called only once when sampling the parameters belonging to the relative search space for the first time. :meth:`~optuna.samplers.BaseSampler.sample_independent` is used to sample parameters that don't belong to the relative search space. The following figure depicts the lifetime of a trial and how the above three methods are called in the trial. .. image:: ../../../../image/sampling-sequence.png | """ def __str__(self) -> str: return self.__class__.__name__
[docs] @abc.abstractmethod def infer_relative_search_space( self, study: Study, trial: FrozenTrial ) -> Dict[str, BaseDistribution]: """Infer the search space that will be used by relative sampling in the target trial. This method is called right before :func:`~optuna.samplers.BaseSampler.sample_relative` method, and the search space returned by this method is passed to it. The parameters not contained in the search space will be sampled by using :func:`~optuna.samplers.BaseSampler.sample_independent` method. Args: study: Target study object. trial: Target trial object. Take a copy before modifying this object. Returns: A dictionary containing the parameter names and parameter's distributions. .. seealso:: Please refer to :func:`~optuna.search_space.intersection_search_space` as an implementation of :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`. """ raise NotImplementedError
[docs] @abc.abstractmethod def sample_relative( self, study: Study, trial: FrozenTrial, search_space: Dict[str, BaseDistribution] ) -> Dict[str, Any]: """Sample parameters in a given search space. This method is called once at the beginning of each trial, i.e., right before the evaluation of the objective function. This method is suitable for sampling algorithms that use relationship between parameters such as Gaussian Process and CMA-ES. .. note:: The failed trials are ignored by any build-in samplers when they sample new parameters. Thus, failed trials are regarded as deleted in the samplers' perspective. Args: study: Target study object. trial: Target trial object. Take a copy before modifying this object. search_space: The search space returned by :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`. Returns: A dictionary containing the parameter names and the values. """ raise NotImplementedError
[docs] @abc.abstractmethod def sample_independent( self, study: Study, trial: FrozenTrial, param_name: str, param_distribution: BaseDistribution, ) -> Any: """Sample a parameter for a given distribution. This method is called only for the parameters not contained in the search space returned by :func:`~optuna.samplers.BaseSampler.sample_relative` method. This method is suitable for sampling algorithms that do not use relationship between parameters such as random sampling and TPE. .. note:: The failed trials are ignored by any build-in samplers when they sample new parameters. Thus, failed trials are regarded as deleted in the samplers' perspective. Args: study: Target study object. trial: Target trial object. Take a copy before modifying this object. param_name: Name of the sampled parameter. param_distribution: Distribution object that specifies a prior and/or scale of the sampling algorithm. Returns: A parameter value. """ raise NotImplementedError
[docs] def after_trial( self, study: Study, trial: FrozenTrial, state: TrialState, values: Optional[Sequence[float]], ) -> None: """Trial post-processing. This method is called after the objective function returns and right before the trial is finished and its state is stored. .. note:: Added in v2.4.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.4.0. Args: study: Target study object. trial: Target trial object. Take a copy before modifying this object. state: Resulting trial state. values: Resulting trial values. Guaranteed to not be :obj:`None` if trial succeeded. """ pass
[docs] def reseed_rng(self) -> None: """Reseed sampler's random number generator. This method is called by the :class:`~optuna.study.Study` instance if trials are executed in parallel with the option ``n_jobs>1``. In that case, the sampler instance will be replicated including the state of the random number generator, and they may suggest the same values. To prevent this issue, this method assigns a different seed to each random number generator. """ pass
def _raise_error_if_multi_objective(self, study: Study) -> None: if study._is_multi_objective(): raise ValueError( "If the study is being used for multi-objective optimization, " f"{self.__class__.__name__} cannot be used." )
_CONSTRAINTS_KEY = "constraints" def _process_constraints_after_trial( constraints_func: Callable[[FrozenTrial], Sequence[float]], study: Study, trial: FrozenTrial, state: TrialState, ) -> None: if state not in [TrialState.COMPLETE, TrialState.PRUNED]: return constraints = None try: con = constraints_func(trial) if np.any(np.isnan(con)): raise ValueError("Constraint values cannot be NaN.") if not isinstance(con, (tuple, list)): warnings.warn( f"Constraints should be a sequence of floats but got {type(con).__name__}." ) constraints = tuple(con) finally: assert constraints is None or isinstance(constraints, tuple) study._storage.set_trial_system_attr( trial._trial_id, _CONSTRAINTS_KEY, constraints, )