Source code for optuna.trial._fixed

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

from optuna import distributions
from optuna._deprecated import deprecated_func
from optuna.distributions import BaseDistribution
from optuna.distributions import CategoricalChoiceType
from optuna.distributions import CategoricalDistribution
from optuna.distributions import FloatDistribution
from optuna.distributions import IntDistribution
from optuna.trial._base import BaseTrial


_suggest_deprecated_msg = "Use :func:`~optuna.trial.FixedTrial.suggest_float` instead."


[docs]class FixedTrial(BaseTrial): """A trial class which suggests a fixed value for each parameter. This object has the same methods as :class:`~optuna.trial.Trial`, and it suggests pre-defined parameter values. The parameter values can be determined at the construction of the :class:`~optuna.trial.FixedTrial` object. In contrast to :class:`~optuna.trial.Trial`, :class:`~optuna.trial.FixedTrial` does not depend on :class:`~optuna.study.Study`, and it is useful for deploying optimization results. Example: Evaluate an objective function with parameter values given by a user. .. testcode:: import optuna def objective(trial): x = trial.suggest_float("x", -100, 100) y = trial.suggest_categorical("y", [-1, 0, 1]) return x**2 + y assert objective(optuna.trial.FixedTrial({"x": 1, "y": 0})) == 1 .. note:: Please refer to :class:`~optuna.trial.Trial` for details of methods and properties. Args: params: A dictionary containing all parameters. number: A trial number. Defaults to ``0``. """ def __init__(self, params: Dict[str, Any], number: int = 0) -> None: self._params = params self._suggested_params: Dict[str, Any] = {} self._distributions: Dict[str, BaseDistribution] = {} self._user_attrs: Dict[str, Any] = {} self._system_attrs: Dict[str, Any] = {} self._datetime_start = datetime.datetime.now() self._number = number def suggest_float( self, name: str, low: float, high: float, *, step: Optional[float] = None, log: bool = False, ) -> float: return self._suggest(name, FloatDistribution(low, high, log=log, step=step))
[docs] @deprecated_func("3.0.0", "6.0.0", text=_suggest_deprecated_msg) def suggest_uniform(self, name: str, low: float, high: float) -> float: return self.suggest_float(name, low, high)
[docs] @deprecated_func("3.0.0", "6.0.0", text=_suggest_deprecated_msg) def suggest_loguniform(self, name: str, low: float, high: float) -> float: return self.suggest_float(name, low, high, log=True)
[docs] @deprecated_func("3.0.0", "6.0.0", text=_suggest_deprecated_msg) def suggest_discrete_uniform(self, name: str, low: float, high: float, q: float) -> float: return self.suggest_float(name, low, high, step=q)
def suggest_int(self, name: str, low: int, high: int, step: int = 1, log: bool = False) -> int: return int(self._suggest(name, IntDistribution(low, high, log=log, step=step))) def suggest_categorical( self, name: str, choices: Sequence[CategoricalChoiceType] ) -> CategoricalChoiceType: return self._suggest(name, CategoricalDistribution(choices=choices)) def report(self, value: float, step: int) -> None: pass def should_prune(self) -> bool: return False def set_user_attr(self, key: str, value: Any) -> None: self._user_attrs[key] = value def set_system_attr(self, key: str, value: Any) -> None: self._system_attrs[key] = value def _suggest(self, name: str, distribution: BaseDistribution) -> Any: if name not in self._params: raise ValueError( "The value of the parameter '{}' is not found. Please set it at " "the construction of the FixedTrial object.".format(name) ) value = self._params[name] param_value_in_internal_repr = distribution.to_internal_repr(value) if not distribution._contains(param_value_in_internal_repr): warnings.warn( "The value {} of the parameter '{}' is out of " "the range of the distribution {}.".format(value, name, distribution) ) if name in self._distributions: distributions.check_distribution_compatibility(self._distributions[name], distribution) self._suggested_params[name] = value self._distributions[name] = distribution return value @property def params(self) -> Dict[str, Any]: return self._suggested_params @property def distributions(self) -> Dict[str, BaseDistribution]: return self._distributions @property def user_attrs(self) -> Dict[str, Any]: return self._user_attrs @property def system_attrs(self) -> Dict[str, Any]: return self._system_attrs @property def datetime_start(self) -> Optional[datetime.datetime]: return self._datetime_start @property def number(self) -> int: return self._number