import datetime
from typing import Any
from typing import Dict
from typing import Optional
from typing import overload
from typing import Sequence
import warnings
from optuna import distributions
from optuna._convert_positional_args import convert_positional_args
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 _SUGGEST_INT_POSITIONAL_ARGS
from optuna.trial._base import BaseTrial
_suggest_deprecated_msg = "Use suggest_float{args} 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))
@convert_positional_args(previous_positional_arg_names=_SUGGEST_INT_POSITIONAL_ARGS)
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)))
@overload
def suggest_categorical(self, name: str, choices: Sequence[None]) -> None: ...
@overload
def suggest_categorical(self, name: str, choices: Sequence[bool]) -> bool: ...
@overload
def suggest_categorical(self, name: str, choices: Sequence[int]) -> int: ...
@overload
def suggest_categorical(self, name: str, choices: Sequence[float]) -> float: ...
@overload
def suggest_categorical(self, name: str, choices: Sequence[str]) -> str: ...
@overload
def suggest_categorical(
self, name: str, choices: Sequence[CategoricalChoiceType]
) -> CategoricalChoiceType: ...
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
[docs]
@deprecated_func("3.1.0", "5.0.0")
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