import datetime
from typing import Any
from typing import Dict
from typing import Optional
from optuna._experimental import experimental
from optuna.distributions import BaseDistribution
from optuna import logging
from optuna.trial._state import TrialState
_logger = logging.get_logger(__name__)
[docs]class FrozenTrial(object):
"""Status and results of a :class:`~optuna.trial.Trial`.
Attributes:
number:
Unique and consecutive number of :class:`~optuna.trial.Trial` for each
:class:`~optuna.study.Study`. Note that this field uses zero-based numbering.
state:
:class:`TrialState` of the :class:`~optuna.trial.Trial`.
value:
Objective value of the :class:`~optuna.trial.Trial`.
datetime_start:
Datetime where the :class:`~optuna.trial.Trial` started.
datetime_complete:
Datetime where the :class:`~optuna.trial.Trial` finished.
params:
Dictionary that contains suggested parameters.
user_attrs:
Dictionary that contains the attributes of the :class:`~optuna.trial.Trial` set with
:func:`optuna.trial.Trial.set_user_attr`.
intermediate_values:
Intermediate objective values set with :func:`optuna.trial.Trial.report`.
"""
[docs] def __init__(
self,
number, # type: int
state, # type: TrialState
value, # type: Optional[float]
datetime_start, # type: Optional[datetime.datetime]
datetime_complete, # type: Optional[datetime.datetime]
params, # type: Dict[str, Any]
distributions, # type: Dict[str, BaseDistribution]
user_attrs, # type: Dict[str, Any]
system_attrs, # type: Dict[str, Any]
intermediate_values, # type: Dict[int, float]
trial_id, # type: int
):
# type: (...) -> None
self.number = number
self.state = state
self.value = value
self.datetime_start = datetime_start
self.datetime_complete = datetime_complete
self.params = params
self.user_attrs = user_attrs
self.system_attrs = system_attrs
self.intermediate_values = intermediate_values
self._distributions = distributions
self._trial_id = trial_id
# Ordered list of fields required for `__repr__`, `__hash__` and dataframe creation.
# TODO(hvy): Remove this list in Python 3.6 as the order of `self.__dict__` is preserved.
_ordered_fields = [
"number",
"value",
"datetime_start",
"datetime_complete",
"params",
"_distributions",
"user_attrs",
"system_attrs",
"intermediate_values",
"_trial_id",
"state",
]
def __eq__(self, other):
# type: (Any) -> bool
if not isinstance(other, FrozenTrial):
return NotImplemented
return other.__dict__ == self.__dict__
def __lt__(self, other):
# type: (Any) -> bool
if not isinstance(other, FrozenTrial):
return NotImplemented
return self.number < other.number
def __le__(self, other):
# type: (Any) -> bool
if not isinstance(other, FrozenTrial):
return NotImplemented
return self.number <= other.number
def __hash__(self):
# type: () -> int
return hash(tuple(getattr(self, field) for field in self._ordered_fields))
def __repr__(self):
# type: () -> str
return "{cls}({kwargs})".format(
cls=self.__class__.__name__,
kwargs=", ".join(
"{field}={value}".format(
field=field if not field.startswith("_") else field[1:],
value=repr(getattr(self, field)),
)
for field in self._ordered_fields
),
)
def _validate(self):
# type: () -> None
if self.datetime_start is None:
raise ValueError("`datetime_start` is supposed to be set.")
if self.state.is_finished():
if self.datetime_complete is None:
raise ValueError("`datetime_complete` is supposed to be set for a finished trial.")
else:
if self.datetime_complete is not None:
raise ValueError(
"`datetime_complete` is supposed to be None for an unfinished trial."
)
if self.state == TrialState.COMPLETE and self.value is None:
raise ValueError("`value` is supposed to be set for a complete trial.")
if set(self.params.keys()) != set(self.distributions.keys()):
raise ValueError(
"Inconsistent parameters {} and distributions {}.".format(
set(self.params.keys()), set(self.distributions.keys())
)
)
for param_name, param_value in self.params.items():
distribution = self.distributions[param_name]
param_value_in_internal_repr = distribution.to_internal_repr(param_value)
if not distribution._contains(param_value_in_internal_repr):
raise ValueError(
"The value {} of parameter '{}' isn't contained in the distribution "
"{}.".format(param_value, param_name, distribution)
)
@property
def distributions(self):
# type: () -> Dict[str, BaseDistribution]
"""Dictionary that contains the distributions of :attr:`params`."""
return self._distributions
@distributions.setter
def distributions(self, value):
# type: (Dict[str, BaseDistribution]) -> None
self._distributions = value
@property
def last_step(self):
# type: () -> Optional[int]
if len(self.intermediate_values) == 0:
return None
else:
return max(self.intermediate_values.keys())
@property
def duration(self):
# type: () -> Optional[datetime.timedelta]
"""Return the elapsed time taken to complete the trial.
Returns:
The duration.
"""
if self.datetime_start and self.datetime_complete:
return self.datetime_complete - self.datetime_start
else:
return None
[docs]@experimental("2.0.0")
def create_trial(
*,
state: Optional[TrialState] = None,
value: Optional[float] = None,
params: Optional[Dict[str, Any]] = None,
distributions: Optional[Dict[str, BaseDistribution]] = None,
user_attrs: Optional[Dict[str, Any]] = None,
system_attrs: Optional[Dict[str, Any]] = None,
intermediate_values: Optional[Dict[int, float]] = None
) -> FrozenTrial:
"""Create a new :class:`~optuna.trial.FrozenTrial`.
Example:
.. testcode::
import optuna
from optuna.distributions import CategoricalDistribution
from optuna.distributions import UniformDistribution
trial = optuna.trial.create_trial(
params={"x": 1.0, "y": 0},
distributions={
"x": UniformDistribution(0, 10),
"y": CategoricalDistribution([-1, 0, 1]),
},
value=5.0,
)
assert isinstance(trial, optuna.trial.FrozenTrial)
assert trial.value == 5.0
assert trial.params == {"x": 1.0, "y": 0}
.. seealso::
See :func:`~optuna.study.Study.add_trial` for how this function can be used to create a
study from existing trials.
.. note::
Please note that this is a low-level API. In general, trials that are passed to objective
functions are created inside :func:`~optuna.study.Study.optimize`.
Args:
state:
Trial state.
value:
Trial objective value. Must be specified if ``state`` is :class:`TrialState.COMPLETE`.
params:
Dictionary with suggested parameters of the trial.
distributions:
Dictionary with parameter distributions of the trial.
user_attrs:
Dictionary with user attributes.
system_attrs:
Dictionary with system attributes. Should not have to be used for most users.
intermediate_values:
Dictionary with intermediate objective values of the trial.
Returns:
Created trial.
"""
params = params or {}
distributions = distributions or {}
user_attrs = user_attrs or {}
system_attrs = system_attrs or {}
intermediate_values = intermediate_values or {}
state = state or TrialState.COMPLETE
datetime_start = datetime.datetime.now()
if state.is_finished():
datetime_complete = datetime_start # type: Optional[datetime.datetime]
else:
datetime_complete = None
trial = FrozenTrial(
number=-1,
trial_id=-1,
state=state,
value=value,
datetime_start=datetime_start,
datetime_complete=datetime_complete,
params=params,
distributions=distributions,
user_attrs=user_attrs,
system_attrs=system_attrs,
intermediate_values=intermediate_values,
)
trial._validate()
return trial