optuna.trial.FrozenTrial¶

class optuna.trial.FrozenTrial(number: int, state: optuna.trial._state.TrialState, value: Optional[float], datetime_start: Optional[datetime.datetime], datetime_complete: Optional[datetime.datetime], params: Dict[str, Any], distributions: Dict[str, optuna.distributions.BaseDistribution], user_attrs: Dict[str, Any], system_attrs: Dict[str, Any], intermediate_values: Dict[int, float], trial_id: int)[source]¶

Status and results of a Trial.

This object has the same methods as Trial, and it suggests best parameter values among performed trials. In contrast to Trial, FrozenTrial does not depend on Study, and it is useful for deploying optimization results.

Example

Re-evaluate an objective function with parameter values optimized study.

import optuna

def objective(trial):
    x = trial.suggest_uniform("x", -1, 1)
    return x ** 2

study = optuna.create_study()
study.optimize(objective, n_trials=3)

assert objective(study.best_trial) == study.best_value

Note

Attributes are set in optuna.Study.optimize(), but several attributes can be updated after the optimization. That means such attributes are overwritten by the re-evaluation if your objective updates attributes of Trial.

Example:

Overwritten attributes.

import copy
import datetime

import optuna


def objective(trial):
    x = trial.suggest_uniform("x", -1, 1)

    # this user attribute always differs
    trial.set_user_attr("evaluation time", datetime.datetime.now())

    return x ** 2


study = optuna.create_study()
study.optimize(objective, n_trials=3)

best_trial = study.best_trial
best_trial_copy = copy.deepcopy(best_trial)

# re-evaluate
objective(best_trial)

# the user attribute is overwritten by re-evaluation
assert best_trial.user_attrs != best_trial_copy.user_attrs

Note

Please refer to Trial for details of methods and properties.

number¶: Unique and consecutive number of Trial for each Study. Note that this field uses zero-based numbering.

state¶: TrialState of the Trial.

value¶: Objective value of the Trial.

datetime_start¶: Datetime where the Trial started.

datetime_complete¶: Datetime where the Trial finished.

params¶: Dictionary that contains suggested parameters.

user_attrs¶: Dictionary that contains the attributes of the Trial set with optuna.trial.Trial.set_user_attr().

intermediate_values¶: Intermediate objective values set with optuna.trial.Trial.report().

Methods

`report`(value, step)	Interface of report function.
`set_system_attr`(key, value)
`set_user_attr`(key, value)
`should_prune`()	Suggest whether the trial should be pruned or not.
`suggest_categorical`(name, choices)
`suggest_discrete_uniform`(name, low, high, q)
`suggest_float`(name, low, high, *[, step, log])
`suggest_int`(name, low, high[, step, log])
`suggest_loguniform`(name, low, high)
`suggest_uniform`(name, low, high)

Attributes

`datetime_start`
`distributions`	Dictionary that contains the distributions of `params`.
`duration`	Return the elapsed time taken to complete the trial.
`last_step`	Return the maximum step of intermediate_values in the trial.
`number`
`params`
`system_attrs`
`user_attrs`

property distributions¶: Dictionary that contains the distributions of params.

property duration¶

Return the elapsed time taken to complete the trial.

Returns: The duration.

property last_step¶

Return the maximum step of intermediate_values in the trial.

Returns: The maximum step of intermediates.

report(value: float, step: int) → None [source]¶

Interface of report function.

Since FrozenTrial is not pruned, this report function does nothing.