optuna.study.Study

class optuna.study.Study(study_name: str, storage: Union[str, storages.BaseStorage], sampler: samplers.BaseSampler = None, pruner: pruners.BasePruner = None)[source]

A study corresponds to an optimization task, i.e., a set of trials.

This object provides interfaces to run a new Trial, access trials’ history, set/get user-defined attributes of the study itself.

Note that the direct use of this constructor is not recommended. To create and load a study, please refer to the documentation of create_study() and load_study() respectively.

__init__(study_name: str, storage: Union[str, storages.BaseStorage], sampler: samplers.BaseSampler = None, pruner: pruners.BasePruner = None) → None[source]

Methods

__init__(study_name, storage[, sampler, pruner])

add_trial(trial)

Add trial to study.

enqueue_trial(params)

Enqueue a trial with given parameter values.

get_trials([deepcopy])

Return all trials in the study.

optimize(func[, n_trials, timeout, n_jobs, ...])

Optimize an objective function.

set_system_attr(key, value)

Set a system attribute to the study.

set_user_attr(key, value)

Set a user attribute to the study.

stop()

Exit from the current optimization loop after the running trials finish.

trials_dataframe([attrs, multi_index])

Export trials as a pandas DataFrame.

Attributes

best_params

Return parameters of the best trial in the study.

best_trial

Return the best trial in the study.

best_value

Return the best objective value in the study.

direction

Return the direction of the study.

system_attrs

Return system attributes.

trials

Return all trials in the study.

user_attrs

Return user attributes.
add_trial(trial: optuna.trial._frozen.FrozenTrial) → None[source]

Add trial to study.

The trial is validated before being added.

Example

import optuna
from optuna.distributions import UniformDistribution

def objective(trial):
    x = trial.suggest_uniform('x', 0, 10)
    return x ** 2

study = optuna.create_study()
assert len(study.trials) == 0

trial = optuna.trial.create_trial(
    params={"x": 2.0},
    distributions={"x": UniformDistribution(0, 10)},
    value=4.0,
)

study.add_trial(trial)
assert len(study.trials) == 1

study.optimize(objective, n_trials=3)
assert len(study.trials) == 4

other_study = optuna.create_study()

for trial in study.trials:
    other_study.add_trial(trial)
assert len(other_study.trials) == len(study.trials)

other_study.optimize(objective, n_trials=2)
assert len(other_study.trials) == len(study.trials) + 2

See also

This method should in general be used to add already evaluated trials (trial.state.is_finished() == True). To queue trials for evaluation, please refer to enqueue_trial().

See also

See create_trial() for how to create trials.

Parameters

trial – Trial to add.

Raises

ValueError – If trial is an invalid state.

Note

Added in v2.0.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.0.0.

property best_params

Return parameters of the best trial in the study.

Returns

A dictionary containing parameters of the best trial.

property best_trial

Return the best trial in the study.

Returns

A FrozenTrial object of the best trial.

property best_value

Return the best objective value in the study.

Returns

A float representing the best objective value.

property direction

Return the direction of the study.

Returns

A StudyDirection object.

enqueue_trial(params: Dict[str, Any]) → None[source]

Enqueue a trial with given parameter values.

You can fix the next sampling parameters which will be evaluated in your objective function.

Example

import optuna

def objective(trial):
    x = trial.suggest_uniform('x', 0, 10)
    return x ** 2

study = optuna.create_study()
study.enqueue_trial({'x': 5})
study.enqueue_trial({'x': 0})
study.optimize(objective, n_trials=2)

assert study.trials[0].params == {'x': 5}
assert study.trials[1].params == {'x': 0}
Parameters

params – Parameter values to pass your objective function.

Note

Added in v1.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v1.2.0.

get_trials(deepcopy: bool = True) → List[FrozenTrial]

Return all trials in the study.

The returned trials are ordered by trial number.

For library users, it’s recommended to use more handy trials property to get the trials instead.

Parameters

deepcopy – Flag to control whether to apply copy.deepcopy() to the trials. Note that if you set the flag to False, you shouldn’t mutate any fields of the returned trial. Otherwise the internal state of the study may corrupt and unexpected behavior may happen.

Returns

A list of FrozenTrial objects.

optimize(func: ObjectiveFuncType, n_trials: Optional[int] = None, timeout: Optional[float] = None, n_jobs: int = 1, catch: Tuple[Type[Exception], ...] = (), callbacks: Optional[List[Callable[[Study, FrozenTrial], None]]] = None, gc_after_trial: bool = False, show_progress_bar: bool = False) → None[source]

Optimize an objective function.

Optimization is done by choosing a suitable set of hyperparameter values from a given range. Uses a sampler which implements the task of value suggestion based on a specified distribution. The sampler is specified in create_study() and the default choice for the sampler is TPE. See also TPESampler for more details on ‘TPE’.

Parameters

  • func – A callable that implements objective function.

  • n_trials – The number of trials. If this argument is set to None, there is no limitation on the number of trials. If timeout is also set to None, the study continues to create trials until it receives a termination signal such as Ctrl+C or SIGTERM.

  • timeout – Stop study after the given number of second(s). If this argument is set to None, the study is executed without time limitation. If n_trials is also set to None, the study continues to create trials until it receives a termination signal such as Ctrl+C or SIGTERM.

  • n_jobs – The number of parallel jobs. If this argument is set to -1, the number is set to CPU count.

  • catch – A study continues to run even when a trial raises one of the exceptions specified in this argument. Default is an empty tuple, i.e. the study will stop for any exception except for TrialPruned.

  • callbacks – List of callback functions that are invoked at the end of each trial. Each function must accept two parameters with the following types in this order: Study and FrozenTrial.

  • gc_after_trial –

    Flag to determine whether to automatically run garbage collection after each trial. Set to True to run the garbage collection, False otherwise. When it runs, it runs a full collection by internally calling gc.collect(). If you see an increase in memory consumption over several trials, try setting this flag to True.

    See also

    How do I avoid running out of memory (OOM) when optimizing studies?

  • show_progress_bar – Flag to show progress bars or not. To disable progress bar, set this False. Currently, progress bar is experimental feature and disabled when n_jobs \(\ne 1\).

set_system_attr(key: str, value: Any) → None[source]

Set a system attribute to the study.

Note that Optuna internally uses this method to save system messages. Please use set_user_attr() to set users’ attributes.

Parameters

  • key – A key string of the attribute.

  • value – A value of the attribute. The value should be JSON serializable.

set_user_attr(key: str, value: Any) → None[source]

Set a user attribute to the study.

Parameters

  • key – A key string of the attribute.

  • value – A value of the attribute. The value should be JSON serializable.

stop() → None[source]

Exit from the current optimization loop after the running trials finish.

This method lets the running optimize() method return immediately after all trials which the optimize() method spawned finishes. This method does not affect any behaviors of parallel or successive study processes.

Raises

RuntimeError – If this method is called outside an objective function or callback.

property system_attrs

Return system attributes.

Returns

A dictionary containing all system attributes.

property trials

Return all trials in the study.

The returned trials are ordered by trial number.

This is a short form of self.get_trials(deepcopy=True).

Returns

A list of FrozenTrial objects.

trials_dataframe(attrs: Tuple[str, ...] = ('number', 'value', 'datetime_start', 'datetime_complete', 'duration', 'params', 'user_attrs', 'system_attrs', 'state'), multi_index: bool = False) → pd.DataFrame[source]

Export trials as a pandas DataFrame.

The DataFrame provides various features to analyze studies. It is also useful to draw a histogram of objective values and to export trials as a CSV file. If there are no trials, an empty DataFrame is returned.

Example

import optuna
import pandas

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

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

# Create a dataframe from the study.
df = study.trials_dataframe()
assert isinstance(df, pandas.DataFrame)
assert df.shape[0] == 3  # n_trials.
Parameters

  • attrs – Specifies field names of FrozenTrial to include them to a DataFrame of trials.

  • multi_index – Specifies whether the returned DataFrame employs MultiIndex or not. Columns that are hierarchical by nature such as (params, x) will be flattened to params_x when set to False.

Returns

A pandas DataFrame of trials in the Study.

property user_attrs

Return user attributes.

Returns

A dictionary containing all user attributes.