brk-code

optuna の API リファレンス

optuna (optuna/optuna) の公開 API 188 件 —— クラス 66、関数 31、メソッド 91。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

リポジトリ: optuna/optuna

種別件数
クラス66
関数31
メソッド91

API 一覧

classoptuna._callbacks.MaxTrialsCallback
Set a maximum number of trials before ending the study.
funcoptuna._deprecated.deprecated_class(deprecated_version:str, removed_version:str, name:str | None=None, text:str | None=None) -> 'Callable[[CT], CT]'
Decorate class as deprecated.
funcoptuna._deprecated.deprecated_func(deprecated_version:str, removed_version:str, name:str | None=None, text:str | None=None) -> 'Callable[[Callable[FP, FT]], Callable[FP, FT]]'
Decorate function as deprecated.
funcoptuna._deprecated.wrapper(*args:Any, **kwargs:Any) -> 'FT'
Decorates a function as deprecated.
funcoptuna._experimental.experimental_class(version:str, name:str | None=None) -> Callable[[CT], CT]
Decorate class as experimental.
funcoptuna._experimental.experimental_func(version:str, name:str | None=None) -> Callable[[Callable[FP, FT]], Callable[FP, FT]]
Decorate function as experimental.
funcoptuna._hypervolume.wfg.compute_hypervolume(loss_vals:np.ndarray, reference_point:np.ndarray, assume_pareto:bool=False) -> float
Hypervolume calculator for any dimension.
classoptuna.artifacts._backoff.Backoff
An artifact store's middleware for exponential backoff.
classoptuna.artifacts._boto3.Boto3ArtifactStore
An artifact backend for Boto3.
funcoptuna.artifacts._download.download_artifact(*artifact_store:ArtifactStore, *file_path:str, *artifact_id:str) -> None
Download an artifact from the artifact store.
classoptuna.artifacts._filesystem.FileSystemArtifactStore
An artifact store for file systems.
classoptuna.artifacts._gcs.GCSArtifactStore
An artifact backend for Google Cloud Storage (GCS).
classoptuna.artifacts._protocol.ArtifactStore
A protocol defining the interface for an artifact backend.
methodoptuna.artifacts._protocol.ArtifactStore.open_reader(artifact_id:str) -> BinaryIO
Open the artifact identified by the artifact_id.
methodoptuna.artifacts._protocol.ArtifactStore.remove(artifact_id:str) -> None
Remove the artifact identified by the artifact_id.
methodoptuna.artifacts._protocol.ArtifactStore.write(artifact_id:str, content_body:BinaryIO) -> None
Save the content to the backend.
classoptuna.artifacts._upload.ArtifactMeta
Meta information for an artifact.
classoptuna.artifacts.exceptions.ArtifactNotFound
Exception raised when an artifact is not found.
classoptuna.distributions.BaseDistribution
Base class for distributions.
classoptuna.distributions.CategoricalDistribution
A categorical distribution.
classoptuna.distributions.DiscreteUniformDistribution
A discretized uniform distribution in the linear domain.
methodoptuna.distributions.DiscreteUniformDistribution.q() -> float
Discretization step.
classoptuna.distributions.FloatDistribution
A distribution on floats.
classoptuna.distributions.IntDistribution
A distribution on integers.
classoptuna.distributions.IntLogUniformDistribution
A uniform distribution on integers in the log domain.
classoptuna.distributions.IntUniformDistribution
A uniform distribution on integers.
classoptuna.distributions.LogUniformDistribution
A uniform distribution in the log domain.
classoptuna.distributions.UniformDistribution
A uniform distribution in the linear domain.
funcoptuna.distributions.check_distribution_compatibility(dist_old:BaseDistribution, dist_new:BaseDistribution) -> None
A function to check compatibility of two distributions.
funcoptuna.distributions.distribution_to_json(dist:BaseDistribution) -> str
Serialize a distribution to JSON format.
funcoptuna.distributions.json_to_distribution(json_str:str) -> BaseDistribution
Deserialize a distribution in JSON format.
classoptuna.exceptions.CLIUsageError
Exception for CLI.
classoptuna.exceptions.DuplicatedStudyError
Exception for a duplicated study name.
classoptuna.exceptions.ExperimentalWarning
Experimental Warning class.
classoptuna.exceptions.OptunaError
Base class for Optuna specific errors.
classoptuna.exceptions.StorageInternalError
Exception for storage operation.
classoptuna.exceptions.TrialPruned
Exception for pruned trials.
classoptuna.exceptions.UpdateFinishedTrialError
Exception for updating a finished trial.
classoptuna.importance._base.BaseImportanceEvaluator
Abstract parameter importance evaluator.
classoptuna.importance._fanova._evaluator.FanovaImportanceEvaluator
fANOVA importance evaluator.
classoptuna.importance._ped_anova.evaluator.PedAnovaImportanceEvaluator
PED-ANOVA importance evaluator.
funcoptuna.logging.create_default_formatter() -> logging.Formatter
Create a default formatter of log messages.
funcoptuna.logging.disable_default_handler() -> None
Disable the default handler of the Optuna's root logger.
funcoptuna.logging.disable_propagation() -> None
Disable propagation of the library log outputs.
funcoptuna.logging.enable_default_handler() -> None
Enable the default handler of the Optuna's root logger.
funcoptuna.logging.enable_propagation() -> None
Enable propagation of the library log outputs.
funcoptuna.logging.get_logger(name:str) -> logging.Logger
Return a logger with the specified name.
funcoptuna.logging.get_verbosity() -> int
Return the current level for the Optuna's root logger.
funcoptuna.logging.set_verbosity(verbosity:int) -> None
Set the level for the Optuna's root logger.
classoptuna.pruners._base.BasePruner
Base class for pruners.
classoptuna.pruners._hyperband.HyperbandPruner
Pruner using Hyperband.
classoptuna.pruners._median.MedianPruner
Pruner using the median stopping rule.
classoptuna.pruners._nop.NopPruner
Pruner which never prunes trials.
classoptuna.pruners._patient.PatientPruner
Pruner which wraps another pruner with tolerance.
classoptuna.pruners._percentile.PercentilePruner
Pruner to keep the specified percentile of the trials.
classoptuna.pruners._successive_halving.SuccessiveHalvingPruner
Pruner using Asynchronous Successive Halving Algorithm.
classoptuna.pruners._threshold.ThresholdPruner
Pruner to detect outlying metrics of the trials.
classoptuna.samplers._base.BaseSampler
Base class for samplers.
methodoptuna.samplers._base.BaseSampler.after_trial(study:Study, trial:FrozenTrial, state:TrialState, values:Sequence[float] | None) -> None
Trial post-processing.
methodoptuna.samplers._base.BaseSampler.before_trial(study:Study, trial:FrozenTrial) -> None
Trial pre-processing.
methodoptuna.samplers._base.BaseSampler.reseed_rng() -> None
Reseed sampler's random number generator.
methodoptuna.samplers._base.BaseSampler.sample_independent(study:Study, trial:FrozenTrial, param_name:str, param_distribution:BaseDistribution) -> Any
Sample a parameter for a given distribution.
methodoptuna.samplers._base.BaseSampler.sample_relative(study:Study, trial:FrozenTrial, search_space:dict[str, BaseDistribution]) -> dict[str, Any]
Sample parameters in a given search space.
classoptuna.samplers._ga._base.BaseGASampler
Base class for Genetic Algorithm (GA) samplers.
methodoptuna.samplers._ga._base.BaseGASampler.get_parent_population(study:Study, generation:int) -> list[FrozenTrial]
Get the parent population of the given generation.
methodoptuna.samplers._ga._base.BaseGASampler.get_population(study:Study, generation:int) -> list[FrozenTrial]
Get the population of the given generation.
methodoptuna.samplers._ga._base.BaseGASampler.get_trial_generation(study:Study, trial:FrozenTrial) -> int
Get the generation number of the given trial.
classoptuna.samplers._gp.sampler.GPSampler
Sampler using Gaussian process-based Bayesian optimization.
classoptuna.samplers._lazy_random_state.LazyRandomState
Lazy Random State class.
classoptuna.samplers._nsgaiii._sampler.NSGAIIISampler
Multi-objective sampler using the NSGA-III algorithm.
classoptuna.samplers._partial_fixed.PartialFixedSampler
Sampler with partially fixed parameters.
classoptuna.samplers._random.RandomSampler
Sampler using random sampling.
classoptuna.samplers.nsgaii._crossovers._base.BaseCrossover
Base class for crossovers.
classoptuna.samplers.nsgaii._mutations._base.BaseMutation
Base class for mutations.
methodoptuna.samplers.nsgaii._mutations._base.BaseMutation.mutation(param:float, rng:np.random.RandomState, study:Study, search_space_bounds:np.ndarray) -> float
Mutate the given parameter.
classoptuna.samplers.nsgaii._sampler.NSGAIISampler
Multi-objective sampler using the NSGA-II algorithm.
classoptuna.storages._base.BaseStorage
Base class for storages.
methodoptuna.storages._base.BaseStorage.check_trial_is_updatable(trial_id:int, trial_state:TrialState) -> None
Check whether a trial state is updatable.
methodoptuna.storages._base.BaseStorage.create_new_study(directions:Sequence[StudyDirection], study_name:str | None=None) -> int
Create a new study from a name.
methodoptuna.storages._base.BaseStorage.create_new_trial(study_id:int, template_trial:FrozenTrial | None=None) -> int
Create and add a new trial to a study.
methodoptuna.storages._base.BaseStorage.delete_study(study_id:int) -> None
Delete a study.
methodoptuna.storages._base.BaseStorage.get_all_trials(study_id:int, deepcopy:bool=True, states:Container[TrialState] | None=None) -> list[FrozenTrial]
Read all trials in a study.
methodoptuna.storages._base.BaseStorage.get_best_trial(study_id:int) -> FrozenTrial
Return the trial with the best value in a study.
methodoptuna.storages._base.BaseStorage.get_n_trials(study_id:int, state:tuple[TrialState, ...] | TrialState | None=None) -> int
Count the number of trials in a study.
methodoptuna.storages._base.BaseStorage.get_study_directions(study_id:int) -> list[StudyDirection]
Read whether a study maximizes or minimizes an objective.
methodoptuna.storages._base.BaseStorage.get_study_id_from_name(study_name:str) -> int
Read the ID of a study.
methodoptuna.storages._base.BaseStorage.get_study_name_from_id(study_id:int) -> str
Read the study name of a study.
methodoptuna.storages._base.BaseStorage.get_study_system_attrs(study_id:int) -> dict[str, Any]
Read the optuna-internal attributes of a study.
methodoptuna.storages._base.BaseStorage.get_study_user_attrs(study_id:int) -> dict[str, Any]
Read the user-defined attributes of a study.
methodoptuna.storages._base.BaseStorage.get_trial(trial_id:int) -> FrozenTrial
Read a trial.
methodoptuna.storages._base.BaseStorage.get_trial_id_from_study_id_trial_number(study_id:int, trial_number:int) -> int
Read the trial ID of a trial.
methodoptuna.storages._base.BaseStorage.get_trial_number_from_id(trial_id:int) -> int
Read the trial number of a trial.
methodoptuna.storages._base.BaseStorage.get_trial_param(trial_id:int, param_name:str) -> float
Read the parameter of a trial.
methodoptuna.storages._base.BaseStorage.get_trial_params(trial_id:int) -> dict[str, Any]
Read the parameter dictionary of a trial.
methodoptuna.storages._base.BaseStorage.get_trial_system_attrs(trial_id:int) -> dict[str, Any]
Read the optuna-internal attributes of a trial.
methodoptuna.storages._base.BaseStorage.get_trial_user_attrs(trial_id:int) -> dict[str, Any]
Read the user-defined attributes of a trial.
methodoptuna.storages._base.BaseStorage.remove_session() -> None
Clean up all connections to a database.
methodoptuna.storages._base.BaseStorage.set_study_system_attr(study_id:int, key:str, value:JSONSerializable) -> None
Register an optuna-internal attribute to a study.
methodoptuna.storages._base.BaseStorage.set_study_user_attr(study_id:int, key:str, value:Any) -> None
Register a user-defined attribute to a study.
methodoptuna.storages._base.BaseStorage.set_trial_intermediate_value(trial_id:int, step:int, intermediate_value:float) -> None
Report an intermediate value of an objective function.
methodoptuna.storages._base.BaseStorage.set_trial_param(trial_id:int, param_name:str, param_value_internal:float, distribution:BaseDistribution) -> None
Set a parameter to a trial.
methodoptuna.storages._base.BaseStorage.set_trial_state_values(trial_id:int, state:TrialState, values:Sequence[float] | None=None) -> bool
Update the state and values of a trial.
methodoptuna.storages._base.BaseStorage.set_trial_system_attr(trial_id:int, key:str, value:JSONSerializable) -> None
Set an optuna-internal attribute to a trial.
methodoptuna.storages._base.BaseStorage.set_trial_user_attr(trial_id:int, key:str, value:Any) -> None
Set a user-defined attribute to a trial.
classoptuna.storages._heartbeat.BaseHeartbeat
Base class for heartbeat.
methodoptuna.storages._heartbeat.BaseHeartbeat.get_heartbeat_interval() -> int | None
Get the heartbeat interval if it is set.
methodoptuna.storages._heartbeat.BaseHeartbeat.record_heartbeat(trial_id:int) -> None
Record the heartbeat of the trial.
funcoptuna.storages._heartbeat.fail_stale_trials(study:'optuna.Study') -> None
Fail stale trials and run their failure callbacks.
funcoptuna.storages._heartbeat.is_heartbeat_enabled(storage:BaseStorage) -> bool
Check whether the storage enables the heartbeat.
funcoptuna.storages._rdb.alembic.env.run_migrations_offline()
Run migrations in 'offline' mode.
funcoptuna.storages._rdb.alembic.env.run_migrations_online()
Run migrations in 'online' mode.
classoptuna.storages._rdb.storage.RDBStorage
Storage class for RDB backend.
methodoptuna.storages._rdb.storage.RDBStorage.get_all_versions() -> list[str]
Return the schema version list.
methodoptuna.storages._rdb.storage.RDBStorage.get_head_version() -> str
Return the latest schema version.
methodoptuna.storages._rdb.storage.RDBStorage.remove_session() -> None
Removes the current session.
methodoptuna.storages._rdb.storage.RDBStorage.upgrade() -> None
Upgrade the storage schema.
funcoptuna.storages.get_storage(storage:None | str | BaseStorage) -> BaseStorage
Only for internal usage.
classoptuna.storages.journal._base.BaseJournalBackend
Base class for Journal storages.
methodoptuna.storages.journal._base.BaseJournalBackend.append_logs(logs:list[dict[str, Any]]) -> None
Append logs to the backend.
classoptuna.storages.journal._base.BaseJournalLogStorage
Base class for Journal storages.
classoptuna.storages.journal._base.BaseJournalSnapshot
Optional base class for Journal storages.
methodoptuna.storages.journal._base.BaseJournalSnapshot.load_snapshot() -> bytes | None
Load snapshot from the backend.
methodoptuna.storages.journal._base.BaseJournalSnapshot.save_snapshot(snapshot:bytes) -> None
Save snapshot to the backend.
classoptuna.storages.journal._file.JournalFileBackend
File storage class for Journal log backend.
methodoptuna.storages.journal._file.JournalFileOpenLock.release() -> None
Release a lock by removing the created file.
methodoptuna.storages.journal._file.JournalFileSymlinkLock.release() -> None
Release a lock by removing the symbolic link.
classoptuna.storages.journal._redis.JournalRedisBackend
Redis storage class for Journal log backend.
classoptuna.storages.journal._storage.JournalStorage
Storage class for Journal storage backend.
classoptuna.study._frozen.FrozenStudy
Basic attributes of a :class:`~optuna.study.Study`.
classoptuna.study._study_direction.StudyDirection
Direction of a :class:`~optuna.study.Study`.
methodoptuna.study.study.Study.add_trial(trial:FrozenTrial) -> None
Add trial to study.
methodoptuna.study.study.Study.add_trials(trials:Iterable[FrozenTrial]) -> None
Add trials to study.
methodoptuna.study.study.Study.best_params() -> dict[str, Any]
Return parameters of the best trial in the study.
methodoptuna.study.study.Study.best_trial() -> FrozenTrial
Return the best trial in the study.
methodoptuna.study.study.Study.best_trials() -> list[FrozenTrial]
Return trials located at the Pareto front in the study.
methodoptuna.study.study.Study.best_value() -> float
Return the best objective value in the study.
methodoptuna.study.study.Study.direction() -> StudyDirection
Return the direction of the study.
methodoptuna.study.study.Study.directions() -> list[StudyDirection]
Return the directions of the study.
methodoptuna.study.study.Study.enqueue_trial(params:dict[str, Any], user_attrs:dict[str, Any] | None=None, skip_if_exists:bool=False) -> None
Enqueue a trial with given parameter values.
methodoptuna.study.study.Study.get_trials(deepcopy:bool=True, states:Container[TrialState] | None=None) -> list[FrozenTrial]
Return all trials in the study.
methodoptuna.study.study.Study.metric_names() -> list[str] | None
Return metric names.
methodoptuna.study.study.Study.set_metric_names(metric_names:list[str]) -> None
Set metric names.
methodoptuna.study.study.Study.set_system_attr(key:str, value:Any) -> None
Set a system attribute to the study.
methodoptuna.study.study.Study.set_user_attr(key:str, value:Any) -> None
Set a user attribute to the study.
methodoptuna.study.study.Study.system_attrs() -> dict[str, Any]
Return system attributes.
methodoptuna.study.study.Study.trials() -> list[FrozenTrial]
Return all trials in the study.
methodoptuna.study.study.Study.user_attrs() -> dict[str, Any]
Return user attributes.
funcoptuna.study.study.copy_study(*from_study_name:str, *from_storage:str | storages.BaseStorage, *to_storage:str | storages.BaseStorage, *to_study_name:str | None=None) -> None
Copy study from one storage to another.
funcoptuna.study.study.delete_study(*study_name:str, *storage:str | storages.BaseStorage) -> None
Delete a :class:`~optuna.study.Study` object.
funcoptuna.study.study.get_all_study_names(storage:str | storages.BaseStorage) -> list[str]
Get all study names stored in a specified storage.
classoptuna.terminator.erroreval.BaseErrorEvaluator
Base class for error evaluators.
classoptuna.terminator.erroreval.StaticErrorEvaluator
An error evaluator that always returns a constant value.
funcoptuna.terminator.erroreval.report_cross_validation_scores(trial:Trial, scores:list[float]) -> None
A function to report cross-validation scores of a trial.
classoptuna.terminator.improvement.evaluator.BaseImprovementEvaluator
Base class for improvement evaluators.
classoptuna.terminator.terminator.BaseTerminator
Base class for terminators.
classoptuna.terminator.terminator.Terminator
Automatic stopping mechanism for Optuna studies.
classoptuna.trial._base.BaseTrial
Base class for trials.
classoptuna.trial._fixed.FixedTrial
A trial class which suggests a fixed value for each parameter.
methodoptuna.trial._fixed.FixedTrial.constraints() -> dict[str, float]
Returns constraint values.
methodoptuna.trial._fixed.FixedTrial.set_constraint(key:str, value:float) -> None
Set a constraint value for the trial.
classoptuna.trial._frozen.FrozenTrial
Status and results of a :class:`~optuna.trial.Trial`.
methodoptuna.trial._frozen.FrozenTrial.constraints() -> dict[str, float]
Returns constraint values.
methodoptuna.trial._frozen.FrozenTrial.duration() -> datetime.timedelta | None
Return the elapsed time taken to complete the trial.
methodoptuna.trial._frozen.FrozenTrial.report(value:float, step:int) -> None
Interface of report function.
methodoptuna.trial._frozen.FrozenTrial.set_constraint(key:str, value:float) -> None
Set a constraint value for the trial.
methodoptuna.trial._frozen.FrozenTrial.should_prune() -> bool
Suggest whether the trial should be pruned or not.
classoptuna.trial._state.TrialState
State of a :class:`~optuna.trial.Trial`.
classoptuna.trial._trial.Trial
A trial is a process of evaluating an objective function.
methodoptuna.trial._trial.Trial.constraints() -> dict[str, float]
Returns constraint values.
methodoptuna.trial._trial.Trial.datetime_start() -> datetime.datetime | None
Return start datetime.
methodoptuna.trial._trial.Trial.distributions() -> dict[str, BaseDistribution]
Return distributions of parameters to be optimized.
methodoptuna.trial._trial.Trial.params() -> dict[str, Any]
Return parameters to be optimized.
methodoptuna.trial._trial.Trial.report(value:float, step:int) -> None
Report an objective function value for a given step.
methodoptuna.trial._trial.Trial.set_constraint(key:str, value:float) -> None
Set a constraint value for the trial.
methodoptuna.trial._trial.Trial.set_system_attr(key:str, value:Any) -> None
Set system attributes to the trial.
methodoptuna.trial._trial.Trial.set_user_attr(key:str, value:Any) -> None
Set user attributes to the trial.
methodoptuna.trial._trial.Trial.should_prune() -> bool
Suggest whether the trial should be pruned or not.
methodoptuna.trial._trial.Trial.suggest_discrete_uniform(name:str, low:float, high:float, q:float) -> float
Suggest a value for the discrete parameter.
methodoptuna.trial._trial.Trial.suggest_float(name:str, low:float, high:float, *step:float | None=None, *log:bool=False) -> float
Suggest a value for the floating point parameter.
methodoptuna.trial._trial.Trial.suggest_int(name:str, low:int, high:int, *step:int=1, *log:bool=False) -> int
Suggest a value for the integer parameter.
methodoptuna.trial._trial.Trial.suggest_loguniform(name:str, low:float, high:float) -> float
Suggest a value for the continuous parameter.
methodoptuna.trial._trial.Trial.suggest_uniform(name:str, low:float, high:float) -> float
Suggest a value for the continuous parameter.
methodoptuna.trial._trial.Trial.system_attrs() -> dict[str, Any]
Return system attributes.
methodoptuna.trial._trial.Trial.user_attrs() -> dict[str, Any]
Return user attributes.
funcoptuna.visualization._hypervolume_history.plot_hypervolume_history(study:Study, reference_point:Sequence[float]) -> 'go.Figure'
Plot hypervolume history of all trials in a study.
funcoptuna.visualization._intermediate_values.plot_intermediate_values(study:Study) -> 'go.Figure'
Plot intermediate values of all trials in a study.
funcoptuna.visualization._timeline.plot_timeline(study:Study, n_recent_trials:int | None=None) -> 'go.Figure'
Plot the timeline of a study.
funcoptuna.visualization.matplotlib._timeline.plot_timeline(study:Study, n_recent_trials:int | None=None) -> 'Axes'
Plot the timeline of a study.

この情報について

掲載しているシグネチャは optuna/optuna の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・ 型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。 詳しくは仕組みの解説をご覧ください。

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