Exceptions

class optuna.exceptions.OptunaError[source]

Base class for Optuna specific errors.

class optuna.exceptions.TrialPruned[source]

Exception for pruned trials.

This error tells a trainer that the current Trial was pruned. It is supposed to be raised after optuna.trial.Trial.should_prune() as shown in the following example.

Example

import optuna
from sklearn.linear_model import SGDClassifier

def objective(trial):
    alpha = trial.suggest_uniform('alpha', 0.0, 1.0)
    clf = SGDClassifier(alpha=alpha)
    n_train_iter = 100

    for step in range(n_train_iter):
        clf.partial_fit(X_train, y_train, classes=classes)

        intermediate_value = clf.score(X_test, y_test)
        trial.report(intermediate_value, step)

        if trial.should_prune():
            raise optuna.exceptions.TrialPruned()

    return clf.score(X_test, y_test)

study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=20)
class optuna.exceptions.CLIUsageError[source]

Exception for CLI.

CLI raises this exception when it receives invalid configuration.

class optuna.exceptions.StorageInternalError[source]

Exception for storage operation.

This error is raised when an operation failed in backend DB of storage.

class optuna.exceptions.DuplicatedStudyError[source]

Exception for a duplicated study name.

This error is raised when a specified study name already exists in the storage.