Exceptions

class optuna.exceptions.OptunaError

Base class for Optuna specific errors.

class optuna.exceptions.TrialPruned

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

Exception for CLI.

CLI raises this exception when it receives invalid configuration.

class optuna.exceptions.StorageInternalError

Exception for storage operation.

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

class optuna.exceptions.DuplicatedStudyError

Exception for a duplicated study name.

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