[docs]
class OptunaError(Exception):
"""Base class for Optuna specific errors."""
pass
[docs]
class TrialPruned(OptunaError):
"""Exception for pruned trials.
This error tells a trainer that the current :class:`~optuna.trial.Trial` was pruned. It is
supposed to be raised after :func:`optuna.trial.Trial.should_prune` as shown in the following
example.
See also:
:class:`optuna.TrialPruned` is an alias of :class:`optuna.exceptions.TrialPruned`.
Example:
.. testcode::
import numpy as np
from sklearn.datasets import load_iris
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
import optuna
X, y = load_iris(return_X_y=True)
X_train, X_valid, y_train, y_valid = train_test_split(X, y)
classes = np.unique(y)
def objective(trial):
alpha = trial.suggest_float("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_valid, y_valid)
trial.report(intermediate_value, step)
if trial.should_prune():
raise optuna.TrialPruned()
return clf.score(X_valid, y_valid)
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=20)
"""
pass
[docs]
class CLIUsageError(OptunaError):
"""Exception for CLI.
CLI raises this exception when it receives invalid configuration.
"""
pass
[docs]
class StorageInternalError(OptunaError):
"""Exception for storage operation.
This error is raised when an operation failed in backend DB of storage.
"""
pass
[docs]
class DuplicatedStudyError(OptunaError):
"""Exception for a duplicated study name.
This error is raised when a specified study name already exists in the storage.
"""
pass
class ExperimentalWarning(Warning):
"""Experimental Warning class.
This implementation exists here because the policy of `FutureWarning` has been changed
since Python 3.7 was released. See the details in
https://docs.python.org/3/library/warnings.html#warning-categories.
"""
pass