-jiO "SS\5r"SS\5r"SS\5r"SS\5r"SS \5r"S S \\5r"S S \5r g)c\rSrSrSrSrg) OptunaErrorz&Base class for Optuna specific errors.N__name__ __module__ __qualname____firstlineno____doc____static_attributes__rK/home/james-whalen/.local/lib/python3.13/site-packages/optuna/exceptions.pyrrs0r rc\rSrSrSrSrg) TrialPrunedaHException 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) rNrrr rrrs ,\ r rc\rSrSrSrSrg) CLIUsageError9zVException for CLI. CLI raises this exception when it receives invalid configuration. rNrrr rrr9   r rc\rSrSrSrSrg)StorageInternalErrorBzjException for storage operation. This error is raised when an operation failed in backend DB of storage. rNrrr rrrBrr rc\rSrSrSrSrg)DuplicatedStudyErrorKzxException for a duplicated study name. This error is raised when a specified study name already exists in the storage. rNrrr rrrKrr rc\rSrSrSrSrg)UpdateFinishedTrialErrorTzkException for updating a finished trial. This error is raised when attempting to update a finished trial. rNrrr rrrTrr rc\rSrSrSrSrg)ExperimentalWarning]zExperimental 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. rNrrr rr r ]s  r r N) Exceptionrrrrr RuntimeErrorrWarningr rr rr%s\ ) / +/ d K  ;  ;  {L  ' r