
    -jiZ                    d    S SK Jr  S SKJr  S SKJr  \(       a  S SKJr  S SKJ	r	   " S S\5      r
g)	    )annotations)TYPE_CHECKING)
BasePruner)Study)FrozenTrialc                  "    \ rS rSrSrSS jrSrg)	NopPruner   a  Pruner which never prunes trials.

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():
                    assert False, "should_prune() should always return False with this pruner."
                    raise optuna.TrialPruned()

            return clf.score(X_valid, y_valid)


        study = optuna.create_study(direction="maximize", pruner=optuna.pruners.NopPruner())
        study.optimize(objective, n_trials=20)
c                    g)NF )selfstudytrials      M/home/james-whalen/.local/lib/python3.13/site-packages/optuna/pruners/_nop.pypruneNopPruner.prune6   s        r   N)r   r   r   r   returnbool)__name__
__module____qualname____firstlineno____doc__r   __static_attributes__r   r   r   r	   r	      s    &Pr   r	   N)
__future__r   typingr   optuna.prunersr   optuna.studyr   optuna.trialr   r	   r   r   r   <module>r!      s%    "   % "(*
 *r   