
    ,YHh                     .    d dl Z d dlmZ  G d de      Zy)    N)
BasePrunerc                   $    e Zd ZdZdddddefdZy)		NopPrunera  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)
    studyzoptuna.study.Studytrialzoptuna.trial.FrozenTrialreturnc                      y)NF )selfr   r   s      L/var/www/html/planif/env/lib/python3.12/site-packages/optuna/pruners/_nop.pyprunezNopPruner.prune.   s        N)__name__
__module____qualname____doc__boolr   r
   r   r   r   r      s%    &P/ 8R W[ r   r   )optunaoptuna.prunersr   r   r
   r   r   <module>r      s     %*
 *r   