class optuna.pruners.NopPruner[source]

Pruner which never prunes trials.


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_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_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',
study.optimize(objective, n_trials=20)

Initialize self. See help(type(self)) for accurate signature.



Initialize self.

prune(study, trial)

Judge whether the trial should be pruned based on the reported values.

prune(study: optuna.study.Study, trial: optuna.trial._frozen.FrozenTrial)bool[source]

Judge whether the trial should be pruned based on the reported values.

Note that this method is not supposed to be called by library users. Instead, optuna.trial.Trial.report() and optuna.trial.Trial.should_prune() provide user interfaces to implement pruning mechanism in an objective function.

  • study – Study object of the target study.

  • trial – FrozenTrial object of the target trial. Take a copy before modifying this object.


A boolean value representing whether the trial should be pruned.