Source code for optuna.pruners.nop

from optuna.pruners import BasePruner
from optuna import type_checking

if type_checking.TYPE_CHECKING:
    from optuna.study import Study  # NOQA
    from optuna.trial import FrozenTrial  # NOQA


[docs]class NopPruner(BasePruner): """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_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', pruner=optuna.pruners.NopPruner()) study.optimize(objective, n_trials=20) """ def prune(self, study, trial): # type: (Study, FrozenTrial) -> bool return False