Source code for optuna.pruners._nop

import optuna
from optuna.pruners import BasePruner


[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) """
[docs] def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: return False