optuna.pruners.NopPruner
- class optuna.pruners.NopPruner[source]
Pruner which never prunes trials.
Example
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)
Methods
prune
(study, trial)Judge whether the trial should be pruned based on the reported values.
- prune(study, trial)[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()
andoptuna.trial.Trial.should_prune()
provide user interfaces to implement pruning mechanism in an objective function.- Parameters:
study (Study) – Study object of the target study.
trial (FrozenTrial) – FrozenTrial object of the target trial. Take a copy before modifying this object.
- Returns:
A boolean value representing whether the trial should be pruned.
- Return type: