L iZZddlmZddlmZddlmZer ddlmZddlm Z GddeZ y) ) annotations) TYPE_CHECKING) BasePruner)Study) FrozenTrialceZdZdZddZy) NopPruneraPruner 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) cy)NF)selfstudytrials Y/mnt/ssd/data/python-lab/Trading/venv/lib/python3.12/site-packages/optuna/pruners/_nop.pyprunezNopPruner.prune6sN)r rrrreturnbool)__name__ __module__ __qualname____doc__rr rrr r s &Prr N) __future__rtypingroptuna.prunersr optuna.studyr optuna.trialrr r rrrs%" %"(* *r