optuna.pruners.PercentilePruner
- class optuna.pruners.PercentilePruner(percentile, n_startup_trials=5, n_warmup_steps=0, interval_steps=1)[源代码]
Pruner to keep the specified percentile of the trials.
Prune if the best intermediate value is in the bottom percentile among trials at the same step.
示例
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(): raise optuna.TrialPruned() return clf.score(X_valid, y_valid) study = optuna.create_study( direction="maximize", pruner=optuna.pruners.PercentilePruner( 25.0, n_startup_trials=5, n_warmup_steps=30, interval_steps=10 ), ) study.optimize(objective, n_trials=20)
- 参数
percentile (float) – Percentile which must be between 0 and 100 inclusive (e.g., When given 25.0, top of 25th percentile trials are kept).
n_startup_trials (int) – Pruning is disabled until the given number of trials finish in the same study.
n_warmup_steps (int) – Pruning is disabled until the trial exceeds the given number of step. Note that this feature assumes that
step
starts at zero.interval_steps (int) – Interval in number of steps between the pruning checks, offset by the warmup steps. If no value has been reported at the time of a pruning check, that particular check will be postponed until a value is reported. Value must be at least 1.
- 返回类型
None
Methods
prune
(study, trial)Judge whether the trial should be pruned based on the reported values.
- prune(study, trial)[源代码]
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.- 参数
study (optuna.study.Study) – Study object of the target study.
trial (optuna.trial._frozen.FrozenTrial) – FrozenTrial object of the target trial. Take a copy before modifying this object.
- 返回
A boolean value representing whether the trial should be pruned.
- 返回类型