optuna.pruners.PercentilePruner

class optuna.pruners.PercentilePruner(percentile: float, n_startup_trials: int = 5, n_warmup_steps: int = 0, interval_steps: int = 1)[source]

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.

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_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():
            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)
Parameters
  • percentile – 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 – Pruning is disabled until the given number of trials finish in the same study.

  • n_warmup_steps – Pruning is disabled until the trial exceeds the given number of step. Note that this feature assumes that step starts at zero.

  • interval_steps – 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.

__init__(percentile: float, n_startup_trials: int = 5, n_warmup_steps: int = 0, interval_steps: int = 1)None[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(percentile[, n_startup_trials, …])

Initialize self.

prune(study, trial)

Judge whether the trial should be pruned based on the reported values.

prune(study: optuna.study.Study, trial: optuna.trial._frozen.FrozenTrial)bool[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() and optuna.trial.Trial.should_prune() provide user interfaces to implement pruning mechanism in an objective function.

Parameters
  • study – Study object of the target study.

  • trial – FrozenTrial object of the target trial. Take a copy before modifying this object.

Returns

A boolean value representing whether the trial should be pruned.