optuna.pruners.MedianPruner

class optuna.pruners.MedianPruner(n_startup_trials: int = 5, n_warmup_steps: int = 0, interval_steps: int = 1)

Pruner using the median stopping rule.

Prune if the trial’s best intermediate result is worse than median of intermediate results of previous trials at the same step.

Example

We minimize an objective function with the median stopping rule.

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.MedianPruner(n_startup_trials=5,
                                                               n_warmup_steps=30,
                                                               interval_steps=10))
study.optimize(objective, n_trials=20)

Parameters

• 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.

__init__(n_startup_trials: int = 5, n_warmup_steps: int = 0, interval_steps: int = 1) → None

Methods

__init__([n_startup_trials, n_warmup_steps, ...])
prune(study, trial)	Judge whether the trial should be pruned based on the reported values.

prune(study: Study, trial: FrozenTrial) → bool

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.