optuna.pruners.MedianPruner

class optuna.pruners.MedianPruner(n_startup_trials=5, n_warmup_steps=0, interval_steps=1)[源代码]

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.

示例

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

  • 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.

返回类型

None

Methods

prune(study, trial)

Judge whether the trial should be pruned based on the reported values.
prune(study, trial)

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.

参数
返回

A boolean value representing whether the trial should be pruned.

返回类型

bool