optuna.TrialPruned

exception optuna.TrialPruned[源码]

用于被剪枝（pruned）试验的异常。

此错误告知训练器当前的 Trial 已被剪枝。它应该在 optuna.trial.Trial.should_prune() 之后抛出，如下面的示例所示。

另请参阅

optuna.TrialPruned 是 optuna.exceptions.TrialPruned 的别名。

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")
study.optimize(objective, n_trials=20)

add_note(): Exception.add_note(note) – 为异常添加一个附注