test_bagging.py 文件源码

def test_bootstrap_samples():
    # Test that bootstrapping samples generate non-perfect base estimators.
    rng = check_random_state(0)
    X_train, X_test, y_train, y_test = train_test_split(boston.data,
                                                        boston.target,
                                                        random_state=rng)

    base_estimator = DecisionTreeRegressor().fit(X_train, y_train)

    # without bootstrap, all trees are perfect on the training set
    ensemble = BaggingRegressor(base_estimator=DecisionTreeRegressor(),
                                max_samples=1.0,
                                bootstrap=False,
                                random_state=rng).fit(X_train, y_train)

    assert_equal(base_estimator.score(X_train, y_train),
                 ensemble.score(X_train, y_train))

    # with bootstrap, trees are no longer perfect on the training set
    ensemble = BaggingRegressor(base_estimator=DecisionTreeRegressor(),
                                max_samples=1.0,
                                bootstrap=True,
                                random_state=rng).fit(X_train, y_train)

    assert_greater(base_estimator.score(X_train, y_train),
                   ensemble.score(X_train, y_train))