test_weight_boosting.py 文件源码

def test_staged_predict():
    # Check staged predictions.
    rng = np.random.RandomState(0)
    iris_weights = rng.randint(10, size=iris.target.shape)
    boston_weights = rng.randint(10, size=boston.target.shape)

    # AdaBoost classification
    for alg in ['SAMME', 'SAMME.R']:
        clf = AdaBoostClassifier(algorithm=alg, n_estimators=10)
        clf.fit(iris.data, iris.target, sample_weight=iris_weights)

        predictions = clf.predict(iris.data)
        staged_predictions = [p for p in clf.staged_predict(iris.data)]
        proba = clf.predict_proba(iris.data)
        staged_probas = [p for p in clf.staged_predict_proba(iris.data)]
        score = clf.score(iris.data, iris.target, sample_weight=iris_weights)
        staged_scores = [
            s for s in clf.staged_score(
                iris.data, iris.target, sample_weight=iris_weights)]

        assert_equal(len(staged_predictions), 10)
        assert_array_almost_equal(predictions, staged_predictions[-1])
        assert_equal(len(staged_probas), 10)
        assert_array_almost_equal(proba, staged_probas[-1])
        assert_equal(len(staged_scores), 10)
        assert_array_almost_equal(score, staged_scores[-1])

    # AdaBoost regression
    clf = AdaBoostRegressor(n_estimators=10, random_state=0)
    clf.fit(boston.data, boston.target, sample_weight=boston_weights)

    predictions = clf.predict(boston.data)
    staged_predictions = [p for p in clf.staged_predict(boston.data)]
    score = clf.score(boston.data, boston.target, sample_weight=boston_weights)
    staged_scores = [
        s for s in clf.staged_score(
            boston.data, boston.target, sample_weight=boston_weights)]

    assert_equal(len(staged_predictions), 10)
    assert_array_almost_equal(predictions, staged_predictions[-1])
    assert_equal(len(staged_scores), 10)
    assert_array_almost_equal(score, staged_scores[-1])