test_voting_classifier.py 文件源码

def test_predict_proba_on_toy_problem():
    """Calculate predicted probabilities on toy dataset."""
    clf1 = LogisticRegression(random_state=123)
    clf2 = RandomForestClassifier(random_state=123)
    clf3 = GaussianNB()
    X = np.array([[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
    y = np.array([1, 1, 2, 2])

    clf1_res = np.array([[0.59790391, 0.40209609],
                         [0.57622162, 0.42377838],
                         [0.50728456, 0.49271544],
                         [0.40241774, 0.59758226]])

    clf2_res = np.array([[0.8, 0.2],
                         [0.8, 0.2],
                         [0.2, 0.8],
                         [0.3, 0.7]])

    clf3_res = np.array([[0.9985082, 0.0014918],
                         [0.99845843, 0.00154157],
                         [0., 1.],
                         [0., 1.]])

    t00 = (2*clf1_res[0][0] + clf2_res[0][0] + clf3_res[0][0]) / 4
    t11 = (2*clf1_res[1][1] + clf2_res[1][1] + clf3_res[1][1]) / 4
    t21 = (2*clf1_res[2][1] + clf2_res[2][1] + clf3_res[2][1]) / 4
    t31 = (2*clf1_res[3][1] + clf2_res[3][1] + clf3_res[3][1]) / 4

    eclf = VotingClassifier(estimators=[
                            ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
                            voting='soft',
                            weights=[2, 1, 1])
    eclf_res = eclf.fit(X, y).predict_proba(X)

    assert_almost_equal(t00, eclf_res[0][0], decimal=1)
    assert_almost_equal(t11, eclf_res[1][1], decimal=1)
    assert_almost_equal(t21, eclf_res[2][1], decimal=1)
    assert_almost_equal(t31, eclf_res[3][1], decimal=1)

    try:
        eclf = VotingClassifier(estimators=[
                                ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
                                voting='hard')
        eclf.fit(X, y).predict_proba(X)

    except AttributeError:
        pass
    else:
        raise AssertionError('AttributeError for voting == "hard"'
                             ' and with predict_proba not raised')