test_cross_validation.py 文件源码

def test_cross_val_score_multilabel():
    X = np.array([[-3, 4], [2, 4], [3, 3], [0, 2], [-3, 1],
                  [-2, 1], [0, 0], [-2, -1], [-1, -2], [1, -2]])
    y = np.array([[1, 1], [0, 1], [0, 1], [0, 1], [1, 1],
                  [0, 1], [1, 0], [1, 1], [1, 0], [0, 0]])
    clf = KNeighborsClassifier(n_neighbors=1)
    scoring_micro = make_scorer(precision_score, average='micro')
    scoring_macro = make_scorer(precision_score, average='macro')
    scoring_samples = make_scorer(precision_score, average='samples')
    score_micro = cval.cross_val_score(clf, X, y, scoring=scoring_micro, cv=5)
    score_macro = cval.cross_val_score(clf, X, y, scoring=scoring_macro, cv=5)
    score_samples = cval.cross_val_score(clf, X, y,
                                         scoring=scoring_samples, cv=5)
    assert_almost_equal(score_micro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 3])
    assert_almost_equal(score_macro, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])
    assert_almost_equal(score_samples, [1, 1 / 2, 3 / 4, 1 / 2, 1 / 4])