test_classification.py 文件源码

def test_precision_refcall_f1_score_multilabel_unordered_labels():
    # test that labels need not be sorted in the multilabel case
    y_true = np.array([[1, 1, 0, 0]])
    y_pred = np.array([[0, 0, 1, 1]])
    for average in ['samples', 'micro', 'macro', 'weighted', None]:
        p, r, f, s = precision_recall_fscore_support(
            y_true, y_pred, labels=[3, 0, 1, 2], warn_for=[], average=average)
        assert_array_equal(p, 0)
        assert_array_equal(r, 0)
        assert_array_equal(f, 0)
        if average is None:
            assert_array_equal(s, [0, 1, 1, 0])