def test_add(self):
        prio_set_list = event._PrioritizedSetList()
        objs = [(i,) for i in range(5)]

        prio_set_list.add(0, objs[0])
        assert prio_set_list.list == [(0, {objs[0]})]

        prio_set_list.add(0, objs[1])
        assert prio_set_list.list == [(0, {objs[0], objs[1]})]

        prio_set_list.add(10, objs[2])
        assert prio_set_list.list == [(10, {objs[2]}),
                                      (0,  {objs[0], objs[1]})]

        prio_set_list.add(-10, objs[3])
        assert prio_set_list.list == [( 10, {objs[2]}),           # noqa: E201
                                      (  0, {objs[0], objs[1]}),  # noqa: E201
                                      (-10, {objs[3]})]
        prio_set_list.add(-1, objs[4])
        assert prio_set_list.list == [( 10, {objs[2]}),           # noqa: E201
                                      (  0, {objs[0], objs[1]}),  # noqa: E201
                                      ( -1, {objs[4]}),           # noqa: E201
                                      (-10, {objs[3]})]
def balanced_accuracy_scoring(clf, X, y):
    """Scoring function that computes the balanced accuracy to be used
    internally in the cross-validation procedure.
    """
    y_pred = clf.predict(X)
    conf_mat = confusion_matrix(y, y_pred)
    bal_acc = 0.
    for i in range(len(conf_mat)):
        bal_acc += (float(conf_mat[i, i])) / np.sum(conf_mat[i])

    bal_acc /= len(conf_mat)
    return bal_acc