How to use the fklearn.validation.evaluators.roc_auc_evaluator function in fklearn

def test_random_search_tuner(tmpdir):
    train_set = pd.DataFrame({
        'id': ["id1", "id2", "id3", "id3"],
        'date': pd.to_datetime(["2016-01-01", "2016-02-01", "2016-03-01", "2016-04-01"]),
        'x': [.2, .9, .3, .3],
        'target': [0, 1, 0, 1]
    })

    eval_fn = roc_auc_evaluator(target_column="target")

    space = {
        'learning_rate': lambda: np.random.choice([1e-3, 1e-2, 1e-1, 1, 10]),
        'num_estimators': lambda: np.random.choice([1, 2, 3])
    }

    @curry
    def param_train_fn(space, train_set):
        return xgb_classification_learner(features=["x"],
                                          target="target",
                                          learning_rate=space["learning_rate"],
                                          num_estimators=space["num_estimators"])(train_set)

    split_fn = out_of_time_and_space_splitter(n_splits=2, in_time_limit="2016-05-01",
                                              space_column="id", time_column="date")

def eval_fn():
    return roc_auc_evaluator

def eval_fn():
    return roc_auc_evaluator

def test_grid_search_tuner(tmpdir):
    train_set = pd.DataFrame({
        'id': ["id1", "id2", "id3", "id3"],
        'date': pd.to_datetime(["2016-01-01", "2016-02-01", "2016-03-01", "2016-04-01"]),
        'x': [.2, .9, .3, .3],
        'target': [0, 1, 0, 1]
    })

    eval_fn = roc_auc_evaluator(target_column="target")

    space = {
        'learning_rate': lambda: [1e-3, 1e-2, 1e-1],
        'num_estimators': lambda: [1, 2],
        'silent': lambda: [True]
    }

    @curry
    def param_train_fn(space, train_set):
        return xgb_classification_learner(features=["x"],
                                          target="target",
                                          learning_rate=space["learning_rate"],
                                          num_estimators=space["num_estimators"])(train_set)

    split_fn = out_of_time_and_space_splitter(n_splits=2, in_time_limit="2016-05-01",
                                              space_column="id", time_column="date")

def test_roc_auc_evaluator():
    predictions = pd.DataFrame(
        {
            'target': [0, 1, 0, 1],
            'prediction': [.2, .9, .3, .3]
        }
    )

    eval_fn = roc_auc_evaluator(prediction_column="prediction",
                                target_column="target",
                                eval_name="eval_name")

    result = eval_fn(predictions)

    assert result["eval_name"] == 0.875