How to use the lightfm.evaluation.recall_at_k function in lightfm

def test_recall_at_k():

    no_users, no_items = (10, 100)

    train, test = _generate_data(no_users, no_items)

    model = LightFM(loss="bpr")
    model.fit_partial(test)

    for k in (10, 5, 1):

        # Without omitting train interactions
        recall = evaluation.recall_at_k(model, test, k=k)
        expected_mean_recall = _recall_at_k(model, test, k)

        assert np.allclose(recall.mean(), expected_mean_recall)
        assert len(recall) == (test.getnnz(axis=1) > 0).sum()
        assert (
            len(evaluation.recall_at_k(model, train, preserve_rows=True))
            == test.shape[0]
        )

        # With omitting train interactions
        recall = evaluation.recall_at_k(model, test, k=k, train_interactions=train)
        expected_mean_recall = _recall_at_k(model, test, k, train=train)

        assert np.allclose(recall.mean(), expected_mean_recall)

)
    evaluation.recall_at_k(
        model, test, train_interactions=train, check_intersections=True
    )
    evaluation.precision_at_k(
        model, test, train_interactions=train, check_intersections=True
    )
    evaluation.reciprocal_rank(
        model, test, train_interactions=train, check_intersections=True
    )

    # check no error is raised when there are intersections but flag is False
    evaluation.auc_score(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.recall_at_k(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.precision_at_k(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.reciprocal_rank(
        model, train, train_interactions=train, check_intersections=False
    )

with pytest.raises(ValueError):
        evaluation.precision_at_k(
            model, train, train_interactions=train, check_intersections=True
        )

    with pytest.raises(ValueError):
        evaluation.reciprocal_rank(
            model, train, train_interactions=train, check_intersections=True
        )

    # check no errors raised when train and test have no interactions in common
    evaluation.auc_score(
        model, test, train_interactions=train, check_intersections=True
    )
    evaluation.recall_at_k(
        model, test, train_interactions=train, check_intersections=True
    )
    evaluation.precision_at_k(
        model, test, train_interactions=train, check_intersections=True
    )
    evaluation.reciprocal_rank(
        model, test, train_interactions=train, check_intersections=True
    )

    # check no error is raised when there are intersections but flag is False
    evaluation.auc_score(
        model, train, train_interactions=train, check_intersections=False
    )
    evaluation.recall_at_k(
        model, train, train_interactions=train, check_intersections=False
    )

for k in (10, 5, 1):

        # Without omitting train interactions
        recall = evaluation.recall_at_k(model, test, k=k)
        expected_mean_recall = _recall_at_k(model, test, k)

        assert np.allclose(recall.mean(), expected_mean_recall)
        assert len(recall) == (test.getnnz(axis=1) > 0).sum()
        assert (
            len(evaluation.recall_at_k(model, train, preserve_rows=True))
            == test.shape[0]
        )

        # With omitting train interactions
        recall = evaluation.recall_at_k(model, test, k=k, train_interactions=train)
        expected_mean_recall = _recall_at_k(model, test, k, train=train)

        assert np.allclose(recall.mean(), expected_mean_recall)

train, test = _generate_data(no_users, no_items)

    model = LightFM(loss="bpr")
    model.fit_partial(test)

    for k in (10, 5, 1):

        # Without omitting train interactions
        recall = evaluation.recall_at_k(model, test, k=k)
        expected_mean_recall = _recall_at_k(model, test, k)

        assert np.allclose(recall.mean(), expected_mean_recall)
        assert len(recall) == (test.getnnz(axis=1) > 0).sum()
        assert (
            len(evaluation.recall_at_k(model, train, preserve_rows=True))
            == test.shape[0]
        )

        # With omitting train interactions
        recall = evaluation.recall_at_k(model, test, k=k, train_interactions=train)
        expected_mean_recall = _recall_at_k(model, test, k, train=train)

        assert np.allclose(recall.mean(), expected_mean_recall)

def recall_at_k_on_ranks(
        ranks, test_interactions, train_interactions=None, k=10, preserve_rows=False):
    return recall_at_k(
        model=ModelMockRanksCacher(ranks.copy()),
        test_interactions=test_interactions,
        train_interactions=train_interactions,
        k=k,
        preserve_rows=preserve_rows,
    )