How to use the lightfm.data.Dataset function in lightfm

def test_fitting():

    users, items = 10, 100

    dataset = Dataset()
    dataset.fit(range(users), range(items))

    assert dataset.interactions_shape() == (users, items)
    assert dataset.user_features_shape() == (users, users)
    assert dataset.item_features_shape() == (items, items)

    assert dataset.build_interactions([])[0].shape == (users, items)
    assert dataset.build_user_features([]).getnnz() == users
    assert dataset.build_item_features([]).getnnz() == items

def test_exceptions():

    users, items = 10, 100

    dataset = Dataset()
    dataset.fit(range(users), range(items))

    with pytest.raises(ValueError):
        dataset.build_interactions([(users + 1, 0)])

    with pytest.raises(ValueError):
        dataset.build_interactions([(0, items + 1)])

    dataset.fit_partial([users + 1], [items + 1])
    dataset.build_interactions([(users + 1, 0)])
    dataset.build_interactions([(0, items + 1)])

def test_build_features():

    users, items = 10, 100

    dataset = Dataset(user_identity_features=False, item_identity_features=False)
    dataset.fit(
        range(users),
        range(items),
        ["user:{}".format(x) for x in range(users)],
        ["item:{}".format(x) for x in range(items)],
    )

    # Build from lists
    user_features = dataset.build_user_features(
        [
            (user_id, ["user:{}".format(x) for x in range(users)])
            for user_id in range(users)
        ]
    )
    assert user_features.getnnz() == users ** 2

def test_fitting_no_identity():

    users, items = 10, 100

    dataset = Dataset(user_identity_features=False, item_identity_features=False)
    dataset.fit(range(users), range(items))

    assert dataset.interactions_shape() == (users, items)
    assert dataset.user_features_shape() == (users, 0)
    assert dataset.item_features_shape() == (items, 0)

    assert dataset.build_interactions([])[0].shape == (users, items)
    assert dataset.build_user_features([], normalize=False).getnnz() == 0
    assert dataset.build_item_features([], normalize=False).getnnz() == 0