def test_dynamic_updates(self):
        """
        TensorCoFi dynamic update
        We will take a tensor cofi. Train the model, evaluate it. Then we remove all the user factors
        and recompute them using the online_user_factors to check if the performance is almost the same...
        """
        pyTF = PyTensorCoFi(n_factors=20, n_iterations=5, c_lambda=0.05, c_alpha=40)

        evaluator = Evaluator()
        tf = TensorCoFi(n_factors=2, n_iterations=100, c_lambda=0.05, c_alpha=40)
        df = pd.read_csv(resource_filename(testfm.__name__, "data/movielenshead.dat"), sep="::", header=None,
                         names=["user", "item", "rating", "date", "title"])
        training, testing = testfm.split.holdoutByRandom(df, 0.7)
        users = {user: list(entries) for user, entries in training.groupby("user")["item"]}

        tf.fit(training)
        map1 = evaluator.evaluate_model(tf, testing)  # map of the original model

        #now we try to replace the original factors with on the fly computed factors
        #lets iterate over the training data of items and the users
        for u, items in users.items():
            #user id in the tf
            uid = tf.data_map[tf.get_user_column()][u]  # user id
            iids = [tf.data_map[tf.get_item_column()][i] for i in items]  # item ids that user has seen
            #original_factors = tf.factors["user"][uid]
            new_factors = pyTF.online_user_factors(tf.factors[1], iids, p_param=40, lambda_param=0.05)
            #replace original factors with the new factors
            tf.factors[0][uid, :] = new_factors
            #tf.update_user_factors(uid, new_factors)


        #lets evaluate the new model with real-time updated factors
        map2 = evaluator.evaluate_model(tf, testing)
        #The difference should be smaller than 20%
        assert abs(map2[0]-map1[0]) < 0.2*map1[0]
dataset/ETHPy150Open grafos-ml/test.fm/src/tests/test_models.py/PyTensorTest.test_dynamic_updates/swapped operands of `-`