How to use the ampligraph.evaluation.create_mappings function in ampligraph

def test_to_idx():
    X = np.array([['a', 'x', 'b'], ['c', 'y', 'd']])
    X_idx_expected = [[0, 0, 1], [2, 1, 3]]
    rel_to_idx, ent_to_idx = create_mappings(X)
    X_idx = to_idx(X, ent_to_idx=ent_to_idx, rel_to_idx=rel_to_idx)

    np.testing.assert_array_equal(X_idx, X_idx_expected)

def test_generate_corruptions_for_eval():
    X = np.array([['a', 'x', 'b'],
                  ['c', 'x', 'd'],
                  ['e', 'x', 'f'],
                  ['b', 'y', 'h'],
                  ['a', 'y', 'l']])

    rel_to_idx, ent_to_idx = create_mappings(X)
    X = to_idx(X, ent_to_idx=ent_to_idx, rel_to_idx=rel_to_idx)

    with tf.Session() as sess:
        all_ent = tf.constant(list(ent_to_idx.values()), dtype=tf.int64)
        x = tf.constant(np.array([X[0]]), dtype=tf.int64)
        x_n_actual = sess.run(generate_corruptions_for_eval(x, all_ent))
        x_n_expected = np.array([[0, 0, 0],
                                 [0, 0, 1],
                                 [0, 0, 2],
                                 [0, 0, 3],
                                 [0, 0, 4],
                                 [0, 0, 5],
                                 [0, 0, 6],
                                 [0, 0, 7],
                                 [0, 0, 1],
                                 [1, 0, 1],

Returns
        -------
        rel_to_idx : dictionary
            Relation to idx mapping dictionary
        ent_to_idx : dictionary
            entity to idx mapping dictionary
        """
        from ..evaluation import create_mappings
        if use_all:
            complete_dataset = []
            for key in self.dataset.keys():
                complete_dataset.append(self.dataset[key])
            self.rel_to_idx, self.ent_to_idx = create_mappings(np.concatenate(complete_dataset, axis=0))

        else:
            self.rel_to_idx, self.ent_to_idx = create_mappings(self.dataset["train"])
            
        return self.rel_to_idx, self.ent_to_idx

Relation to idx mapping dictionary
        ent_to_idx : dictionary
            entity to idx mapping dictionary
        """
        if (len(self.rel_to_idx) == 0 or len(self.ent_to_idx) == 0 or (regenerate is True)) \
                and (not self.using_existing_db):
            from ..evaluation import create_mappings
            self._create_schema()
            if use_all:
                complete_dataset = []
                for key in self.dataset.keys():
                    complete_dataset.append(self.dataset[key])
                self.rel_to_idx, self.ent_to_idx = create_mappings(np.concatenate(complete_dataset, axis=0))

            else:
                self.rel_to_idx, self.ent_to_idx = create_mappings(self.dataset["train"])

            self._insert_entities_in_db()
        return self.rel_to_idx, self.ent_to_idx

use_all : boolean
            If True, it generates mapping from all the data. If False, it only uses training set to generate mappings
            
        Returns
        -------
        rel_to_idx : dictionary
            Relation to idx mapping dictionary
        ent_to_idx : dictionary
            entity to idx mapping dictionary
        """
        from ..evaluation import create_mappings
        if use_all:
            complete_dataset = []
            for key in self.dataset.keys():
                complete_dataset.append(self.dataset[key])
            self.rel_to_idx, self.ent_to_idx = create_mappings(np.concatenate(complete_dataset, axis=0))

        else:
            self.rel_to_idx, self.ent_to_idx = create_mappings(self.dataset["train"])
            
        return self.rel_to_idx, self.ent_to_idx

Returns
        -------
        rel_to_idx : dictionary
            Relation to idx mapping dictionary
        ent_to_idx : dictionary
            entity to idx mapping dictionary
        """
        if (len(self.rel_to_idx) == 0 or len(self.ent_to_idx) == 0 or (regenerate is True)) \
                and (not self.using_existing_db):
            from ..evaluation import create_mappings
            self._create_schema()
            if use_all:
                complete_dataset = []
                for key in self.dataset.keys():
                    complete_dataset.append(self.dataset[key])
                self.rel_to_idx, self.ent_to_idx = create_mappings(np.concatenate(complete_dataset, axis=0))

            else:
                self.rel_to_idx, self.ent_to_idx = create_mappings(self.dataset["train"])

            self._insert_entities_in_db()
        return self.rel_to_idx, self.ent_to_idx