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

def test_evaluate_performance_so_side_corruptions_without_filter():
    X = load_wn18()
    model = ComplEx(batches_count=10, seed=0, epochs=5, k=200, eta=10, loss='nll',
                    regularizer=None, optimizer='adam', optimizer_params={'lr': 0.01}, verbose=True)
    model.fit(X['train'])

    X_filter = np.concatenate((X['train'], X['valid'], X['test']))
    ranks = evaluate_performance(X['test'][::20], model, X_filter,  verbose=True,
                                 use_default_protocol=False, corrupt_side='s+o')
    mrr = mrr_score(ranks)
    hits_10 = hits_at_n_score(ranks, n=10)
    print("ranks: %s" % ranks)
    print("MRR: %f" % mrr)
    print("Hits@10: %f" % hits_10)
    assert(mrr is not np.Inf)

print('MAR:', mr_sep)
    print('Mrr:', mrr_score(ranks_sep))
    print('hits10:', hits_at_n_score(ranks_sep, 10))
    print('hits3:', hits_at_n_score(ranks_sep, 3))
    print('hits1:', hits_at_n_score(ranks_sep, 1))

    from ampligraph.evaluation import evaluate_performance

    from ampligraph.evaluation import hits_at_n_score, mrr_score, mr_score
    ranks = evaluate_performance(wn18['test'][::100], model, verbose=True, corrupt_side='s+o',
                                 use_default_protocol=True)
    print('----------corrupted with default protocol-----------------')
    mr_joint = mr_score(ranks)
    mrr_joint = mrr_score(ranks)
    print('MAR:', mr_joint)
    print('Mrr:', mrr_score(ranks))
    print('hits10:', hits_at_n_score(ranks, 10))
    print('hits3:', hits_at_n_score(ranks, 3))
    print('hits1:', hits_at_n_score(ranks, 1))
    
    np.testing.assert_equal(mr_sep, mr_joint)
    assert(mrr_joint is not np.Inf)

from ampligraph.evaluation import hits_at_n_score, mrr_score, mr_score
    ranks = evaluate_performance(wn18['test'][::100], model, X_filter, verbose=True, corrupt_side='o',
                                 use_default_protocol=False)

    ranks_sep.extend(ranks)
    from ampligraph.evaluation import evaluate_performance

    from ampligraph.evaluation import hits_at_n_score, mrr_score, mr_score
    ranks = evaluate_performance(wn18['test'][::100], model, X_filter, verbose=True, corrupt_side='s',
                                 use_default_protocol=False)
    ranks_sep.extend(ranks)
    print('----------EVAL WITH FILTER-----------------')
    print('----------Subj and obj corrupted separately-----------------')
    mr_sep = mr_score(ranks_sep)
    print('MAR:', mr_sep)
    print('Mrr:', mrr_score(ranks_sep))
    print('hits10:', hits_at_n_score(ranks_sep, 10))
    print('hits3:', hits_at_n_score(ranks_sep, 3))
    print('hits1:', hits_at_n_score(ranks_sep, 1))

    from ampligraph.evaluation import evaluate_performance

    from ampligraph.evaluation import hits_at_n_score, mrr_score, mr_score
    ranks = evaluate_performance(wn18['test'][::100], model, X_filter, verbose=True, corrupt_side='s+o',
                                 use_default_protocol=True)
    print('----------corrupted with default protocol-----------------')
    mr_joint = mr_score(ranks)
    mrr_joint = mrr_score(ranks)
    print('MAR:', mr_joint)
    print('Mrr:', mrr_joint)
    print('hits10:', hits_at_n_score(ranks, 10))
    print('hits3:', hits_at_n_score(ranks, 3))

'x_valid':X['test'][:1000], 
        'criteria':'mrr', 'x_filter':filter,
        'stop_interval': 2, 
        'burn_in':0, 
        'check_interval':100
    })
    # model.fit(np.concatenate((X['train'], X['valid'])))

    # Run the evaluation procedure on the test set. Will create filtered rankings.
    # To disable filtering: filter_triples=None
    ranks = evaluate_performance(X['test'], model=model, filter_triples=filter,
                                verbose=True)

    # compute and print metrics:
    mr = mar_score(ranks)
    mrr = mrr_score(ranks)
    hits_1 = hits_at_n_score(ranks, n=1)
    hits_3 = hits_at_n_score(ranks, n=3)
    hits_10 = hits_at_n_score(ranks, n=10)

    with open("result_{0}_{1}.txt".format(args.dataset, args.model), "w") as fo:
        fo.write("mr(test): {0} mrr(test): {1} hits 1: {2} hits 3: {3} hits 10: {4}".format(mr, mrr, hits_1, hits_3, hits_10))

if not hasattr(model, 'early_stopping_epoch') or model.early_stopping_epoch is None:
        early_stopping_epoch = np.nan
    else:
        early_stopping_epoch = model.early_stopping_epoch

    # Run the evaluation procedure on the test set. Will create filtered rankings.
    # To disable filtering: filter_triples=None
    ranks = evaluate_performance(X['test'],
                                 model,
                                 filter,
                                 verbose=False)

    # compute and print metrics:
    mr = mr_score(ranks)
    mrr = mrr_score(ranks)
    hits_1 = hits_at_n_score(ranks, n=1)
    hits_3 = hits_at_n_score(ranks, n=3)
    hits_10 = hits_at_n_score(ranks, n=10)

    return {
        "mr": mr,
        "mrr": mrr,
        "H@1": hits_1,
        "H@3": hits_3,
        "H@10": hits_10,
        "hyperparams": hyperparams,
        "time": time.time() - start_time,
        "early_stopping_epoch": early_stopping_epoch
    }

# compute and store test_loss
            ranks = []
            
            # Get each triple and compute the rank for that triple
            for x_test_triple in range(self.eval_dataset_handle.get_size("valid")):
                rank_triple = self.sess_train.run(self.rank)
                ranks.append(rank_triple)
                
            if self.early_stopping_criteria == 'hits10':
                current_test_value = hits_at_n_score(ranks, 10)
            elif self.early_stopping_criteria == 'hits3':
                current_test_value = hits_at_n_score(ranks, 3)
            elif self.early_stopping_criteria == 'hits1':
                current_test_value = hits_at_n_score(ranks, 1)
            elif self.early_stopping_criteria == 'mrr':
                current_test_value = mrr_score(ranks)

            if self.early_stopping_best_value is None:  # First validation iteration
                self.early_stopping_best_value = current_test_value
                self.early_stopping_first_value = current_test_value
            elif self.early_stopping_best_value >= current_test_value:
                self.early_stopping_stop_counter += 1
                if self.early_stopping_stop_counter == self.early_stopping_params.get(
                        'stop_interval', DEFAULT_STOP_INTERVAL_EARLY_STOPPING):

                    # If the best value for the criteria has not changed from
                    #  initial value then
                    # save the model before early stopping
                    if self.early_stopping_best_value == self.early_stopping_first_value:
                        self._save_trained_params()

                    if self.verbose:

def evaluation(ranks):
        mrr = mrr_score(ranks)
        mr = mr_score(ranks)
        hits_1 = hits_at_n_score(ranks, n=1)
        hits_3 = hits_at_n_score(ranks, n=3)
        hits_10 = hits_at_n_score(ranks, n=10)
        return mrr, mr, hits_1, hits_3, hits_10