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

{
        '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))

# The entire dataset will be used to filter out false positives statements
    # created by the corruption procedure:
    filter = np.concatenate((X['train'], X['valid'], X['test']))
    
    print("Start fitting...no early stopping")

    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))

{
        '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))

with open(args.hyperparams, "r") as fi:
        param_grid = json.load(fi)
        print("input param: ", param_grid)

    # Train the model on all possibile combinations of hyperparameters.
    # Models are validated on the validation set.
    # It returnes a model re-trained on training and validation sets.
    print("start executing to find the best...")
    best_model, best_params, best_mrr_train, \
    ranks_test, mrr_test = select_best_model_ranking(model_class, X_dict,
                                                      param_grid,
                                                      filter_retrain=True,
                                                      eval_splits=100,
                                                      verbose=True)

    mr_test = mar_score(ranks_test)
    hits_1 = hits_at_n_score(ranks_test, n=1)
    hits_3 = hits_at_n_score(ranks_test, n=3)
    hits_10 = hits_at_n_score(ranks_test, n=10)

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