How to use the pomegranate.BayesianNetwork.from_structure function in pomegranate

structure (iterable(iterable)): structure as returned from
                    define_bayes_net_structure
            fields (list(unicode)): field names to learn
            prior_data (list(data)): optional list of training samples to use
                    as a prior for each network.

        Return:
            BayesianNetworkModel: A predictive model training on the given data

        """
        type_to_network = {}
        for type_, data in input_data.type_to_data.items():
            if prior_data is not None:
                # Make defensive copy
                data = list(data) + list(prior_data)
            bayesian_network = BayesianNetwork.from_structure(data, structure)
            type_to_network[type_] = bayesian_network
        return BayesianNetworkModel(type_to_network, fields, segmenter=input_data.segmenter)

def default_bayesian_likelihood(dataset, trainset, testset, meta):
    struct = glob.glob("data/*/{}_structure.json".format(dataset))
    assert len(struct) == 1
    bn1 = BayesianNetwork.from_json(struct[0])

    trainset_mapped = mapper(trainset, meta)
    testset_mapped = mapper(testset, meta)
    prob = []
    for item in trainset_mapped:
        try:
            prob.append(bn1.probability(item))
        except:
            prob.append(1e-8)
    l1 = np.mean(np.log(np.asarray(prob) + 1e-8))

    bn2 = BayesianNetwork.from_structure(trainset_mapped, bn1.structure)
    prob = []
    for item in testset_mapped:
        try:
            prob.append(bn2.probability(item))
        except:
            prob.append(1e-8)
    l2 = np.mean(np.log(np.asarray(prob) + 1e-8))

    return [{
        "name": "default",
        "syn_likelihood": l1,
        "test_likelihood": l2,
    }]

LOGGER.info('Evaluating using Bayesian Likelihood.')
    structure_json = json.dumps(metadata['structure'])
    bn1 = BayesianNetwork.from_json(structure_json)

    train_mapped = _mapper(train, metadata)
    test_mapped = _mapper(test, metadata)
    prob = []
    for item in train_mapped:
        try:
            prob.append(bn1.probability(item))
        except Exception:
            prob.append(1e-8)

    l1 = np.mean(np.log(np.asarray(prob) + 1e-8))

    bn2 = BayesianNetwork.from_structure(train_mapped, bn1.structure)
    prob = []

    for item in test_mapped:
        try:
            prob.append(bn2.probability(item))
        except Exception:
            prob.append(1e-8)

    l2 = np.mean(np.log(np.asarray(prob) + 1e-8))

    return pd.DataFrame([{
        "name": "Bayesian Likelihood",
        "syn_likelihood": l1,
        "test_likelihood": l2,
    }])