mlp_aux = MLP_AUX(dataset, args.negative_sampling_size,
eval(args.layers), args.epochs, args.batch_size,
args.validation_split, args.user_sampling_size,
args.core_number, args.sim_threshold)
model = mlp_aux.train_model()
hits, ndcgs = evaluate_model(model, dataset.test_data,
dataset.test_negatives, 10, 1, True)
print("Hitrate: {}".format(sum(hits) / len(hits)))
print("NDCG: {}".format(sum(ndcgs) / len(ndcgs)))
elif args.network_type == 'parallel':
dataset = Dataset(dataset_name=args.dataset)
parallel = Parallel(dataset, args.negative_sampling_size,
eval(args.layers), args.epochs, args.batch_size,
args.validation_split)
model = parallel.train_model()
hits, ndcgs = evaluate_model(model, dataset.test_data,
dataset.test_negatives, 10, 1)
print("Hitrate: {}".format(sum(hits) / len(hits)))
print("NDCG: {}".format(sum(ndcgs) / len(ndcgs)))
elif args.network_type == 'parallel-aux':
dataset = Dataset(dataset_name=args.dataset)
parallel_aux = Parallel_AUX(dataset, args.negative_sampling_size,
eval(args.layers), args.epochs,
args.batch_size, args.validation_split,
args.user_sampling_size, args.core_number,
args.sim_threshold)
model = parallel_aux.train_model()
hits, ndcgs = evaluate_model(model, dataset.test_data,
dataset.test_negatives, 10, 1, True)
print("Hitrate: {}".format(sum(hits) / len(hits)))
