args.lr = eval(args.lr)
args.path = './data/' + args.dataset + '/'
args.user_dataset = args.path + 'userRating'
args.group_dataset = args.path + 'groupRating'
args.user_in_group_path = "./data/" + args.dataset + "/groupMember.txt"
print('args', args)
# initial helper
helper = Helper()
# get the dict of users in group
g_m_d = helper.gen_group_member_dict(args.user_in_group_path)
# initial dataSet class
dataset = GDataset(args.user_dataset, args.group_dataset,
args.num_negatives, g_m_d)
# get group number
num_group = max(g_m_d.keys()) + 1
num_users, num_items = dataset.num_users, dataset.num_items
# print('num_users',num_users)
# build AGREE model
agree = AGREE(num_users, num_items, num_group, args.embedding_size, g_m_d,
args.drop_ratio).to(device)
# config information
print("AGREE at embedding size %d, run Iteration:%d" %
(args.embedding_size, args.epoch))
history = []
tops = [5, 10, 15, 20]
for epoch in range(args.epoch):
# training
hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
return hr, ndcg
if __name__ == '__main__':
# initial parameter class
config = Config()
# initial helper
helper = Helper()
# get the dict of users in group
g_m_d = helper.gen_group_member_dict(config.user_in_group_path)
# initial dataSet class
dataset = GDataset(config.user_dataset, config.group_dataset,
config.num_negatives)
# get group number~
num_group = len(g_m_d)
num_users, num_items = dataset.num_users, dataset.num_items
# build AGREE model
agree = AGREE(num_users, num_items, num_group, config.embedding_size,
g_m_d, config.drop_ratio)
# config information
print("AGREE at embedding size %d, run Iteration:%d, NDCG and HR at %d" %
(config.embedding_size, config.epoch, config.topK))
# train the model
for epoch in range(config.epoch):
agree.train()
model.eval()
(hits, ndcgs) = helper.evaluate_model(model, testRatings, testNegatives, K, type_m)
hr, ndcg = np.array(hits).mean(), np.array(ndcgs).mean()
return hr, ndcg
if __name__ == '__main__':
# initial parameter class
config = Config()
# initial helper
helper = Helper()
# initial dataSet class
dataset = GDataset(config.user_dataset, config.group_dataset,config.num_negatives)
# get group number
num_users, num_items, num_groups = dataset.num_users, dataset.num_items, dataset.num_groups
# build NCF model
ncf = NCF(num_users, num_items, num_groups, config.factor_num, config.num_layers , config.drop_ratio)
# config information
print("NCF at embedding size %d, run Iteration:%d, NDCG and HR at %d" %(config.factor_num, config.epoch, config.topK))
# train the model
for epoch in range(config.epoch):
ncf.train()
# 开始训练时间
t1 = time()
training(ncf, dataset.get_user_dataloader(config.batch_size), epoch, config, 'user')