# 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)
agree.cuda()
# 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()
# 开始训练时间
t1 = time()
training(agree, dataset.get_user_dataloader(config.batch_size), epoch,
config, 'user')
training(agree, dataset.get_group_dataloader(config.batch_size), epoch,
config, 'group')
print("user and group training time is: [%.1f s]" % (time() - t1))
# 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)
agree.cuda("cuda:0")
# 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()
# 开始训练时间
t1 = time()
training(agree, dataset.get_user_dataloader(config.batch_size), epoch, config, 'user')
training(agree, dataset.get_group_dataloader(config.batch_size), epoch, config, 'group')
print("user and group training time is: [%.1f s]" % (time()-t1))
# evaluation
t2 = time()
u_hr, u_ndcg = evaluation(agree, helper, dataset.user_testRatings, dataset.user_testNegatives, config.topK, 'user')