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')
training(ncf, dataset.get_group_dataloader(config.batch_size), epoch, config, 'group')
print("training time is: [%.1f s]" % (time()-t1))
# evaluation
t2 = time()
u_hr, u_ndcg = evaluation(ncf, helper, dataset.user_testRatings, dataset.user_testNegatives, config.topK, 'user')
print('User Iteration %d [%.1f s]: HR = %.4f, NDCG = %.4f, [%.1f s]' % (
epoch, time() - t1, u_hr, u_ndcg, time() - t2))
g_hr, g_ndcg = evaluation(ncf, helper, dataset.group_testRatings, dataset.group_testNegatives, config.topK, 'group')
print('Group Iteration %d [%.1f s]: HR = %.4f, NDCG = %.4f, [%.1f s]' % (
epoch, time() - t1, g_hr, g_ndcg, time() - t2))
print("Done!")
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()
# 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')
print('User Iteration %d [%.1f s]: HR = %.4f, NDCG = %.4f, [%.1f s]' %
(epoch, time() - t1, u_hr, u_ndcg, time() - t2))
hr, ndcg = evaluation(agree, helper, dataset.group_testRatings,
dataset.group_testNegatives, config.topK,
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
agree.train()
t1 = time()
training(agree, dataset.get_user_dataloader(
args.batch_size), epoch, args, 'user')
training(agree, dataset.get_group_dataloader(
args.batch_size), epoch, args, 'group')
print("user and group training time is: [%.1f s]" % (time() - t1))
t2 = time()
# evaluation
u_hrs, u_ndcgs = [0, 0, 0, 0], [0, 0, 0, 0]
hrs, ndcgs = evaluation(
agree, helper, dataset.group_testRatings, dataset.group_testNegatives, 'group')
for i, _ in enumerate(tops):
print('top%d:Group Iteration %d [%.1f s]: HR = %.4f, '
'NDCG = %.4f, [%.1f s]' % (tops[i], epoch, time() - t1, hrs[i], ndcgs[i], time() - t2))
history.append([u_hrs, u_ndcgs, hrs, ndcgs])