def evaluation(model, helper, testRatings, testNegatives, K, type_m):
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__':
# 初始化参数
config = Config()
# 初始化工具函数
helper = Helper()
# get the dict of follow in user
u_f_d = helper.gen_user_follow_dict(config.follow_in_user_path2)
# 加载群组内的用户，构成dict
# {groupid: [uid1, uid2, ..], ...} 组用户数据
g_m_d = helper.gen_group_member_dict(config.user_in_group_path2)
# 初始化数据类 在这里可以决定使用哪个数据
dataset = GDataset(config.user_dataset2, config.group_dataset2,
config.num_negatives)
# 获取群组的数目、训练集中用户的数目、训练集中物品的数目
num_group = len(g_m_d)
num_users, num_items = dataset.num_users, dataset.num_items
print("num_group is: " + str(num_group))
def evaluation(model, helper, testRatings, testNegatives, K, type_m):
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()
# 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)
def evaluation(model, helper, testRatings, testNegatives, K, type_m):
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):