def model_train_process():
"""
test lfm model train
"""
train_data = read.get_train_data("../data/ratings.txt")
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
for userid in user_vec:
recom_result = give_recom_result(user_vec, item_vec, userid)
ana_recom_result(train_data, userid, recom_result)
def train_process():
ratings_file = '../data/ml-latest-small/ratings.csv'
train_data = get_train_data(ratings_file)
iterTimes = 30 #迭代次数
F = 50 #隐类因子个数
alpha = 0.3 #步长
belta = 0.01 #正则化参数
user_vector, item_vector = train(train_data, iterTimes, alpha, F, belta)
print(user_vector)
print(item_vector)
def model_train_process():
'''
test lfm model train
'''
train_data = read.get_train_data('../data/ratings.txt')
user_vec, item_vec = lfm_train(train_data, 50, 0.01, 0.1, 50)
#print(user_vec['1'])
#print(item_vec['2455'])
recom_list = give_recom_result(user_vec, item_vec, '24')
ana_recom_result(train_data, '24', recom_list)
def model_train_process():
"""
测试lfm模型训练
"""
train_data = read.get_train_data("../../data/movies/ratings.csv")
user_vec, item_vec = lfm_main(train_data, 50, 0.01, 0.1, 50)
print(user_vec['1'])
# print(item_vec)
# print(item_vec['1'])
print(item_vec['2455'])
recom_result = give_recom_result(user_vec, item_vec, "24")
ana_recom_result(train_data, "24", recom_result)
def model_train_process():
"""
test lfm model train
"""
train_data = read.get_train_data("../data/ratings.txt")
user_vec, item_vec = lfm_train(train_data, 100, 0.01, 0.1, 50)
# for userid in user_vec:
recom_result = give_recom_result(user_vec, item_vec, "24")
# print(userid + "\n")
# print(recom_result)
# print("\n")
print(recom_result)
ana_recom_result(train_data, "24", recom_result)