def analysis_recom_result(train_data, userId, recom_result):
"""分析指定用户userId的推荐结果。train_data是用于获取该用户之前感兴趣的item"""
item_info = read.get_item_info("../data/movies.csv")
for user_id, movieId, label in train_data:
if user_id == userId and label == 1:
print(item_info[movieId])
print("recom_result")
for item in recom_result:
print(item_info[item[0]])
def ana_recom_result(train_data, userid, recom_list):
item_info = read.get_item_info("../data/movies.txt")
for data_instance in train_data:
tmp_userid, itemid, label = data_instance
if label == 1 and tmp_userid == userid:
print(item_info)
print("recom result")
for zuhe in recom_list:
print(item_info[zuhe[0]])
def ana_recom_result(train_data, userid, recom_list): # 分析评估推荐结果
"""
:param train_data: train data for lfm model
:param userid: fix userid
:param recom_list: reeom result by lfm
"""
item_info = read.get_item_info("../data/mymovies.txt")
for data_instance in train_data:
tmp_userid, itemid, label = data_instance
if tmp_userid == userid and label == 1: # 训练集中，该user喜欢的电影详情
print(item_info[itemid])
print("recom result")
for zuhe in recom_list: # 组合是元组
print(item_info[zuhe[0]]) # 打印 推荐列表的电影详情
def ana_recom_result(train_data, userid, recom_list):
item_info = read.get_item_info(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) +
"\\recommender\\data\\ml-latest-small\\movies.csv")
for data_ins in train_data:
tmp_userid, itemid, label = data_ins
if tmp_userid == userid and label == 1:
print(item_info[itemid])
print("rec")
for rec in recom_list:
print(item_info[rec[0]])
def ana_recom_result(train_data, userid, recom_list):
"""
:param train_data: train data for lfm model
:param userid: fix userid
:param recom_list: recom result by lfm
"""
item_info = read.get_item_info("../data/movies.txt")
for data_instance in train_data:
tmp_userid, itemid, label = data_instance
if tmp_userid == userid and label == 1:
print(item_info[itemid])
print("recom result")
for zuhe in recom_list:
print(item_info[zuhe[0]])
def ana_recom_result(train_data, userid, recom_list):
"""
debug recom result for userid 此函数用来评价推荐结果的好坏
:param train_data: 训练数据，我们想展示一下之前用户对哪些Item感兴趣
:param userid: 分析哪个用户的推荐结果
:param recom_list: 模型给出的推荐结果
"""
item_info = read.get_item_info("../data/movies.txt")
for data_instance in train_data:
tmp_userid, itemid, label = data_instance
if tmp_userid == userid and label == 1:
print(item_info[itemid])
print("recom_result")
for zuhe in recom_list:
print(item_info[zuhe[0]])
def ana_recom_result(train_data, userid, recom_list):
"""
debug recom result for userid
Args:
train_data: train data for lfm model
userid:fix userid
recom_list: recom result by lfm
"""
item_info = read.get_item_info("../data/movies.txt")
for data_instance in train_data:
tmp_userid, itemid, label = data_instance
if tmp_userid == userid and label == 1:
print item_info[itemid]
print "recom result"
for zuhe in recom_list:
print item_info[zuhe[0]]
def ana_recom_result(train_data,userid,recom_list):
'''
对算法推荐给用户的item进行分析
:param train_data:
:param userid:
:param recom_list:
:return:
'''
item_info=read.get_item_info('../data/movies.txt')
for data_distance in train_data:
tmp_userid,itemid,label=data_distance
if tmp_userid==userid and label==1:
print('user like:',item_info[itemid])
print('recom result')
for combination in recom_list:
print(item_info[combination[0]])
def get_one_user_recom():
"""
give fixed_user recom result
"""
user = "******"
alpha = 0.8
graph = read.get_graph_from_data("../data/ratings.dat")
iter_num = 100
recom_result = personal_rank(graph, user, alpha, iter_num, 100)
item_info = read.get_item_info("../data/movies.dat")
for itemid in graph[user]:
pure_itemid = itemid.split("_")[1]
print(item_info[pure_itemid])
print("----------")
for itemid in recom_result:
pure_itemid = itemid.split("_")[1]
print(item_info[pure_itemid], recom_result[itemid])
def ana_recom_result(train_data, userid, recom_list):
"""
debug recom result for userid
Args:
train_data
userid
recom_list
Return:
"""
item_info = get_item_info('../data/ml-latest-small/movies.csv')
for data_instance in train_data:
temp_userid,itemid,label = data_instance
if temp_userid == userid and label == 1:
print(item_info[itemid])
print('recom result:')
for value in recom_list:
print(item_info[value[0]])
def ana_recom_result(train_data, userid, recom_list):
"""
debug recom result for userid
:param train_data: train data for userid
:param userid: fix userid
:param recom_list: recom result by lfm
:return: no return
"""
# item_info = read.get_item_info("../data/ml-1m/movies.txt")
item_info = read.get_item_info("../data/movies.txt")
print("该用户曾给过好评的电影如下：")
for data_instance in train_data:
tmp_userid, itemid, label = data_instance
if tmp_userid == userid and label == 1:
print(item_info[itemid])
print("前n个推荐电影为：")
cnt = 1
for zuhe in recom_list:
print(cnt, item_info[zuhe[0]])
cnt += 1
def get_one_user_recom():
"""
give one fix_user recom result
:return:
"""
user = "******"
alpha = 0.6
graph = read.get_graph_from_data("../data/rating.txt")
iter_num = 100
recom_result = personal_rank(graph, user, alpha, iter_num)
item_info = read.get_item_info("../data/movies.txt")
for itemid in graph[user]:
pure_itemid = itemid.split("_")[1]
print(item_info[pure_itemid])
print("result---")
for itemid in recom_result:
pure_itemid = itemid.split("_")[1]
print(item_info[pure_itemid])
print(recom_result[itemid])
return recom_result
reverse=True)[:recom_num]:
point, score = combination[0], combination[1]
recom_dict[point] = score
return recom_dict
def get_one_user_by_matrix():
user = '******'
alpha = 0.8
graph = read.get_graph_from_data('../data/ratings.txt')
recom_result = personal_rank_matrix(graph, user, alpha, 100)
return recom_result
if __name__ == '__main__':
item_info = read.get_item_info('../data/movies.txt')
recom_result_base = get_one_user_recom()
recom_result_matrix = get_one_user_by_matrix()
#测试部分1：观察推荐内容
'''
for itemid in recom_result_base:
pure_itemid=itemid.split('_')[1]
print(item_info[pure_itemid])
print(recom_result_base[itemid])
print('----------------')
for itemid in recom_result_matrix:
pure_itemid=itemid.split('_')[1]
print(item_info[pure_itemid])
print(recom_result_matrix[itemid])
'''
