def fillMatrix(F_list, step_list, LearnRating_list, penalty_list, N = 30):
trainDataSet = FO.readDataSet('trainDataSet.txt')
testDataSet = FO.readDataSet('testDataSet.txt')
userList = FO.readUserList('data/users.txt')
itemList = FO.readItemList('data/movies.txt') # 获取物品列表
dataSet = {**trainDataSet, **testDataSet}
mu = calMu(dataSet)
for F in F_list:
for step in step_list:
for LearnRating in LearnRating_list:
for penalty in penalty_list:
print(F,step,LearnRating,penalty)
path = 'SVD++/Matrix/' + str(F) + '-' + str(step) + '-' + str(LearnRating) + '-' + str(penalty)
if not os.path.exists(path):
os.makedirs(path)
bu, bi, p, q = ReadParameter(F, step, LearnRating, penalty)
for user in userList:
print(user)
rank = dict()
userRated = set([user_item[1] for user_item, value in dataSet.items() if user_item[0] == user])
UnRatedList = itemList - userRated
for item in UnRatedList:
rating = Predict(user, item, p, q, bu, bi, mu)
if math.isnan(float(rating)):
continue
rank[item] = round(rating)
if len(rank) == 0:
continue
if N > len(UnRatedList):
N = len(UnRatedList)
chooseList = random.sample(UnRatedList, N)
with open(path + '/new_ratings.txt', 'a') as fileObject:
for choose in chooseList:
fileObject.write(str(user) + '::' + str(choose) + '::' + str(rank[choose]) + '\n')
def calUserSimilarity(trainDataSet):
'''
:param trainDataSet: 训练数据集
:param type: 以何种方式计算相似度
:param simMeas: 计算相似的方法
:return: None
最后将用户之间的相似度写到文件里面
'''
# 建立 物品 到 用户 的倒排表
item_users = dict()
for user_item, rating in trainDataSet.items():
user = user_item[0]
item = user_item[1]
if item not in item_users:
item_users[item] = set()
item_users[item].add(user)
# for item, user in item_users.items():
# print(item, ':', user)
# 读取用户列表
userList = FO.readUserList('data/users.txt')
UserRatingMean = FO.ReadRatingMean(
'UserCF/UserMean/add_trainDataSetRating_mean.txt')
# 遍历每一个用户, 计算用户之间的相似度
for user in userList:
print(user)
W = UserSimilarity(trainDataSet, item_users, userList, UserRatingMean,
user)
filename = 'UserCF/Similarity_add/' + str(user) + '.txt'
FO.WirteSimilarty(filename, W)
def calUserRatingMean(dataSet):
userList = FO.readUserList('data/users.txt')
userMeanDict = dict()
for user in userList:
print(user)
UserRating = [value for user_item, value in dataSet.items() if user_item[0] == user]
userMeanDict[user] = sum(UserRating) / len(UserRating)
return userMeanDict
def calUserSimilarity(trainDataSet, type='implicit', simMeas=cosSim):
'''
:param trainDataSet: 训练数据集
:param type: 以何种方式计算相似度
:param simMeas: 计算相似的方法
:return: None
最后将用户之间的相似度写到文件里面
'''
# 建立 物品 到 用户 的倒排表
item_users = dict()
for user_item, rating in trainDataSet.items():
user = user_item[0]
item = user_item[1]
if item not in item_users:
item_users[item] = set()
item_users[item].add(user)
# for item, user in item_users.items():
# print(item, ':', user)
# 读取用户列表
userList = FO.readUserList('data/users.txt')
# 遍历每一个用户, 计算用户之间的相似度
for user in userList:
print(user)
if type == 'implicit': # 判读计算相似度的方式, 这里隐式计算
W = UserSimilarity_implicit(userList, user)
filename = 'UserCF/UserSimilarity/implicit/' + str(user) + '.txt'
elif type == 'explicit': # 判读计算相似度的方式, 这里显式方式
W = UserSimilarity_explicit(trainDataSet, item_users, userList, user,simMeas)
filename ='UserCF/UserSimilarity/explicit/'+ str(simMeas.__name__)+'/'+str(user) + '.txt'
# 将结果写入文件之中
with open(filename, 'w') as fileObject:
for users, values in W.items():
for u in users:
fileObject.write(str(str(u) + '::'))
fileObject.write(str(values) + '\n')