python3实现基于用户的协同过滤

本文实例为大家分享了python3实现基于用户协同过滤的具体代码，供大家参考，具体内容如下

废话不多说，直接看代码。

#!/usr/bin/python3 # -*- coding: utf-8 -*- #20170916号协同过滤电影推荐基稿 #字典等格式数据处理及直接写入文件   ##from numpy import * import time from math import sqrt ##from texttable import Texttable   class CF:   def __init__(self, movies, ratings, k=5, n=20):   self.movies = movies#[MovieID,Title,Genres]   (self.train_data,self.test_data) = (ratings[0], ratings[1])#[UserID::MovieID::Rating::Timestamp]   # 邻居个数   self.k = k   # 推荐个数   self.n = n   # 用户对电影的评分   # 数据格式{'UserID用户ID':[(MovieID电影ID,Rating用户对电影的评星)]}   self.userDict = {}   # 对某电影评分的用户   # 数据格式：{'MovieID电影ID':[UserID,用户ID]}   # {'1',[1,2,3..],...}   self.ItemUser = {}   # 邻居的信息   self.neighbors = []   # 推荐列表   self.recommandList = []#包含dist和电影id   self.recommand = [] #训练集合测试集的交集，且仅有电影id   #用户评过电影信息   self.train_user = []   self.test_user = []   #给用户的推荐列表，仅含movieid   self.train_rec =[]   self.test_rec = []   #test中的电影评分预测数据集合,   self.forecast = {}#前k个近邻的评分集合   self.score = {}#最终加权平均后的评分集合{“电影id”：预测评分}   #召回率和准确率   self.pre = [0.0,0.0]   self.z = [0.0, 0.0]  '''''  userDict数据格式：  '3': [('3421', 0.8), ('1641', 0.4), ('648', 0.6), ('1394', 0.8), ('3534', 0.6), ('104', 0.8),  ('2735', 0.8), ('1210', 0.8), ('1431', 0.6), ('3868', 0.6), ('1079', 1.0), ('2997', 0.6),  ('1615', 1.0), ('1291', 0.8), ('1259', 1.0), ('653', 0.8), ('2167', 1.0), ('1580', 0.6),  ('3619', 0.4), ('260', 1.0), ('2858', 0.8), ('3114', 0.6), ('1049', 0.8), ('1261', 0.2),  ('552', 0.8), ('480', 0.8), ('1265', 0.4), ('1266', 1.0), ('733', 1.0), ('1196', 0.8),  ('590', 0.8), ('2355', 1.0), ('1197', 1.0), ('1198', 1.0), ('1378', 1.0), ('593', 0.6),  ('1379', 0.8), ('3552', 1.0), ('1304', 1.0), ('1270', 0.6), ('2470', 0.8), ('3168', 0.8),  ('2617', 0.4), ('1961', 0.8), ('3671', 1.0), ('2006', 0.8), ('2871', 0.8), ('2115', 0.8),  ('1968', 0.8), ('1136', 1.0), ('2081', 0.8)]}  ItemUser数据格式：  {'42': ['8'], '2746': ['10'], '2797': ['1'], '2987': ['5'], '1653': ['5', '8', '9'],  '194': ['5'], '3500': ['8', '10'], '3753': ['6', '7'], '1610': ['2', '5', '7'],  '1022': ['1', '10'], '1244': ['2'], '25': ['8', '9']  '''   # 将ratings转换为userDict和ItemUser  def formatRate(self,train_or_test):   self.userDict = {}   self.ItemUser = {}   for i in train_or_test:#[UserID,MovieID,Rating,Timestamp]    # 评分最高为5 除以5 进行数据归一化 ##   temp = (i[1], float(i[2]) / 5)    temp = (i[1], float(i[2])) ##   temp = (i[1], i[2])    # 计算userDict {'用户id':[(电影id,评分),(2,5)...],'2':[...]...}一个观众对每一部电影的评分集合    if(i[0] in self.userDict):     self.userDict[i[0]].append(temp)    else:     self.userDict[i[0]] = [temp]    # 计算ItemUser {'电影id',[用户id..],...}同一部电影的观众集合    if(i[1] in self.ItemUser):     self.ItemUser[i[1]].append(i[0])    else:     self.ItemUser[i[1]] = [i[0]]     # 格式化userDict数据  def formatuserDict(self, userId, p):#userID为待查询目标，p为近邻对象   user = {}   #user数据格式为：电影id：[userID的评分，近邻用户的评分]   for i in self.userDict[userId]:#i为userDict数据中的每个括号同81行    user[i[0]] = [i[1], 0]   for j in self.userDict[p]:    if(j[0] not in user):     user[j[0]] = [0, j[1]]#说明目标用户和近邻用户没有同时对一部电影评分    else:     user[j[0]][1] = j[1]#说明两者对同一部电影都有评分   return user        # 计算余弦距离  def getCost(self, userId, p):   # 获取用户userId和p评分电影的并集   # {'电影ID'：[userId的评分，p的评分]} 没有评分为0   user = self.formatuserDict(userId, p)   x = 0.0   y = 0.0   z = 0.0   for k, v in user.items():#k是键，v是值    x += float(v[0]) * float(v[0])    y += float(v[1]) * float(v[1])    z += float(v[0]) * float(v[1])   if(z == 0.0):    return 0   return z / sqrt(x * y)  #计算皮尔逊相似度 ##  def getCost(self, userId, p): ##   # 获取用户userId和l评分电影的并集 ##   # {'电影ID'：[userId的评分，l的评分]} 没有评分为0 ##   user = self.formatuserDict(userId, p) ##   sumxsq = 0.0 ##   sumysq = 0.0 ##   sumxy = 0.0 ##   sumx = 0.0 ##   sumy = 0.0 ##   n = len(user) ##   for k, v in user.items(): ##    sumx +=float(v[0]) ##    sumy +=float(v[1]) ##    sumxsq += float(v[0]) * float(v[0]) ##    sumysq += float(v[1]) * float(v[1]) ##    sumxy += float(v[0]) * float(v[1]) ##   up = sumxy -sumx*sumy/n ##   down = sqrt((sumxsq - pow(sumxsq,2)/n)*(sumysq - pow(sumysq,2)/n)) ##   if(down == 0.0): ##    return 0 ##   return up/down  # 找到某用户的相邻用户  def getNearestNeighbor(self, userId):   neighbors = []   self.neighbors = []   # 获取userId评分的电影都有那些用户也评过分   for i in self.userDict[userId]:#i为userDict数据中的每个括号同95行#user数据格式为：电影id：[userID的评分，近邻用户的评分]    for j in self.ItemUser[i[0]]:#i[0]为电影编号，j为看同一部电影的每位用户     if(j != userId and j not in neighbors):      neighbors.append(j)   # 计算这些用户与userId的相似度并排序   for i in neighbors:#i为用户id    dist = self.getCost(userId, i)    self.neighbors.append([dist, i])   # 排序默认是升序，reverse=True表示降序   self.neighbors.sort(reverse=True)   self.neighbors = self.neighbors[:self.k]#切片操作，取前k个 ##  print('neighbors',len(neighbors))    # 获取推荐列表  def getrecommandList(self, userId):   self.recommandList = []   # 建立推荐字典   recommandDict = {}   for neighbor in self.neighbors:#这里的neighbor数据格式为[[dist，用户id],[],....]    movies = self.userDict[neighbor[1]]#movies数据格式为[(电影id，评分),()，。。。。]    for movie in movies:     if(movie[0] in recommandDict):      recommandDict[movie[0]] += neighbor[0]####？？？？     else:      recommandDict[movie[0]] = neighbor[0]    # 建立推荐列表   for key in recommandDict:#recommandDict数据格式{电影id：累计dist，。。。}    self.recommandList.append([recommandDict[key], key])#recommandList数据格式【【累计dist，电影id】，【】，。。。。】   self.recommandList.sort(reverse=True) ##  print(len(self.recommandList))   self.recommandList = self.recommandList[:self.n] ##  print(len(self.recommandList))  # 推荐的准确率  def getPrecision(self, userId): ##  print("开始！！！") #先运算test_data，这样最终self.neighbors等保留的是后来计算train_data后的数据(不交换位置的话就得在gR函数中增加参数保留各自的neighbor)   (self.test_user,self.test_rec) = self.getRecommand(self.test_data,userId)#测试集的用户userId所评价的电影和给该用户推荐的电影列表   (self.train_user,self.train_rec) = self.getRecommand(self.train_data,userId)#训练集的用户userId所评价的所有电影集合(self.train_user)和给该用户推荐的电影列表(self.train_rec) #西安电大的张海朋：基于协同过滤的电影推荐系统的构建（2015）中的准确率召回率计算   for i in self.test_rec:    if i in self.train_rec:     self.recommand.append(i)   self.pre[0] = len(self.recommand)/len(self.train_rec)   self.z[0] = len(self.recommand)/len(self.test_rec)   #北京交大黄宇：基于协同过滤的推荐系统设计与实现（2015）中的准、召计算   self.recommand = []#这里没有归零的话，下面计算初始recommand不为空   for i in self.train_rec:    if i in self.test_user:     self.recommand.append(i)   self.pre[1] = len(self.recommand)/len(self.train_rec)   self.z[1] = len(self.recommand)/len(self.test_user) ##  print(self.train_rec,self.test_rec,"20",len(self.train_rec),len(self.train_rec))   #对同一用户分别通过训练集和测试集处理  def getRecommand(self,train_or_test,userId):   self.formatRate(train_or_test)   self.getNearestNeighbor(userId)   self.getrecommandList(userId)   user = [i[0] for i in self.userDict[userId]]#用户userId评分的所有电影集合   recommand = [i[1] for i in self.recommandList]#推荐列表仅有电影id的集合，区别于recommandList（还含有dist） ##  print("userid该用户已通过训练集测试集处理")   return (user,recommand)  #对test的电影进行评分预测  def foreCast(self):   self.forecast = {}#？？？？？前面变量统一定义初始化后，函数内部是否需要该初始化？？？？   same_movie_id = []   neighbors_id = [i[1] for i in self.neighbors] #近邻用户数据仅含用户id的集合         for i in self.test_user:#i为电影id，即在test里的i有被推荐到    if i in self.train_rec:     same_movie_id.append(i)     for j in self.ItemUser[i]:#j为用户id,即寻找近邻用户的评分和相似度      if j in neighbors_id:       user = [i[0] for i in self.userDict[j]]#self.userDict[userId]数据格式：数据格式为[(电影id，评分),()，。。。。]；这里的userid应为近邻用户p       a = self.neighbors[neighbors_id.index(j)]#找到该近邻用户的数据【dist，用户id】       b = self.userDict[j][user.index(i)]#找到该近邻用户的数据【电影id，用户id】       c = [a[0], b[1], a[1]]       if (i in self.forecast):        self.forecast[i].append(c)       else:        self.forecast[i] = [c]#数据格式：字典{“电影id”：【dist，评分，用户id】【】}{'589': [[0.22655856915174025, 0.6, '419'], [0.36264561173211646, 1.0, '1349']。。。} ##  print(same_movie_id)   #每个近邻用户的评分加权平均计算得预测评分   self.score = {}   if same_movie_id :#在test里的电影是否有在推荐列表里，如果为空不做判断，下面的处理会报错    for movieid in same_movie_id:     total_d = 0     total_down = 0     for d in self.forecast[movieid]:#此时的d已经是最里层的列表了【】；self.forecast[movieid]的数据格式[[]]      total_d += d[0]*d[1]      total_down += d[0]     self.score[movieid] = [round(total_d/total_down,3)]#加权平均后取3位小数的精度    #在test里但是推荐没有的电影id,这里先按零计算    for i in self.test_user:     if i not in movieid:      self.score[i] = [0]   else:    for i in self.test_user:     self.score[i] = [0] ##  return self.score  #计算平均绝对误差MAE  def cal_Mae(self,userId):   self.formatRate(self.test_data) ##  print(self.userDict)   for item in self.userDict[userId]:    if item[0] in self.score:     self.score[item[0]].append(item[1])#self.score数据格式[[预测分，实际分]] ##  #过渡代码 ##  for i in self.score: ##   pass   return self.score     # 基于用户的推荐  # 根据对电影的评分计算用户之间的相似度 ## def recommendByUser(self, userId): ##  print("亲，请稍等片刻，系统正在快马加鞭为你运作中")   #人机交互辅助解读， ##  self.getPrecision(self,userId)   # 获取数据 def readFile(filename):  files = open(filename, "r", encoding = "utf-8")  data = []  for line in files.readlines():   item = line.strip().split("::")   data.append(item)  return data  files.close() def load_dict_from_file(filepath):  _dict = {}  try:   with open(filepath, 'r',encoding = "utf -8") as dict_file:    for line in dict_file.readlines():     (key, value) = line.strip().split(':')     _dict[key] = value  except IOError as ioerr:   print ("文件 %s 不存在" % (filepath))  return _dict def save_dict_to_file(_dict, filepath):  try:   with open(filepath, 'w',encoding = "utf - 8") as dict_file:    for (key,value) in _dict.items():     dict_file.write('%s:%s/n' % (key, value))   except IOError as ioerr:   print ("文件 %s 无法创建" % (filepath)) def writeFile(data,filename):  with open(filename, 'w', encoding = "utf-8")as f:   f.write(data)   # -------------------------开始-------------------------------  def start3():  start1 = time.clock()  movies = readFile("D:/d/movies.dat")  ratings = [readFile("D:/d/201709train.txt"),readFile("D:/d/201709test.txt")]  demo = CF(movies, ratings, k=20)  userId = '1000'  demo.getPrecision(userId) ## print(demo.foreCast())  demo.foreCast()  print(demo.cal_Mae(userId)) ## demo.recommendByUser(ID)  #上一句只能实现固定用户查询，这句可以实现“想查哪个查哪个”，后期可以加个循环，挨个查，查到你不想查  print("处理的数据为%d条" % (len(ratings[0])+len(ratings[1]))) ## print("____---",len(ratings[0]),len(ratings[1])) ## print("准确率： %.2f %%" % (demo.pre * 100)) ## print("召回率： %.2f %%" % (demo.z * 100))  print(demo.pre)  print(demo.z)  end1 = time.clock()  print("耗费时间： %f s" % (end1 - start1)) def start1():  start1 = time.clock()  movies = readFile("D:/d/movies.dat")  ratings = [readFile("D:/d/201709train.txt"),readFile("D:/d/201709test.txt")]  demo = CF(movies, ratings, k = 20)  demo.formatRate(ratings[0])  writeFile(str(demo.userDict),"D:/d/dd/userDict.txt")  writeFile(str(demo.ItemUser), "D:/d/dd/ItemUser.txt") ## save_dict_to_file(demo.userDict,"D:/d/dd/userDict.txt") ## save_dict_to_file(demo.ItemUser,"D:/d/dd/ItemUser.txt")  print("处理结束") ## with open("D:/d/dd/userDict.txt",'r',encoding = 'utf-8') as f: ##  diction = f.read() ##  i = 0 ##  for j in eval(diction): ##   print(j) ##   i += 1 ##   if i == 4: ##    break def start2():  start1 = time.clock()  movies = readFile("D:/d/movies.dat")  ratings = [readFile("D:/d/201709train.txt"),readFile("D:/d/201709test.txt")]  demo = CF(movies, ratings, k = 20)  demo.formatRate_toMovie(ratings[0])  writeFile(str(demo.movieDict),"D:/d/dd/movieDict.txt") ## writeFile(str(demo.userDict),"D:/d/dd/userDict.txt") ## writeFile(str(demo.ItemUser), "D:/d/dd/ItemUser.txt") ## save_dict_to_file(demo.userDict,"D:/d/dd/userDict.txt") ## save_dict_to_file(demo.ItemUser,"D:/d/dd/ItemUser.txt")  print("处理结束")   if __name__ == '__main__':  start1()