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rDict = {} # 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ex(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()

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。