python实现协同过滤推荐算法完整代码示例

#!/usr/bin/python3# -*- coding: utf-8 -*-from numpy import *import timefrom texttable import Texttableclass CF:  def __init__(self, movies, ratings, k=5, n=10):    self.movies = movies    self.ratings = ratings    # 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 = []    self.cost = 0.0  # 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):    self.formatRate()    # 推荐个数 等于 本身评分电影个数，用户计算准确率    self.n = len(self.userDict[userId])    self.getNearestNeighbor(userId)    self.getrecommandList(userId)    self.getPrecision(userId)  # 获取推荐列表  def getrecommandList(self, userId):    self.recommandList = []    # 建立推荐字典    recommandDict = {}    for neighbor in self.neighbors:      movies = self.userDict[neighbor[1]]      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:      self.recommandList.append([recommandDict[key], key])    self.recommandList.sort(reverse=True)    self.recommandList = self.recommandList[:self.n]  # 将ratings转换为userDict和ItemUser  def formatRate(self):    self.userDict = {}    self.ItemUser = {}    for i in self.ratings:      # 评分最高为5 除以5 进行数据归一化      temp = (i[1], float(i[2]) / 5)      # 计算userDict {'1':[(1,5),(2,5)...],'2':[...]...}      if(i[0] in self.userDict):        self.userDict[i[0]].append(temp)      else:        self.userDict[i[0]] = [temp]      # 计算ItemUser {'1',[1,2,3..],...}      if(i[1] in self.ItemUser):        self.ItemUser[i[1]].append(i[0])      else:        self.ItemUser[i[1]] = [i[0]]  # 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]:      for j in self.ItemUser[i[0]]:        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:      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]  # 格式化userDict数据  def formatuserDict(self, userId, l):    user = {}    for i in self.userDict[userId]:      user[i[0]] = [i[1], 0]    for j in self.userDict[l]:      if(j[0] not in user):        user[j[0]] = [0, j[1]]      else:        user[j[0]][1] = j[1]    return user  # 计算余弦距离  def getCost(self, userId, l):    # 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, l)    x = 0.0    y = 0.0    z = 0.0    for k, v in user.items():      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)  # 推荐的准确率  def getPrecision(self, userId):    user = [i[0] for i in self.userDict[userId]]    recommand = [i[1] for i in self.recommandList]    count = 0.0    if(len(user) >= len(recommand)):      for i in recommand:        if(i in user):          count += 1.0      self.cost = count / len(recommand)    else:      for i in user:        if(i in recommand):          count += 1.0      self.cost = count / len(user)  # 显示推荐列表  def showTable(self):    neighbors_id = [i[1] for i in self.neighbors]    table = Texttable()    table.set_deco(Texttable.HEADER)    table.set_cols_dtype(["t", "t", "t", "t"])    table.set_cols_align(["l", "l", "l", "l"])    rows = []    rows.append([u"movie ID", u"Name", u"release", u"from userID"])    for item in self.recommandList:      fromID = []      for i in self.movies:        if i[0] == item[1]:          movie = i          break      for i in self.ItemUser[item[1]]:        if i in neighbors_id:          fromID.append(i)      movie.append(fromID)      rows.append(movie)    table.add_rows(rows)    print(table.draw())# 获取数据def readFile(filename):  files = open(filename, "r", encoding="utf-8")  # 如果读取不成功试一下  # files = open(filename, "r", encoding="iso-8859-15")  data = []  for line in files.readlines():    item = line.strip().split("::")    data.append(item)  return data# -------------------------开始-------------------------------start = time.clock()movies = readFile("/home/hadoop/Python/CF/movies.dat")ratings = readFile("/home/hadoop/Python/CF/ratings.dat")demo = CF(movies, ratings, k=20)demo.recommendByUser("100")print("推荐列表为：")demo.showTable()print("处理的数据为%d条" % (len(demo.ratings)))print("准确率： %.2f %%" % (demo.cost * 100))end = time.clock()print("耗费时间： %f s" % (end - start))