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218 lines
8.1 KiB
Python
218 lines
8.1 KiB
Python
#!/usr/bin/python
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# coding:utf8
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'''
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Created on 2015-06-22
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Update on 2017-05-16
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@author: Lockvictor/片刻
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《推荐系统实践》协同过滤算法源代码
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参考地址：https://github.com/Lockvictor/MovieLens-RecSys
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更新地址：https://github.com/apachecn/MachineLearning
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'''
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import sys
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import math
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import random
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from operator import itemgetter
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print(__doc__)
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# 作用：使得随机数据可预测
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random.seed(0)
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class UserBasedCF():
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''' TopN recommendation - UserBasedCF '''
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def __init__(self):
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self.trainset = {}
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self.testset = {}
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# n_sim_user: top 20个用户， n_rec_movie: top 10个推荐结果
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self.n_sim_user = 20
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self.n_rec_movie = 10
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# user_sim_mat: 用户之间的相似度， movie_popular: 电影的出现次数， movie_count: 总电影数量
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self.user_sim_mat = {}
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self.movie_popular = {}
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self.movie_count = 0
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print >> sys.stderr, 'similar user number = %d' % self.n_sim_user
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print >> sys.stderr, 'recommended movie number = %d' % self.n_rec_movie
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@staticmethod
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def loadfile(filename):
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"""loadfile(加载文件，返回一个生成器)
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Args:
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filename 文件名
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Returns:
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line 行数据，去空格
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"""
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fp = open(filename, 'r')
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for i, line in enumerate(fp):
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yield line.strip('\r\n')
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if i > 0 and i % 100000 == 0:
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print >> sys.stderr, 'loading %s(%s)' % (filename, i)
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fp.close()
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print >> sys.stderr, 'load %s success' % filename
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def generate_dataset(self, filename, pivot=0.7):
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"""loadfile(加载文件，将数据集按照7:3 进行随机拆分)
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Args:
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filename 文件名
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pivot 拆分比例
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"""
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trainset_len = 0
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testset_len = 0
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for line in self.loadfile(filename):
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# 用户ID，电影名称，评分，时间戳
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user, movie, rating, timestamp = line.split('::')
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# 通过pivot和随机函数比较，然后初始化用户和对应的值
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if (random.random() < pivot):
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# dict.setdefault(key, default=None)
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# key -- 查找的键值
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# default -- 键不存在时，设置的默认键值
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self.trainset.setdefault(user, {})
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self.trainset[user][movie] = int(rating)
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trainset_len += 1
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else:
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self.testset.setdefault(user, {})
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self.testset[user][movie] = int(rating)
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testset_len += 1
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print >> sys.stderr, '分离训练集和测试集成功'
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print >> sys.stderr, 'train set = %s' % trainset_len
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print >> sys.stderr, 'test set = %s' % testset_len
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def calc_user_sim(self):
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"""calc_user_sim(计算用户之间的相似度)"""
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# build inverse table for item-users
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# key=movieID, value=list of userIDs who have seen this movie
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print >> sys.stderr, 'building movie-users inverse table...'
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movie2users = dict()
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for user, movies in self.trainset.iteritems():
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for movie in movies:
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# inverse table for item-users
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if movie not in movie2users:
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movie2users[movie] = set()
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movie2users[movie].add(user)
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# count item popularity at the same time
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if movie not in self.movie_popular:
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self.movie_popular[movie] = 0
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self.movie_popular[movie] += 1
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print >> sys.stderr, 'build movie-users inverse table success'
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# save the total movie number, which will be used in evaluation
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self.movie_count = len(movie2users)
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print >> sys.stderr, 'total movie number = %d' % self.movie_count
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usersim_mat = self.user_sim_mat
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# 统计在相同电影时，用户同时出现的次数
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print >> sys.stderr, 'building user co-rated movies matrix...'
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for movie, users in movie2users.iteritems():
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for u in users:
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for v in users:
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if u == v:
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continue
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usersim_mat.setdefault(u, {})
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usersim_mat[u].setdefault(v, 0)
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usersim_mat[u][v] += 1
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print >> sys.stderr, 'build user co-rated movies matrix success'
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# calculate similarity matrix
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print >> sys.stderr, 'calculating user similarity matrix...'
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simfactor_count = 0
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PRINT_STEP = 2000000
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for u, related_users in usersim_mat.iteritems():
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for v, count in related_users.iteritems():
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# 余弦相似度
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usersim_mat[u][v] = count / math.sqrt(len(self.trainset[u]) * len(self.trainset[v]))
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simfactor_count += 1
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# 打印进度条
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if simfactor_count % PRINT_STEP == 0:
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print >> sys.stderr, 'calculating user similarity factor(%d)' % simfactor_count
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print >> sys.stderr, 'calculate user similarity matrix(similarity factor) success'
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print >> sys.stderr, 'Total similarity factor number = %d' % simfactor_count
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def recommend(self, user):
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"""recommend(推荐top K的用户，所看过的电影，对电影进行相似度sum的排序，取出top N的电影数)
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Args:
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user 用户
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Returns:
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rec_movie 电影推荐列表，按照相似度从大到小的排序
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"""
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''' Find K similar users and recommend N movies. '''
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K = self.n_sim_user
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N = self.n_rec_movie
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rank = dict()
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watched_movies = self.trainset[user]
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# 找出top 10的用户和相似度
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# v=similar user, wuv=similarity factor
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for v, wuv in sorted(self.user_sim_mat[user].items(), key=itemgetter(1), reverse=True)[0:K]:
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for movie in self.trainset[v]:
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if movie in watched_movies:
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continue
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# predict the user's "interest" for each movie
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rank.setdefault(movie, 0)
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rank[movie] += wuv
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# return the N best movies
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return sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]
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def evaluate(self):
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''' return precision, recall, coverage and popularity '''
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print >> sys.stderr, 'Evaluation start...'
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# 返回top 10的推荐结果
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N = self.n_rec_movie
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# varables for precision and recall
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# hit表示命中(测试集和推荐集相同+1)，rec_count 每个用户的推荐数， test_count 每个用户对应的测试数据集的电影数
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hit = 0
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rec_count = 0
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test_count = 0
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# varables for coverage
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all_rec_movies = set()
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# varables for popularity
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popular_sum = 0
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for i, user in enumerate(self.trainset):
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if i > 0 and i % 500 == 0:
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print >> sys.stderr, 'recommended for %d users' % i
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test_movies = self.testset.get(user, {})
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rec_movies = self.recommend(user)
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# 对比测试集和推荐集的差异
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for movie, w in rec_movies:
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if movie in test_movies:
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hit += 1
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all_rec_movies.add(movie)
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# 计算用户对应的电影出现次数log值的sum加和
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popular_sum += math.log(1 + self.movie_popular[movie])
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rec_count += N
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test_count += len(test_movies)
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precision = hit / (1.0*rec_count)
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recall = hit / (1.0*test_count)
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coverage = len(all_rec_movies) / (1.0*self.movie_count)
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popularity = popular_sum / (1.0*rec_count)
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print >> sys.stderr, 'precision=%.4f \t recall=%.4f \t coverage=%.4f \t popularity=%.4f' % (precision, recall, coverage, popularity)
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if __name__ == '__main__':
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ratingfile = 'input/16.RecommendedSystem/ml-1m/ratings.dat'
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# 创建UserCF对象
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usercf = UserBasedCF()
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# 将数据按照 7:3的比例，拆分成：训练集和测试集，存储在usercf的trainset河testset中
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usercf.generate_dataset(ratingfile, pivot=0.7)
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# 计算用户之间的相似度
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usercf.calc_user_sim()
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# 评估推荐效果
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usercf.evaluate()
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