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213 lines
7.9 KiB
Python
213 lines
7.9 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 ItemBasedCF():
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''' TopN recommendation - ItemBasedCF '''
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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_movie = 20
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self.n_rec_movie = 10
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# user_sim_mat: 电影之间的相似度， movie_popular: 电影的出现次数， movie_count: 总电影数量
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self.movie_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 movie number = %d' % self.n_sim_movie
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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, _ = 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_movie_sim(self):
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"""calc_movie_sim(计算用户之间的相似度)"""
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print >> sys.stderr, 'counting movies number and popularity...'
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for user, movies in self.trainset.iteritems():
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for movie in movies:
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# count item popularity
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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, 'count movies number and popularity success'
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# save the total number of movies
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self.movie_count = len(self.movie_popular)
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print >> sys.stderr, 'total movie number = %d' % self.movie_count
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# 统计在相同用户时，不同电影同时出现的次数
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itemsim_mat = self.movie_sim_mat
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print >> sys.stderr, 'building co-rated users matrix...'
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for user, movies in self.trainset.iteritems():
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for m1 in movies:
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for m2 in movies:
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if m1 == m2:
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continue
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itemsim_mat.setdefault(m1, {})
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itemsim_mat[m1].setdefault(m2, 0)
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itemsim_mat[m1][m2] += 1
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print >> sys.stderr, 'build co-rated users matrix success'
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# calculate similarity matrix
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print >> sys.stderr, 'calculating movie similarity matrix...'
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simfactor_count = 0
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PRINT_STEP = 2000000
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for m1, related_movies in itemsim_mat.iteritems():
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for m2, count in related_movies.iteritems():
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# 余弦相似度
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itemsim_mat[m1][m2] = count / math.sqrt(self.movie_popular[m1] * self.movie_popular[m2])
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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 movie similarity factor(%d)' % simfactor_count
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print >> sys.stderr, 'calculate movie similarity matrix(similarity factor) success'
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print >> sys.stderr, 'Total similarity factor number = %d' % simfactor_count
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# @profile
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def recommend(self, user):
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"""recommend(找出top K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 movies and recommend N movies. '''
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K = self.n_sim_movie
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N = self.n_rec_movie
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rank = {}
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watched_movies = self.trainset[user]
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# 计算top K 电影的相似度
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# rating=电影评分, w=不同电影出现的次数
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# 耗时分析：98.2%的时间在 line-154行
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for movie, rating in watched_movies.iteritems():
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for related_movie, w in sorted(self.movie_sim_mat[movie].items(), key=itemgetter(1), reverse=True)[0:K]:
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if related_movie in watched_movies:
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continue
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rank.setdefault(related_movie, 0)
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rank[related_movie] += w * rating
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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 N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.RecommenderSystems/ml-1m/ratings.dat'
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# 创建ItemCF对象
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itemcf = ItemBasedCF()
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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和testset中
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itemcf.generate_dataset(ratingfile, pivot=0.7)
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# 计算用户之间的相似度
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itemcf.calc_movie_sim()
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# 评估推荐效果
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itemcf.evaluate()
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