更新文件名

2026-05-08 06:33:55 +08:00 · 2017-06-05 11:50:16 +08:00
parent ac0f425015
commit 9448063ab2
11 changed files with 6 additions and 5 deletions
--- a/src/python/16.RecommenderSystems/usercf.py
+++ b/src/python/16.RecommenderSystems/usercf.py
@@ -0,0 +1,220 @@
+#!/usr/bin/python
+# coding:utf8
+
+'''
+Created on 2015-06-22
+Update  on 2017-05-16
+@author: Lockvictor/片刻
+《推荐系统实践》协同过滤算法源代码
+参考地址：https://github.com/Lockvictor/MovieLens-RecSys
+更新地址：https://github.com/apachecn/MachineLearning
+'''
+import sys
+import math
+import random
+from operator import itemgetter
+print(__doc__)
+# 作用：使得随机数据可预测
+random.seed(0)
+
+
+class UserBasedCF():
+    ''' TopN recommendation - UserBasedCF '''
+    def __init__(self):
+        self.trainset = {}
+        self.testset = {}
+
+        # n_sim_user: top 20个用户， n_rec_movie: top 10个推荐结果
+        self.n_sim_user = 20
+        self.n_rec_movie = 10
+
+        # 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
+        self.user_sim_mat = {}
+        self.movie_popular = {}
+        self.movie_count = 0
+
+        print >> sys.stderr, 'similar user number = %d' % self.n_sim_user
+        print >> sys.stderr, 'recommended movie number = %d' % self.n_rec_movie
+
+    @staticmethod
+    def loadfile(filename):
+        """loadfile(加载文件，返回一个生成器)
+
+        Args:
+            filename   文件名
+        Returns:
+            line       行数据，去空格
+        """
+        fp = open(filename, 'r')
+        for i, line in enumerate(fp):
+            yield line.strip('\r\n')
+            if i > 0 and i % 100000 == 0:
+                print >> sys.stderr, 'loading %s(%s)' % (filename, i)
+        fp.close()
+        print >> sys.stderr, 'load %s success' % filename
+
+    def generate_dataset(self, filename, pivot=0.7):
+        """loadfile(加载文件，将数据集按照7:3 进行随机拆分)
+
+        Args:
+            filename   文件名
+            pivot      拆分比例
+        """
+        trainset_len = 0
+        testset_len = 0
+
+        for line in self.loadfile(filename):
+            # 用户ID，电影名称，评分，时间戳
+            user, movie, rating, timestamp = line.split('::')
+            # 通过pivot和随机函数比较，然后初始化用户和对应的值
+            if (random.random() < pivot):
+
+                # dict.setdefault(key, default=None)
+                # key -- 查找的键值
+                # default -- 键不存在时，设置的默认键值
+                self.trainset.setdefault(user, {})
+                self.trainset[user][movie] = int(rating)
+                trainset_len += 1
+            else:
+                self.testset.setdefault(user, {})
+                self.testset[user][movie] = int(rating)
+                testset_len += 1
+
+        print >> sys.stderr, '分离训练集和测试集成功'
+        print >> sys.stderr, 'train set = %s' % trainset_len
+        print >> sys.stderr, 'test  set = %s' % testset_len
+
+    def calc_user_sim(self):
+        """calc_user_sim(计算用户之间的相似度)"""
+
+        # build inverse table for item-users
+        # key=movieID, value=list of userIDs who have seen this movie
+        print >> sys.stderr, 'building movie-users inverse table...'
+        movie2users = dict()
+
+        for user, movies in self.trainset.iteritems():
+            for movie in movies:
+                # inverse table for item-users
+                if movie not in movie2users:
+                    movie2users[movie] = set()
+                movie2users[movie].add(user)
+                # count item popularity at the same time
+                if movie not in self.movie_popular:
+                    self.movie_popular[movie] = 0
+                self.movie_popular[movie] += 1
+
+        print >> sys.stderr, 'build movie-users inverse table success'
+
+        # save the total movie number, which will be used in evaluation
+        self.movie_count = len(movie2users)
+        print >> sys.stderr, 'total movie number = %d' % self.movie_count
+
+        usersim_mat = self.user_sim_mat
+        # 统计在相同电影时，不同用户同时出现的次数
+        print >> sys.stderr, 'building user co-rated movies matrix...'
+
+        for movie, users in movie2users.iteritems():
+            for u in users:
+                for v in users:
+                    if u == v:
+                        continue
+                    usersim_mat.setdefault(u, {})
+                    usersim_mat[u].setdefault(v, 0)
+                    usersim_mat[u][v] += 1
+        print >> sys.stderr, 'build user co-rated movies matrix success'
+
+        # calculate similarity matrix
+        print >> sys.stderr, 'calculating user similarity matrix...'
+        simfactor_count = 0
+        PRINT_STEP = 2000000
+        for u, related_users in usersim_mat.iteritems():
+            for v, count in related_users.iteritems():
+                # 余弦相似度
+                usersim_mat[u][v] = count / math.sqrt(len(self.trainset[u]) * len(self.trainset[v]))
+                simfactor_count += 1
+                # 打印进度条
+                if simfactor_count % PRINT_STEP == 0:
+                    print >> sys.stderr, 'calculating user similarity factor(%d)' % simfactor_count
+
+        print >> sys.stderr, 'calculate user similarity matrix(similarity factor) success'
+        print >> sys.stderr, 'Total similarity factor number = %d' % simfactor_count
+
+    # @profile
+    def recommend(self, user):
+        """recommend(找出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)
+
+        Args:
+            user       用户
+        Returns:
+            rec_movie  电影推荐列表，按照相似度从大到小的排序
+        """
+        ''' Find K similar users and recommend N movies. '''
+        K = self.n_sim_user
+        N = self.n_rec_movie
+        rank = dict()
+        watched_movies = self.trainset[user]
+
+        # 计算top K 用户的相似度
+        # v=similar user, wuv=不同用户同时出现的次数
+        # 耗时分析：50.4%的时间在 line-160行
+        for v, wuv in sorted(self.user_sim_mat[user].items(), key=itemgetter(1), reverse=True)[0:K]:
+            for movie in self.trainset[v]:
+                if movie in watched_movies:
+                    continue
+                # predict the user's "interest" for each movie
+                rank.setdefault(movie, 0)
+                rank[movie] += wuv
+        # return the N best movies
+        return sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]
+
+    def evaluate(self):
+        ''' return precision, recall, coverage and popularity '''
+        print >> sys.stderr, 'Evaluation start...'
+
+        # 返回top N的推荐结果
+        N = self.n_rec_movie
+        # varables for precision and recall
+        # 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
+        hit = 0
+        rec_count = 0
+        test_count = 0
+        # varables for coverage
+        all_rec_movies = set()
+        # varables for popularity
+        popular_sum = 0
+
+        for i, user in enumerate(self.trainset):
+            if i > 0 and i % 500 == 0:
+                print >> sys.stderr, 'recommended for %d users' % i
+            test_movies = self.testset.get(user, {})
+            rec_movies = self.recommend(user)
+
+            # 对比测试集和推荐集的差异
+            for movie, w in rec_movies:
+                if movie in test_movies:
+                    hit += 1
+                all_rec_movies.add(movie)
+                # 计算用户对应的电影出现次数log值的sum加和
+                popular_sum += math.log(1 + self.movie_popular[movie])
+            rec_count += N
+            test_count += len(test_movies)
+
+        precision = hit / (1.0*rec_count)
+        recall = hit / (1.0*test_count)
+        coverage = len(all_rec_movies) / (1.0*self.movie_count)
+        popularity = popular_sum / (1.0*rec_count)
+
+        print >> sys.stderr, 'precision=%.4f \t recall=%.4f \t coverage=%.4f \t popularity=%.4f' % (precision, recall, coverage, popularity)
+
+
+if __name__ == '__main__':
+    ratingfile = 'input/16.RecommenderSystems/ml-1m/ratings.dat'
+
+    # 创建UserCF对象
+    usercf = UserBasedCF()
+    # 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
+    usercf.generate_dataset(ratingfile, pivot=0.7)
+    # 计算用户之间的相似度
+    usercf.calc_user_sim()
+    # 评估推荐效果
+    usercf.evaluate()