逐行分析下面的代码：print('Building new-user table ...') new_user = {} for user, news in trainSet.items(): for new in news: if new not in new_user: new_user[new] = set() new_user[new].add(user) print('Build new-user table success!') new_count = len(new_user) print('Total new number = %d' % new_count) print('Build user co-rated users matrix ...') for new, users in new_user.items(): for u in users: for v in users: if u == v: continue user_sim_matrix.setdefault(u, {}) user_sim_matrix[u].setdefault(v, 0) user_sim_matrix[u][v] += 1 print('Build user co-rated users matrix success!') print('Calculating user similarity matrix ...') for u, related_users in user_sim_matrix.items(): for v, count in related_users.items(): user_sim_matrix[u][v] = count / math.sqrt(len(trainSet[u]) * len(trainSet[v])) print('Calculate user similarity matrix success!')

这段代码的作用是建立一个基于用户的协同过滤推荐系统，用于推荐给用户他们可能感兴趣的新闻。

首先，代码输出 "Building new-user table ..." 的提示信息，表示正在构建新用户表。然后，通过遍历训练集 trainSet 中的每个用户和他们的新闻，将用户和其阅读过的新闻加入到新用户表 new_user 中。

接下来，程序遍历新用户表中的每个新闻和阅读过该新闻的用户，然后构建用户协同矩阵，即统计共同阅读过某个新闻的所有用户的数量，并将其存储在 user_sim_matrix 中。

最后，程序通过计算用户相似度矩阵，即将用户协同矩阵中的数量转化为相似度得分，来计算用户之间的相似度，并以 "Calculating user similarity matrix ..." 的提示信息表示该过程正在进行中，最终输出 "Calculate user similarity matrix success!" 的提示信息表示计算完成。

相关问题

逐行分析下面的代码：import random import numpy as np import pandas as pd import math from operator import itemgetter data_path = './ml-latest-small/' data = pd.read_csv(data_path+'ratings.csv') data.head() data.pivot(index='userId', columns='newId', values='rating') trainSet, testSet = {}, {} trainSet_len, testSet_len = 0, 0 pivot = 0.75 for ele in data.itertuples(): user, new, rating = getattr(ele, 'userId'), getattr(ele, 'newId'), getattr(ele, 'rating') if random.random() < pivot: trainSet.setdefault(user, {}) trainSet[user][new] = rating trainSet_len += 1 else: testSet.setdefault(user, {}) testSet[user][new] = rating testSet_len += 1 print('Split trainingSet and testSet success!') print('TrainSet = %s' % trainSet_len) print('TestSet = %s' % testSet_len) user_sim_matrix = {} print('Building new-user table ...') new_user = {} for user, news in trainSet.items(): for new in news: if new not in new_user: new_user[new] = set() new_user[new].add(user) print('Build new-user table success!') new_count = len(new_user) print('Total new number = %d' % new_count) print('Build user co-rated users matrix ...') for new, users in new_user.items(): for u in users: for v in users: if u == v: continue user_sim_matrix.setdefault(u, {}) user_sim_matrix[u].setdefault(v, 0) user_sim_matrix[u][v] += 1 print('Build user co-rated users matrix success!') print('Calculating user similarity matrix ...') for u, related_users in user_sim_matrix.items(): for v, count in related_users.items(): user_sim_matrix[u][v] = count / math.sqrt(len(trainSet[u]) * len(trainSet[v])) print('Calculate user similarity matrix success!') k = 20 n = 10 aim_user = 3 rank ={} watched_news = trainSet[aim_user] for v, wuv in sorted(user_sim_matrix[aim_user].items(), key=lambda x: x[1], reverse=True)[0:k]: for new in trainSet[v]: if new in watched_news: continue rank.setdefault(new, 0) rank[new] += wuv rec_news = sorted(rank.items(), key=itemgetter(1), reverse=True)[:n] rec_news

这段代码是一个协同过滤推荐算法的实现，主要包括以下步骤：

1. 导入需要的包：random、numpy、pandas、math、itemgetter。

2. 读取评分数据文件，将数据拆分成训练集和测试集，并统计它们的大小。

3. 构建新闻-用户表，记录每个用户评分过的新闻。

4. 构建用户相似度矩阵，记录每个用户与其他用户的相似度。

5. 根据目标用户和相似度矩阵，推荐目标用户可能感兴趣的新闻。

具体实现细节如下：

1. 读取评分数据文件，并将评分数据拆分成训练集和测试集，以随机数的方式将每个评分数据划分到训练集或测试集中。

2. 构建新闻-用户表，记录每个用户评分过的新闻。具体实现是遍历训练集中的每个评分数据，然后将用户和新闻作为字典的键，评分作为字典的值，添加到训练集字典中。

3. 构建用户相似度矩阵，记录每个用户与其他用户的相似度。具体实现是遍历新闻-用户表，对于每个新闻，将评分过该新闻的所有用户记录到一个集合中。然后遍历集合中的每个用户对，计算它们之间的相似度，存储到用户相似度矩阵中。

4. 根据目标用户和相似度矩阵，推荐目标用户可能感兴趣的新闻。具体实现是遍历相似度矩阵中与目标用户相似度最高的k个用户，然后遍历这些用户评分过的新闻，计算每个新闻与目标用户的相似度权重，最后按权重排序，选取前n个新闻作为推荐结果。

逐行分析下面的代码：import random import numpy as np import pandas as pd import math from operator import itemgetter data_path = './ml-latest-small/' data = pd.read_csv(data_path+'ratings.csv') data.head() data.pivot(index='userId', columns='newId', values='rating') trainSet, testSet = {}, {} trainSet_len, testSet_len = 0, 0 pivot = 0.75 for ele in data.itertuples(): user, new, rating = getattr(ele, 'userId'), getattr(ele, 'newId'), getattr(ele, 'rating') if random.random() < pivot: trainSet.setdefault(user, {}) trainSet[user][new] = rating trainSet_len += 1 else: testSet.setdefault(user, {}) testSet[user][new] = rating testSet_len += 1 print('Split trainingSet and testSet success!') print('TrainSet = %s' % trainSet_len) print('TestSet = %s' % testSet_len) new_popular = {} for user, news in trainSet.items(): for new in news: if new not in new_popular: new_popular[new] = 0 new_popular[new] += 1 new_count = len(new_popular) print('Total movie number = %d' % new_count) print('Build user co-rated news matrix ...') new_sim_matrix = {} for user, news in trainSet.items(): for m1 in news: for m2 in news: if m1 == m2: continue new_sim_matrix.setdefault(m1, {}) new_sim_matrix[m1].setdefault(m2, 0) new_sim_matrix[m1][m2] += 1 print('Build user co-rated movies matrix success!') print('Calculating news similarity matrix ...') for m1, related_news in new_sim_matrix.items(): for m2, count in related_news.items(): if new_popular[m1] == 0 or new_popular[m2] == 0: new_sim_matrix[m1][m2] = 0 else: new_sim_matrix[m1][m2] = count / math.sqrt(new_popular[m1] * new_popular[m2]) print('Calculate news similarity matrix success!') k = 20 n = 10 aim_user = 20 rank ={} watched_news = trainSet[aim_user] for new, rating in watched_news.items(): for related_new, w in sorted(new_sim_matrix[new].items(), key=itemgetter(1), reverse=True)[:k]: if related_new in watched_news: continue rank.setdefault(related_new, 0) rank[related_new] += w * float(rating) rec_news = sorted(rank.items(), key=itemgetter(1), reverse=True)[:n] rec_news

这段代码是一个电影推荐系统的实现。下面是对代码的逐行分析：

1. `import random import numpy as np import pandas as pd import math from operator import itemgetter`：导入需要使用的库

2. `data_path = './ml-latest-small/' data = pd.read_csv(data_path+'ratings.csv') data.head()`：读取电影评分数据，将其存储在一个DataFrame中，并输出前5行数据

3. `data.pivot(index='userId', columns='newId', values='rating')`：将数据按照用户ID和电影ID进行重塑，创建一个用户-电影评分的矩阵

4. `trainSet, testSet = {}, {} trainSet_len, testSet_len = 0, 0 pivot = 0.75`：初始化训练集和测试集，并设置训练集占比为0.75

5. `for ele in data.itertuples():`：遍历数据中的每一行

6. `user, new, rating = getattr(ele, 'userId'), getattr(ele, 'newId'), getattr(ele, 'rating')`：获取每一行数据中的用户ID、电影ID和评分

7. `if random.random() < pivot: trainSet.setdefault(user, {}) trainSet[user][new] = rating trainSet_len += 1 else: testSet.setdefault(user, {}) testSet[user][new] = rating testSet_len += 1`：根据训练集占比将数据划分为训练集和测试集，并统计训练集和测试集中的电影数量

8. `print('Split trainingSet and testSet success!') print('TrainSet = %s' % trainSet_len) print('TestSet = %s' % testSet_len)`：输出训练集和测试集的电影数量

9. `new_popular = {} for user, news in trainSet.items(): for new in news: if new not in new_popular: new_popular[new] = 0 new_popular[new] += 1`：统计每部电影的流行度（出现次数）

10. `new_count = len(new_popular) print('Total movie number = %d' % new_count)`：输出电影总数

11. `new_sim_matrix = {} for user, news in trainSet.items(): for m1 in news: for m2 in news: if m1 == m2: continue new_sim_matrix.setdefault(m1, {}) new_sim_matrix[m1].setdefault(m2, 0) new_sim_matrix[m1][m2] += 1`：构建用户-电影协同过滤矩阵，统计每对电影被多少个用户共同观看过

12. `print('Build user co-rated movies matrix success!')`：输出构建协同过滤矩阵成功信息

13. `for m1, related_news in new_sim_matrix.items(): for m2, count in related_news.items(): if new_popular[m1] == 0 or new_popular[m2] == 0: new_sim_matrix[m1][m2] = 0 else: new_sim_matrix[m1][m2] = count / math.sqrt(new_popular[m1] * new_popular[m2])`：计算电影之间的相似度，使用余弦相似度度量

14. `print('Calculate news similarity matrix success!')`：输出计算电影相似度成功信息

15. `k = 20 n = 10 aim_user = 20`：定义参数，包括推荐电影的数量和目标用户ID

16. `rank ={} watched_news = trainSet[aim_user] for new, rating in watched_news.items(): for related_new, w in sorted(new_sim_matrix[new].items(), key=itemgetter(1), reverse=True)[:k]: if related_new in watched_news: continue rank.setdefault(related_new, 0) rank[related_new] += w * float(rating) rec_news = sorted(rank.items(), key=itemgetter(1), reverse=True)[:n]`：为目标用户推荐电影，根据用户观看历史和电影相似度计算推荐度，并将推荐度排序输出前n个推荐电影。