240722 knn 使用皮尔逊找到相似用户

import json
 import numpy as np# 计算皮尔逊系数
 def pearson_score(dataset, user1, user2):
     if user1 not in dataset:
         raise TypeError('User ' + user1 + ' not present in the dataset')    if user2 not in dataset:
         raise TypeError('User ' + user2 + ' not present in the dataset')    # 提取两个用户都评分过的电影
     rated_by_both = {}    for item in dataset[user1]:
         if item in dataset[user2]:
             rated_by_both[item] = 1    num_ratings = len(rated_by_both) 
    # 都没有评分则是 0 
     if num_ratings == 0:
         return 0    # 计算相同评分的平方值和
     user1_sum = np.sum([dataset[user1][item] for item in rated_by_both])
     user2_sum = np.sum([dataset[user2][item] for item in rated_by_both])    # 计算所有相同评分电影的评分的平方和
     user1_squared_sum = np.sum([np.square(dataset[user1][item]) for item in rated_by_both])
     user2_squared_sum = np.sum([np.square(dataset[user2][item]) for item in rated_by_both])    # 计算数据集乘积之和
     product_sum = np.sum([dataset[user1][item] * dataset[user2][item] for item in rated_by_both])    # 计算皮尔逊相关度
     Sxy = product_sum - (user1_sum * user2_sum / num_ratings)
     Sxx = user1_squared_sum - np.square(user1_sum) / num_ratings
     Syy = user2_squared_sum - np.square(user2_sum) / num_ratings
     # 分母为0的处理
     if Sxx * Syy == 0:
         return 0    return Sxy / np.sqrt(Sxx * Syy)
if __name__=='__main__':
     data_file = 'movie_ratings.json'    with open(data_file, 'r') as f:
         data = json.loads(f.read())    user1 = 'John Carson'
     user2 = 'Michelle Peterson'    print "\nPearson score:"
     print pearson_score(data, user1, user2)