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本例中使用得是著名得电影数据集MovieLens-100数据集
MoviesLens数据集是实现和测试电影推荐最常用得数据集之一,包含943个用户为精选得1682部电影给出得100000个电影评分
主要文件如下1:u.data 2:u.item 3:u.user
1:查看用户/电影排名信息得代码如下
- import pandas as pd
- heads=['user_id','item_id','rating','timestamp']
- ratings=pd.read_csv(r'u.data',sep='\t',names=heads)
- print(ratings)
- print("用户数量",len(ratings))
2:查看导入的电影数据表
代码如下
- import pandas as pd
- u_cols=['user_id','age','sex','occupation','zip_code']
- users=pd.read_csv(r'u.data',sep='|',names=u_cols,encoding='latin-1')
- print(users)
- r_cols=['user_id','movie_id','rating','unix_timestamp']
- ratings=pd.read_csv(r'u.data',sep='\t',names=r_cols,encoding='latin-1')
- print(ratings)
- m_cols=['movie_id','title','release_data','video_release_data','imdb_url']
- movies=pd.read_csv(r'u.item',sep='|',names=m_cols,usecols=range(5),encoding='latin-1')
- print(movies)
3:用协同过滤推荐算法进行电影推荐
误差评估如下
全部代码如下
- import pandas as pd
- import numpy as np
- from sklearn.metrics.pairwise import pairwise_distances
- np.set_printoptions(suppress=True) # 取消科学计数法输出
- pd.set_option('display.max_rows', None) # 展示所有行
- pd.set_option('display.max_columns', None) # 展示所有列
- def predict(scoredata,similarity,type='user'):
- #基于物品得推荐
- if type=='item':
- predt_mat=scoredata.dot(similarity)/np.array([np.abs(similarity).sum(axis=1)])
- elif type=='user':
- #计算用户评分值 减少用户评分高低习惯影响
- user_meanscorse=scoredata.mean(axis=1)
- score_diff=(scoredata-user_meanscorse.reshape(-1,1))
- predt_mat=user_meanscorse.reshape(-1,1)+similarity.dot(score_diff)/np.array([np.abs(similarity).sum(axis=1)]).T
- return predt_mat
-
- #读取数据
- print('step 1 读取数据')
- r_cols=['user_id','movie_id','rating','unix_timestamp']
- scoredata=pd.read_csv(r'u.data',sep='\t',names=r_cols,encoding='latin-1')
- print('数据形状',scoredata.shape)
- #生成用户-物品评分矩阵
- print('step2 生成 用户物品评分矩阵')
- n_users=943
- n_items=1682
- data_matrix=np.zeros((n_users,n_items))
- for line in range(np.shape(scoredata)[0]):
- row=scoredata['user_id'][line]-1
- col=scoredata['movie_id'][line]-1
- score=scoredata['rating'][line]
- data_matrix[row,col]=score
- print('用户物品矩阵形状',data_matrix.shape)
- #计算相似度
- print('step3 计算相似度')
- user_similaritry=pairwise_distances(data_matrix,metric='cosine')
- item_similarity=pairwise_distances(data_matrix.T,metric='cosine')
- print('user similarity',user_similaritry.shape)
- print('item similartity',item_similarity.shape)
- #进行相似度进行预测
- print('step4 预测')
- user_prediction=predict(data_matrix,user_similaritry,type='user')
- item_perdiction=predict(data_matrix,item_similarity,type='item')
-
- #显示推荐结果
- print('step 5 显示推荐结果')
- print('----------------')
- print('ubcf预测形状',user_prediction.shape)
- print('real answer\n',data_matrix[:5,5])
- print('预测结果\n',user_prediction)
- print('ibcf预测形状',item_perdiction.shape)
- print('real answer\n',data_matrix[:5,:5])
- print('预测结果\n',item_perdiction)
- #性能评估
- print('step 6 性能评估')
- from sklearn.metrics import mean_squared_error
- from math import sqrt
- def rmse(predct,realNum):
- predct=predct[realNum.nonzero()].flatten()
- realNum=realNum[realNum.nonzero()].flatten()
- return sqrt(mean_squared_error(predct,realNum))
- print('u-base mse=',str(rmse(user_prediction,data_matrix)))
- print('m-based mse=',str(rmse(item_perdiction,data_matrix)))
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