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基于随机梯度下降的矩阵分解推荐算法(python)

发布时间:2018-08-31 09:55:21 作者:ge_nius

这篇文章主要为大家详细介绍了基于随机梯度下降的矩阵分解推荐算法,文中示例代码介绍的非常详细,具有一定的参考价值,感兴趣的小伙伴们可以参考一下

SVD是矩阵分解常用的方法,其原理为:矩阵M可以写成矩阵A、B与C相乘得到,而B可以与A或者C合并,就变成了两个元素M1与M2的矩阵相乘可以得到M。

矩阵分解推荐的思想就是基于此,将每个user和item的内在feature构成的矩阵分别表示为M1与M2,则内在feature的乘积得到M;因此我们可以利用已有数据(user对item的打分)通过随机梯度下降的方法计算出现有user和item最可能的feature对应到的M1与M2(相当于得到每个user和每个item的内在属性),这样就可以得到通过feature之间的内积得到user没有打过分的item的分数。

本文所采用的数据是movielens中的数据,且自行切割成了train和test,但是由于数据量较大,没有用到全部数据。

代码如下:

# -*- coding: utf-8 -*-
"""
Created on Mon Oct 9 19:33:00 2017
@author: wjw
"""
import pandas as pd
import numpy as np
import os
 
def difference(left,right,on): #求两个dataframe的差集
 df = pd.merge(left,right,how='left',on=on) #参数on指的是用于连接的列索引名称
 left_columns = left.columns
 col_y = df.columns[-1] # 得到最后一列
 df = df[df[col_y].isnull()]#得到boolean的list
 df = df.iloc[:,0:left_columns.size]#得到的数据里面还有其他同列名的column
 df.columns = left_columns # 重新定义columns
 return df
 
def readfile(filepath): #读取文件,同时得到训练集和测试集
 
 pwd = os.getcwd()#返回当前工程的工作目录
 os.chdir(os.path.dirname(filepath))
 #os.path.dirname()获得filepath文件的目录;chdir()切换到filepath目录下
 initialData = pd.read_csv(os.path.basename(filepath))
 #basename()获取指定目录的相对路径
 os.chdir(pwd)#回到先前工作目录下
 predData = initialData.iloc[:,0:3] #将最后一列数据去掉
 newIndexData = predData.drop_duplicates()
 trainData = newIndexData.sample(axis=0,frac = 0.1) #90%的数据作为训练集
 testData = difference(newIndexData,trainData,['userId','movieId']).sample(axis=0,frac=0.1)
 return trainData,testData
 
def getmodel(train):
 slowRate = 0.99
 preRmse = 10000000.0
 max_iter = 100
 features = 3
 lamda = 0.2
 gama = 0.01 #随机梯度下降中加入,防止更新过度
 user = pd.DataFrame(train.userId.drop_duplicates(),columns=['userId']).reset_index(drop=True) #把在原来dataFrame中的索引重新设置,drop=True并抛弃
 
 movie = pd.DataFrame(train.movieId.drop_duplicates(),columns=['movieId']).reset_index(drop=True)
 userNum = user.count().loc['userId'] #671
 movieNum = movie.count().loc['movieId'] 
 userFeatures = np.random.rand(userNum,features) #构造user和movie的特征向量集合
 movieFeatures = np.random.rand(movieNum,features)
 #假设每个user和每个movie有3个feature
 userFeaturesFrame =user.join(pd.DataFrame(userFeatures,columns = ['f1','f2','f3']))
 movieFeaturesFrame =movie.join(pd.DataFrame(movieFeatures,columns= ['f1','f2','f3']))
 userFeaturesFrame = userFeaturesFrame.set_index('userId')
 movieFeaturesFrame = movieFeaturesFrame.set_index('movieId') #重新设置index
 
 for i in range(max_iter): 
  rmse = 0
  n = 0
  for index,row in user.iterrows():
   uId = row.userId
   userFeature = userFeaturesFrame.loc[uId] #得到userFeatureFrame中对应uId的feature
 
   u_m = train[train['userId'] == uId] #找到在train中userId点评过的movieId的data
   for index,row in u_m.iterrows(): 
    u_mId = int(row.movieId)
    realRating = row.rating
    movieFeature = movieFeaturesFrame.loc[u_mId] 
 
    eui = realRating-np.dot(userFeature,movieFeature)
    rmse += pow(eui,2)
    n += 1
    userFeaturesFrame.loc[uId] += gama * (eui*movieFeature-lamda*userFeature) 
    movieFeaturesFrame.loc[u_mId] += gama*(eui*userFeature-lamda*movieFeature)
  nowRmse = np.sqrt(rmse*1.0/n)
  print('step:%f,rmse:%f'%((i+1),nowRmse))
  if nowRmse<preRmse:
   preRmse = nowRmse
  elif nowRmse<0.5:
   break
  elif nowRmse-preRmse<=0.001:
   break
  gama*=slowRate
 return userFeaturesFrame,movieFeaturesFrame
 
def evaluate(userFeaturesFrame,movieFeaturesFrame,test):
 test['predictRating']='NAN' # 新增一列
 
 for index,row in test.iterrows(): 
  
  print(index)
  userId = row.userId
  movieId = row.movieId
  if userId not in userFeaturesFrame.index or movieId not in movieFeaturesFrame.index:
   continue
  userFeature = userFeaturesFrame.loc[userId]
  movieFeature = movieFeaturesFrame.loc[movieId]
  test.loc[index,'predictRating'] = np.dot(userFeature,movieFeature) #不定位到不能修改值
  
 return test 
 
if __name__ == "__main__":
 filepath = r"E:\学习\研究生\推荐系统\ml-latest-small\ratings.csv"
 train,test = readfile(filepath)
 userFeaturesFrame,movieFeaturesFrame = getmodel(train)
 result = evaluate(userFeaturesFrame,movieFeaturesFrame,test)

在test中得到的结果为:

NAN则是训练集中没有的数据

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。

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