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pmf-netflix.R
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pmf-netflix.R
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rm(list=ls())
setwd("C:/Users/Lenovo/Code/R/r_pmf_paper")
data=read.table("netflix.txt", header = FALSE)
rating=data[,3]
movie=data[,2]
user=data[,1]
data_num=length(rating)
movie_num= max(movie)#电影数量
user_num=max(user)#用户数量
#parametre
epsilon=50 # Learning rate 学习率
lambda = 0.1#Regularization parameter正则化参数
momentum=0.7#动量优化参数
epoch=1#初始化epoch
maxepoch=50#总训练次数
num_feat = 5 #Rank 10 decomposition 隐因子数量
err_train1=rep(0,maxepoch)
err_valid1=rep(0,maxepoch)
err_random=rep(0,maxepoch)
train_num=(data_num*9)%/%10;
train_vec=data[1:train_num,];#训练集
probe_vec=data[train_num:data_num,];#测试集
movie_num= max(movie)#电影数量1682
user_num=max(user)#用户数量943
#sum_list=max(data)
mean_rating = mean(train_vec[,3])#平均评级
pairs_tr = length((train_vec[,3]))#training data 训练集长度
pairs_pr = length((probe_vec[,3]))#validation data 验证集长度
numbatches= 9 #Number of batches把数据分为9份
num_m = movie_num # Number of movies 电影数量
num_p = user_num #Number of users 用户数量
#初始化
w1_M1 = 0.1*matrix(runif(num_m*num_feat),nrow = num_m , ncol = num_feat ,byrow =T) # Movie feature vectors 生成用户物品特征矩阵d=10
w1_P1 = 0.1*matrix(runif(num_p*num_feat),nrow = num_p , ncol = num_feat ,byrow =T) # User feature vecators
w1_M1_inc = matrix(rep(0,num_m*num_feat),nrow = num_m , ncol = num_feat ,byrow =T)#生成同shape的全零矩阵
w1_P1_inc = matrix(rep(0,num_p*num_feat),nrow = num_p , ncol = num_feat ,byrow =T)
for(epoch in epoch:maxepoch){
#采用mini batch的方法，每次训练9个样本
for(batch in 1:numbatches){
N=10000 #number training triplets per batch 每次训练三元组的数量
aa_p= train_vec[((batch-1)*N+1):(batch*N),1]#读取用户列每次读取一万个
aa_m= train_vec[((batch-1)*N+1):(batch*N),2]#读取电影列
rating = train_vec[((batch-1)*N+1):(batch*N),3]#读取评级列
rating = rating-mean_rating; #Default prediction is the mean rating.
# Compute Predictions %%%%%%%%%%%%%%%%%
pred_out= apply(w1_M1[aa_m,]*w1_P1[aa_p,],1,sum)#每一行进行求和,.*为对应元素相乘 10k*1，用隐特征矩阵相乘得到1w个预测评级
f=sum((pred_out - rating)^2 + 0.5*lambda*(apply((w1_M1[aa_m,]^2 + w1_P1[aa_p,]^2),1,sum) ))
#求出损失函数
#Compute Gradients %%%%%%%%%%%%%%%%%%%迭代
IO =2*(pred_out - rating)
kkkk=num_feat-1
for(kkk in 1:kkkk){
IO =cbind(IO,2*(pred_out - rating))
}
#IO = repmat(2*(pred_out - rating),1,num_feat)
#将损失矩阵的二倍，复制10列
Ix_m=IO*w1_P1[aa_p,] + lambda*w1_M1[aa_m,]#损失*U-lambda*V 就是更新规则
Ix_p=IO*w1_M1[aa_m,] + lambda*w1_P1[aa_p,]#损失*V-lambda*U
dw1_M1 = matrix(rep(0,num_m*num_feat),nrow = num_m , ncol = num_feat ,byrow =T)
dw1_P1 = matrix(rep(0,num_p*num_feat),nrow = num_p , ncol = num_feat ,byrow =T)#生成全零用户特征矩阵
for(ii in 1:N){#迭代一万次 每一行一行来 得到更新矩阵
dw1_M1[aa_m[ii],]= dw1_M1[aa_m[ii],] + Ix_m[ii,]
dw1_P1[aa_p[ii],]= dw1_P1[aa_p[ii],] + Ix_p[ii,]
}
# Update movie and user features %%%%%%%%%%%
w1_M1_inc = momentum*w1_M1_inc + epsilon*dw1_M1/N
w1_M1 = w1_M1 - w1_M1_inc#原矩阵-负导数*学习率
w1_P1_inc = momentum*w1_P1_inc + epsilon*dw1_P1/N
w1_P1 = w1_P1 - w1_P1_inc
}
#此时所有(9轮)batch结束
#现在已经得到了此轮epoch后的一组U和V
# Compute Predictions after Paramete Updates %%%%%%%%%%%%%%%%%
pred_out= apply(w1_M1[aa_m,]*w1_P1[aa_p,],1,sum)
f_s=sum((pred_out - rating)^2 + 0.5*lambda*(apply((w1_M1[aa_m,]^2 + w1_P1[aa_p,]^2),1,sum) ))
err_train1[epoch] = sqrt(f_s/N)
#Compute predictions on the validation set %%%%%%%%%%%%%%%%%%%%%%
NN=pairs_pr#验证集长度
aa_p = probe_vec[,1]#读取验证集的user、movie、rating
aa_m = probe_vec[,2]
rating = probe_vec[,3]
pred_out =apply(w1_M1[aa_m,]*w1_P1[aa_p,],1,sum) + mean_rating#预测结果加上mean才行
pred_out[pred_out>5]=5
pred_out[pred_out<1]=1 #使得预测结果超过评分区间的值依旧掉在区间内
err_valid1[epoch]= sqrt(sum((pred_out- rating)^2)/NN)
RMSE=sqrt(sum((pred_out- rating)^2)/NN)
MAE=sum(abs(pred_out- rating))/NN
print(paste('epoch',epoch,'Train RMSE',signif(err_train1[epoch], 4),'Test RMSE',signif(err_valid1[epoch], 4)))
}
print(paste('RMSE',signif(RMSE, 4),'MAE',signif(MAE, 4)))
#计算Pre@5 Re@5
# TOP-N 推荐
j=10
txt=numeric(user_num)
for(i in 1:user_num){
user_i_rating_real=probe_vec[(probe_vec$V1==i),]
user_i_rating_real=user_i_rating_real[order(user_i_rating_real$V3,decreasing = TRUE),]
user_i_rating=w1_P1[i,]%*%t(w1_M1[user_i_rating_real$V2,])+mean_rating
if(length(user_i_rating_real$V2)>j){
txt[i]=sum(rank(-1*user_i_rating)[1:j]<=j)/j
}
if(length(user_i_rating_real$V2)<=j){
ti=sum(user_i_rating_real$V3>=4)
if(ti!=0){
txt[i]=sum(user_i_rating[1:ti]>=4)/ti
}
}
}
Pre=mean(txt)
txt=numeric(user_num)
for(i in 1:user_num){
user_i_rating_real=probe_vec[(probe_vec$V1==i),]
user_i_rating_real=user_i_rating_real[order(user_i_rating_real$V3,decreasing = TRUE),]
user_i_rating=w1_P1[i,]%*%t(w1_M1[user_i_rating_real$V2,])+mean_rating
if(length(user_i_rating_real$V2)>j){
user_i_rating[user_i_rating<4]=0
user_i_rating[user_i_rating>4]=1
ti=sum(user_i_rating)
if(ti!=0){
bigerthan4=sum(user_i_rating_real$V3>=4)
tinri=sum(user_i_rating[1:bigerthan4])
txt[i]=tinri/ti
}
}
if(length(user_i_rating_real$V2)<=j){
ti=sum(user_i_rating_real$V3>=4)
if(ti!=0){
txt[i]=sum(user_i_rating[1:ti]>=4)/ti
}
}
}
Re=mean(txt)
print(paste('Re',signif(Re, 4),'Pre',signif(Pre, 4)))