How to optimize this process? - r

Question

I have somewhat of broad question, but I will try to make my intent as clear as possible so that people can make suggestions. I am trying to optimize a process I am doing. Generally, what I am doing is feeding a function a data frame of values and generating a prediction off of operations on specific columns. Basically a custom function that is being used with sapply (code below). What I'm doing is much to large to provide any meaningful example, so instead I will try to describe the inputs to the process. I know this will restrict how helpful answers can be, but I am interested in any ideas for optimizing the time it takes me to compute a prediction. Currently it is taking me about 10 seconds to generate one prediction (run the sapply for one line of a dataframe).
mean_rating <- function(df){
user<-df$user
movie<-df$movie
u_row<-which(U_lookup == user)[1]
m_row<-which(M_lookup==movie)[1]
knn_match<- knn_txt[u_row,1:100]
knn_match1<-as.numeric(unlist(knn_match))
dfm_test<- dfm[knn_match1,]
dfm_mov<- dfm_test[,m_row] # row number from DFM associated with the query_movie
C<-mean(dfm_mov)
}
test<-sapply(1:nrow(probe_test),function(x) mean_rating(probe_test[x,]))
Inputs:
dfm is my main data matrix, users in the rows and movies in the columns. Very sparse.
> str(dfm)
Formal class 'dgTMatrix' [package "Matrix"] with 6 slots
..# i : int [1:99072112] 378 1137 1755 1893 2359 3156 3423 4380 5103 6762 ...
..# j : int [1:99072112] 0 0 0 0 0 0 0 0 0 0 ...
..# Dim : int [1:2] 480189 17770
..# Dimnames:List of 2
.. ..$ : NULL
.. ..$ : NULL
..# x : num [1:99072112] 4 5 4 1 4 5 4 5 3 3 ...
..# factors : list()
probe_test is my test set, the set I'm trying to predict for. The actual probe test contains approximately 1.4 million rows but I am trying it on a subset first to optimize the time. It is being fed into my function.
> str(probe_test)
'data.frame': 6 obs. of 6 variables:
$ X : int 1 2 3 4 5 6
$ movie : int 1 1 1 1 1 1
$ user : int 1027056 1059319 1149588 1283744 1394012 1406595
$ Rating : int 3 3 4 3 5 4
$ Rating_Date: Factor w/ 1929 levels "2000-01-06","2000-01-08",..: 1901 1847 1911 1312 1917 1803
$ Indicator : int 1 1 1 1 1 1
U_lookup is the lookup I use to convert between user id and the line of the matrix a user is in since we lose user id's when they are converted to a sparse matrix.
> str(U_lookup)
'data.frame': 480189 obs. of 1 variable:
$ x: int 10 100000 1000004 1000027 1000033 1000035 1000038 1000051 1000053 1000057 ...
M_lookup is the lookup I use to convert between movie id and the column of a matrix a movie is in for similar reasons as above.
> str(M_lookup)
'data.frame': 17770 obs. of 1 variable:
$ x: int 1 10 100 1000 10000 10001 10002 10003 10004 10005 ...
knn_text contains the 100 nearest neighbors for all the lines of dfm
> str(knn_txt)
'data.frame': 480189 obs. of 200 variables:
Thank you for any advice you can provide to me.