将稀疏矩阵写入R中的CSV

Question

I have a sparse matrix ( dgCMatrix ) as the result of fitting a glmnet . I want to write this result to a .csv but can't use write.table() the matrix because it can't coerced into a data.frame .

Is there a way to coerce the sparse matrix to either a data.frame or a regular matrix? Or is there a way to write it to a file while keeping the coefficient names which are probably row names?

Answer 1

That will be dangerous to transform the sparse matrix to a normal one, if the sparse matrix size is too large. In my case (text classification task), I got a matrix of size 22490 by 120,000. If you try get the dense matrix, that will be more than 20 GB, I think. Then R will break down !

So my suggestion, you may simply store the sparse matrix in an efficient and memory friendly way, such as Matrix Market Format , which keeps all non-zero values and their coordinates (row & col number). In the R you can use the method writeMM

Answer 2

as.matrix() will convert to the full dense representation:

> as.matrix(Matrix(0, 3, 2))
     [,1] [,2]
[1,]    0    0
[2,]    0    0
[3,]    0    0

You can write the resulting object out using write.csv or write.table .

Answer 3

Converting directly to a dense matrix is likely to waste a lot of memory. The R package Matrix allows converting the sparse matrix into a memory-efficient coordinate triplet format data frame using the summary() function, which could then be written easily to csv. This is probably simpler and easier than the matrix market approach. See the answer to this related question: Sparse matrix to a data frame in R

Also, here is an illustration from the Matrix package documentation :

## very simple export - in triplet format - to text file:
data(CAex)
s.CA <- summary(CAex)
s.CA # shows  (i, j, x)  [columns of a data frame]
message("writing to ", outf <- tempfile())
write.table(s.CA, file = outf, row.names=FALSE)
## and read it back -- showing off  sparseMatrix():
str(dd <- read.table(outf, header=TRUE))
## has columns (i, j, x) -> we can use via do.call() as arguments to sparseMatrix():
mm <- do.call(sparseMatrix, dd)
stopifnot(all.equal(mm, CAex, tolerance=1e-15))

Answer 4

# input: a sparse matrix with named rows and columns (dimnames)
# returns: a data frame representing triplets (r, c, x) suitable for writing to a CSV file
sparse2triples <- function(m) {
 SM = summary(m)
 D1 = m@Dimnames[[1]][SM[,1]]
 D2 = m@Dimnames[[2]][SM[,2]]
 data.frame(row=D1, col=D2, x=m@x)
}

Example

> library(Matrix)
> dn <- list(LETTERS[1:3], letters[1:5])
> m <- sparseMatrix(i = c(3,1,3,2,2,1), p= c(0:2, 4,4,6), x = 1:6, dimnames = dn)

> m
3 x 5 sparse Matrix of class "dgCMatrix"
  a b c d e
A . 2 . . 6
B . . 4 . 5
C 1 . 3 . .

> sparse2triples(m)
  row col x
1   C   a 1
2   A   b 2
3   B   c 4
4   C   c 3
5   A   e 6
6   B   e 5 

[EDIT: use data.frame]