Coercion of matrix to sparse matrix (dgCMatrix) and maintaining dimnames.

Søren Højsgaard — written Jan 20, 2013 — source

Consider the following matrix

nr <- nc <- 6
set.seed <- 123
m <- matrix(sample(c(rep(0,9), 1),nr*nc, replace=T), nrow=nr, ncol=nc)
sum(m)/length(m)
[1] 0.1667
dimnames(m) <- list(letters[1:nr], letters[1:nc])
m
  a b c d e f
a 0 0 0 0 0 1
b 0 0 0 1 0 1
c 0 0 0 0 0 0
d 0 0 0 0 0 0
e 1 1 0 0 0 0
f 0 0 0 1 0 0

This matrix can be coerced to a sparse matrix with

library("Matrix")
Loading required package: methods
M1 <- as(m, "dgCMatrix")
M1
6 x 6 sparse Matrix of class "dgCMatrix"
  a b c d e f
a . . . . . 1
b . . . 1 . 1
c . . . . . .
d . . . . . .
e 1 1 . . . .
f . . . 1 . .
str(M1)
Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
  ..@ i       : int [1:6] 4 4 1 5 0 1
  ..@ p       : int [1:7] 0 1 2 2 4 4 6
  ..@ Dim     : int [1:2] 6 6
  ..@ Dimnames:List of 2
  .. ..$ : chr [1:6] "a" "b" "c" "d" ...
  .. ..$ : chr [1:6] "a" "b" "c" "d" ...
  ..@ x       : num [1:6] 1 1 1 1 1 1
  ..@ factors : list()

Using Eigen via RcppEigen we can obtain the coercion as:

// [[Rcpp::depends(RcppEigen)]]

#include <RcppEigen.h>
#include <Rcpp.h>

using namespace Rcpp;
// [[Rcpp::export]]
SEXP asdgCMatrix_( SEXP XX_ ){
typedef Eigen::SparseMatrix<double> SpMat;
typedef Eigen::Map<Eigen::MatrixXd> MapMatd; // Input: must be double
MapMatd X(Rcpp::as<MapMatd>(XX_));
SpMat Xsparse = X.sparseView(); // Output: sparse matrix
S4 Xout(wrap(Xsparse)); // Output: as S4 object
NumericMatrix Xin(XX_); // Copy dimnames
Xout.slot("Dimnames") = clone(List(Xin.attr("dimnames")));
return(Xout);
}
(M2 <- asdgCMatrix_(m * 1.0))
6 x 6 sparse Matrix of class "dgCMatrix"
  a b c d e f
a . . . . . 1
b . . . 1 . 1
c . . . . . .
d . . . . . .
e 1 1 . . . .
f . . . 1 . .
str(M2)
Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
  ..@ i       : int [1:6] 4 4 1 5 0 1
  ..@ p       : int [1:7] 0 1 2 2 4 4 6
  ..@ Dim     : int [1:2] 6 6
  ..@ Dimnames:List of 2
  .. ..$ : chr [1:6] "a" "b" "c" "d" ...
  .. ..$ : chr [1:6] "a" "b" "c" "d" ...
  ..@ x       : num [1:6] 1 1 1 1 1 1
  ..@ factors : list()
identical(M1, M2)
[1] TRUE

Compare the performance:

cols <- c("test", "replications", "elapsed", "relative", "user.self", "sys.self")	
rbenchmark::benchmark(asdgCMatrix_(m * 1.0), as(m, "dgCMatrix"),
columns=cols, order="relative", replications=1000)
                 test replications elapsed relative user.self sys.self
1 asdgCMatrix_(m * 1)         1000   0.028     1.00     0.028    0.000
2  as(m, "dgCMatrix")         1000   0.287    10.25     0.284    0.004

For larger matrices the difference in performance gain is smaller:

## 100 x 100 matrix
nr <- nc <- 100
set.seed <- 123
m <- matrix(sample(c(rep(0,9), 1),nr*nc, replace=T), nrow=nr, ncol=nc)
rbenchmark::benchmark(asdgCMatrix_(m * 1.0), as(m, "dgCMatrix"),
columns=cols, order="relative", replications=1000)
                 test replications elapsed relative user.self sys.self
1 asdgCMatrix_(m * 1)         1000   0.133    1.000     0.132    0.000
2  as(m, "dgCMatrix")         1000   0.359    2.699     0.356    0.004
## 1000 x 1000 matrix
nr <- nc <- 1000
set.seed <- 123
m <- matrix(sample(c(rep(0,9), 1),nr*nc, replace=T), nrow=nr, ncol=nc)
rbenchmark::benchmark(asdgCMatrix_(m * 1.0), as(m, "dgCMatrix"),
columns=cols, order="relative", replications=100)
                 test replications elapsed relative user.self sys.self
1 asdgCMatrix_(m * 1)          100   1.193     1.00     1.184    0.004
2  as(m, "dgCMatrix")          100   2.303     1.93     2.092    0.204
## 3000 x 3000 matrix
nr <- nc <- 3000
set.seed <- 123
m <- matrix(sample(c(rep(0,9), 1),nr*nc, replace=T), nrow=nr, ncol=nc)
rbenchmark::benchmark(asdgCMatrix_(m * 1.0), as(m, "dgCMatrix"),
columns=cols, order="relative", replications=100)
                 test replications elapsed relative user.self sys.self
1 asdgCMatrix_(m * 1)          100   8.868    1.000      5.82    3.004
2  as(m, "dgCMatrix")          100  23.441    2.643     18.70    4.636

Thanks to Doug Bates for illustrating to me how set the dimnames attribute.

tags: eigen  matrix  sparse 

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