Iterating non-zero elements in MappedSparseMatrix eigen - c++

Question

I am passing a small sparse matrix(for testing) to a C++ function from R. The matrix belongs to the class dgCMatrix as shown below:
5 x 5 sparse Matrix of class "dgCMatrix"
[1,] . . . . .
[2,] 1 1 . . .
[3,] . . . . .
[4,] . . 1 . .
[5,] . 1 . . .
I am iterating this matrix as mentioned in the documentation here.
My function prints out the value of the iterator and the row index, column index.
The c++ function is defined below:
#include <RcppEigen.h>
// [[Rcpp::depends(RcppEigen)]]
using Eigen::MappedSparseMatrix;
using Eigen::SparseMatrix;
using Eigen::VectorXi;
using Eigen::Map;
using namespace Rcpp;
using namespace std;
// [[Rcpp::export]]
void createRec(RObject sparse_mat, IntegerVector sparse_vec) {
const MappedSparseMatrix<int> spmat(as<MappedSparseMatrix<int> >(sparse_mat));
long int nrow = spmat.rows();
long int ncol = spmat.cols();
NumericVector sim(nrow);
for(int k=0;k<spmat.outerSize();k++){
for(SparseMatrix<int,Eigen::ColMajor>::InnerIterator it(spmat,k);it;++it){
cout<<"k="<<k<<endl;
cout<<"value="<<it.value()<<endl;
cout<<"it.row="<<it.row()<<endl;
cout<<"it.col="<<it.col()<<endl;
cout<<"index="<<it.index()<<endl;
}
}
}
For the matrix given above the following results are printed:
k=0
value=156148016
it.row=66211520
it.col=0
index=66211520
k=1
value=0
it.row=0
it.col=1
index=0
k=1
value=1
it.row=4
it.col=1
index=4
k=2
value=1
it.row=3
it.col=2
index=3
1.) Any explanation for the values corresponding to k=0? Could these be due to passing the matrix in a wrong manner?
2.) k is iterating over outerSize, which is equal to 5, why is it not iterating for k=3,4? Considering it is a sparseMatrix, this behaviour was expected from the iterator.

Answer 1

Whenever you see very large numbers like 156148016 or 66211520, chances are you either have an undefined behavior (UB) or a value was not appropriately initialized. In this case, it is the later. Specifically, the dgCMatrix class' underlying type is that of a double not an int.
The dgCMatrix class is a class of sparse numeric matrices in the compressed, sparse, column-oriented format. In this implementation the non-zero elements in the columns are sorted into increasing row order. dgCMatrix is the "standard" class for sparse numeric matrices in the Matrix package.
Thus, when you are trying to create a map to the memory location of the underlying RObject there is an additional step required to recreate the object anew in the requested different type. After adding the const term, I'm willing to bet the entries are then as expected since the compiler likely keeps in memory the intermediary object.
So, the changing following:
MappedSparseMatrix<int> spmat(as<MappedSparseMatrix<int> >(sparse_mat));
to:
MappedSparseMatrix<double> spmat(as<MappedSparseMatrix<double> >(sparse_mat));
should be sufficient.
The linked example uses a SparseMatrix matrix, here you are using a MappedSparseMatrix but do not setup an appropriate MappedSparseMatrix::InnerIterator for the second loop.
Thus, we have:
for(SparseMatrix<int,Eigen::ColMajor>::InnerIterator it(spmat,k);it;++it){
Going to:
for(MappedSparseMatrix<double>::InnerIterator it(spmat,k);it;++it){
Also, note that the use of Eigen::ColMajor within the SparseMatrix<int, Eigen::ColMajor>::InnerIterator is not needed as that is the default initialization. So, I've removed this statement.
Regarding your second question, on the iteration of k.
k does iterate over both k=3,4 but there are no elements within those columns. Therefore, the inner loop where k is output does not get called.
This is easy to see if we put two k declarative output statements in the outer and inner loops.
e.g.
for(int k = 0; k < spmat.outerSize(); ++k) {
Rcpp::Rcout << "Overall k = " << k << std::endl << std::endl;
for(MappedSparseMatrix<double>::InnerIterator it(spmat,k); it; ++it) {
Rcpp::Rcout << "Inner k = " << k << std::endl;
}
}
Avoid using namespace std;
Adding in namespace sometimes has unintended consequences, especially one as large as std.
Taking the points from above and slightly simplifying your example, we have the following bare bones working example:
#include <RcppEigen.h>
// [[Rcpp::depends(RcppEigen)]]
using Eigen::MappedSparseMatrix;
using Eigen::SparseMatrix;
using Eigen::VectorXi;
using Eigen::Map;
// [[Rcpp::export]]
void createRec(Rcpp::RObject sparse_mat) {
MappedSparseMatrix<double> spmat(Rcpp::as<MappedSparseMatrix<double> >(sparse_mat));
long int nrow = spmat.rows();
Rcpp::NumericVector sim(nrow);
for(int k = 0; k < spmat.outerSize(); ++k) {
Rcpp::Rcout << "Overall k = " << k << std::endl << std::endl;
for(MappedSparseMatrix<double>::InnerIterator it(spmat,k); it; ++it) {
Rcpp::Rcout << "Inner k = " << k << std::endl
<< "value = " << it.value() << std::endl
<< "it.row = " << it.row() << std::endl
<< "it.col = " << it.col() << std::endl
<< "index = " << it.index() << std::endl;
}
}
}
/***R
# Setup values
id_row = c(2, 2, 4, 5)
id_col = c(1, 2, 3, 2)
vals = rep(1,4)
# Make the matrix
x = sparseMatrix(id_row, id_col, x = vals, dims = c(5, 5))
# Test the function
createRec(x)
*/
Output:
Overall k = 0
Inner k = 0
value = 1
it.row = 1
it.col = 0
index = 1
Overall k = 1
Inner k = 1
value = 1
it.row = 1
it.col = 1
index = 1
Inner k = 1
value = 1
it.row = 4
it.col = 1
index = 4
Overall k = 2
Inner k = 2
value = 1
it.row = 3
it.col = 2
index = 3
Overall k = 3
Overall k = 4
For more details on sparse matrices in Eigen and Rcpp, you may wish to read the Rcpp Gallery: Using iterators for sparse vectors and matrices by Soren Hojsgaard and Doug Bates.