Efficient way to check a large sparse matrix for non-finite values

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I have a large sparse matrix. After populating the matrix with some math, I realized I had some infinite values due to a division by zero error. How can I check this matrix for non-finite values?

Here is a toy matrix.

A <- Matrix(nrow = 150000, ncol = 150000, data = 0, sparse = TRUE)
A[1, 1] = Inf
A[1, 3] = NA
A[2, 1] = -Inf

Trying to find its non-finite values gives me an error:

test <- A[!is.finite(A)]
#Error: cannot allocate vector of size 83.8 Gb

I also tried scanning this matrix row by row but it takes forever.

library(magrittr)

for(i in 1:nrow(A)){
    if((
        A[i, ] %>% .[!is.finite(.)] %>% length
    ) > 0) print(i)
}

I then tried running it in parallel, but I think it is overkill. What's more, it still takes a long time.

library(parallel)
library(magrittr)

numCores <- detectCores() - 1
cl <- makeCluster(numCores)
clusterExport(cl, c("A"))
clusterEvalQ(cl, library(magrittr))
out <- A %>% nrow %>% seq %>% parLapply(cl, X = ., function(i) A[i, ] %>% .[!is.finite(.)])

How can I proceed?

sparse-matrix r matrix performance
1 Answers

If we want to know if a sparse matrix A has any Inf, -Inf, NaN or NA, we can do

any(!is.finite(A@x))
#[1] TRUE

If we also want to know their positions, we can do

subset(summary(A), !is.finite(x))
  i j    x
1 1 1  Inf
2 2 1 -Inf
3 1 3   NA

Remark:

See R: element-wise matrix division for distinctions between is.infinite, !is.finite, is.na and is.nan.

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