status updates and parallel vs single-core implementation

[nick-youngblut]

It would be helpful to have more information than just:

[1] 1
[1] 0

during an mbImpute() run (single core). It appears that this output is simply print(mat_num-1-mat_new) in the data_fit2() function. Using message() with a bit of text along with the value allow for more informative output.

---

While I was looking at data_fit2(), I noticed that you have different code for parallel == TRUE versus parallel == FALSE:

    if(!parallel){
      for(mat_new in 1:(mat_num-1)){
        print(mat_num-1-mat_new)
        design_mat_fit = sparseMatrix(i = 1, j =1, x = 0, dims = c(size, row_length))
        track = ((mat_new-1)*size+1):(mat_new*size)
        for(i in 1:size){
          if(is.vector(X)){
            result <- design_mat_row_gen2(y_sim, X[1:n], confidence_set[track[i]+1,1], confidence_set[track[i]+1,2], close_taxa)
            design_mat_fit[i,result$nz_idx] <- result$nz_val
          }
          else{
            result <- design_mat_row_gen2(y_sim, X[1:n,], confidence_set[track[i]+1,1], confidence_set[track[i]+1,2], close_taxa)
            design_mat_fit[i,result$nz_idx] <- result$nz_val
          }
        }
        mat_list[[mat_new]] = design_mat_fit
      }
    }else{
      no_cores <- max(ncores, detectCores() - 1)
      registerDoParallel(cores=no_cores)
      cl <- makeCluster(no_cores, "FORK")
      f <- function(mat_new){
        design_mat_fit = sparseMatrix(i = 1, j =1, x = 0, dims = c(size, row_length))
        track = ((mat_new-1)*size+1):(mat_new*size)
        for(i in 1:size){
          if(is.vector(X)){
            result <- design_mat_row_gen2(y_sim, X[1:n], confidence_set[track[i]+1,1], confidence_set[track[i]+1,2], close_taxa)
            design_mat_fit[i,result$nz_idx] <- result$nz_val
          }
          else{
            result <- design_mat_row_gen2(y_sim, X[1:n,], confidence_set[track[i]+1,1], confidence_set[track[i]+1,2], close_taxa)
            design_mat_fit[i,result$nz_idx] <- result$nz_val
          }
        }
        return(design_mat_fit)
      }
      mat_list <- parLapply(cl, 1:(mat_num-1), f)

Why is this separate code instead of using the same f() function for cores=1 versus cores=>1? Do these different implementations generate different results?

[ruochenj]

Dear user,

Thank you for your suggestion. I will make the output more interpretable and update the package soon.