model failures about rcppml

[zdebruine]

@topepo Thanks for raising this issue, clearly it's causing you some trouble. Unfortunately, I could not reproduce your example using the dataset as supplied on two different machines (Windows and CentOS). I tried all seeds between 1 and 10000 for k = 5. In no case was all(is.na(res$w)) == TRUE.

Yes, too much regularization can drive a factor in w or h to complete sparsity, and thus numerical instability. That is the expectation.

Here's what my initial w matrix looks like:

> w_init <- nmf(biomass, k = 5, seed = 1, maxit = 0)$w

iter |      tol 
---------------
> w_init
          [,1]       [,2]      [,3]      [,4]      [,5]
[1,] 0.2655087 0.89838968 0.2059746 0.4976992 0.9347052
[2,] 0.3721239 0.94467527 0.1765568 0.7176185 0.2121425
[3,] 0.5728534 0.66079779 0.6870228 0.9919061 0.6516738
[4,] 0.9082078 0.62911404 0.3841037 0.3800352 0.1255551
[5,] 0.2016819 0.06178627 0.7698414 0.7774452 0.2672207

> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

I also tried on a different machine:

> sessionInfo()
R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

> library(RcppML)
> library(modeldata)
> library(Matrix)
> 
> data(biomass)
> 
> biomass <- biomass[, 3:7]
> biomass <- as.matrix(biomass)
> biomass <- t(biomass)
> biomass <- Matrix(biomass, sparse = TRUE)
> 
> res <- nmf(biomass, k = 5, seed = 1, maxit = 5)

iter |      tol 
---------------
   1 | 4.58e-01
   2 | 1.03e-01
   3 | 5.50e-03
   4 | 8.68e-04
   5 | 6.20e-04

 convergence not reached in 5 iterations
  (actual tol = 6.20e-04, target tol = 1.00e-04)
> all(is.na(res$w))
[1] FALSE

[topepo]

I updated to the GH version and was able to reproduce what you have. Thanks!