debug_ebpmf_point

Last updated: 2019-10-22

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File	Version	Author	Date	Message
Rmd	7814be6	zihao12	2019-10-22	debug point gamma

I managed to find the bug in ebpmf_point_gamma.Now ELBO increases monotonically.

Also noteworthy is the differences in RMSE compared with https://zihao12.github.io/ebpmf_demo/debug_ebpmf_exponential_mixture.html . Note the data generating process is almost the same. I would further compare the two ebpmf methods.

rm(list  = ls())
devtools::load_all("../ebpmf")

Warning: 1 components of `...` were not used.

We detected these problematic arguments:
* `action`

Did you misspecify an argument?

Loading ebpmf

Warning: package 'testthat' was built under R version 3.5.2

set.seed(123)
library(NNLM)
library(ebpmf)

simulate_data <- function(n, p, K, params, seed = 1234){
  set.seed(seed)
  L = matrix(rgamma(n = n*K, shape = params$al, rate = params$bl), ncol = K)
  F = matrix(rgamma(n = p*K, shape = params$af, rate = params$bf), ncol = K)
  Lam =  L %*% t(F)
  X = matrix(rpois(n*p, Lam), nrow = n)
  Y = matrix(rpois(n*p, Lam), nrow = n)
  return(list(params = params,Lam = Lam,X = X, Y = Y))
}
n = 100
p = 200
K = 3
params = list(al = 100, bl =  109, af = 100, bf = 100, a = 1)
sim = simulate_data(n, p, K, params, seed =  1234)

maxiter = 100

rank 1

mle

## MLE
mle_rank1 = NNLM::nnmf(A = sim$X, k = 1, method = "lee", loss = "mkl", max.iter = 1)
lam_mle_rank1 =  mle_rank1$W %*% mle_rank1$H
ll_train_mle_rank1  = sum(dpois(sim$X, lam_mle_rank1, log = T))
ll_val_mle_rank1  = sum(dpois(sim$Y, lam_mle_rank1, log = T))
rmse_mle_rank1 = sqrt(mean((lam_mle_rank1 - sim$Lam)^2))
data.frame(ll_train = ll_train_mle_rank1,  ll_val = ll_val_mle_rank1, rmse = rmse_mle_rank1)

   ll_train    ll_val      rmse
1 -37606.75 -37849.52 0.1978021

ebpmf

## rank 1 case
out_rank1 =  ebpmf::ebpmf_rank1_point_gamma_helper(X = sim$X, maxiter = 10, verbose = T)

[1] "iter         elbo        kl_l         kl_f       sum_El       sum_Ef"
[1] "  1   1525.9978611673  45.3663647810   68.4682771405   55800.5672468702   1.0002757797"
[1] "  2   1525.9978611679  45.3663647784   68.4682771320   55800.6149360006   1.0002749247"
[1] "  3   1525.9978611699  45.3663647734   68.4682770954   55800.6626321327   1.0002740696"
[1] "  4   1525.9978611658  45.3663647798   68.4682771807   55800.7103325295   1.0002732143"
[1] "  5   1525.9978611681  45.3663647760   68.4682771288   55800.7580488383   1.0002723589"
[1] "  6   1525.9978611788  45.3663647769   68.4682768710   55800.8057672442   1.0002715038"
[1] "  7   1525.9978611697  45.3663647797   68.4682770847   55800.8534679000   1.0002706483"
[1] "  8   1525.9978611703  45.3663647790   68.4682770717   55800.9011965519   1.0002697926"
[1] "  9   1525.9978611726  45.3663647734   68.4682770288   55800.9489323133   1.0002689368"
[1] " 10   1525.9978611725  45.3663647772   68.4682770196   55800.9966705828   1.0002680810"

lam_rank1 =  out_rank1$ql$mean  %*% t(out_rank1$qf$mean)
ll_train_rank1  = sum(dpois(sim$X, lam_rank1, log = T))
ll_val_rank1  = sum(dpois(sim$Y, lam_rank1, log = T))
rmse_rank1 = sqrt(mean((lam_rank1 - sim$Lam)^2))
data.frame(ll_train = ll_train_rank1,  ll_val = ll_val_rank1, rmse = rmse_rank1)

   ll_train    ll_val      rmse
1 -37674.75 -37791.54 0.1434967

rank-k:

out =  ebpmf::ebpmf_point_gamma(X = sim$X, K = K, maxiter.out = 100, verbose = F, fix_g = F)

lam_out =  out$qg$qls_mean  %*% t(out$qg$qfs_mean)
ll_train_out  = sum(dpois(sim$X, lam_out, log = T))
ll_val_out  = sum(dpois(sim$Y, lam_out, log = T))
rmse_out = sqrt(mean((lam_out - sim$Lam)^2))

d = length(out$ELBO)
print(sprintf("ELBO monotonically  increasing? %s", all(out$ELBO[1:(d-1)] < out$ELBO[2:d])))

[1] "ELBO monotonically  increasing? TRUE"

plot(out$ELBO)

data.frame(ll_train = ll_train_out,  ll_val = ll_val_out, rmse = rmse_out)

   ll_train    ll_val      rmse
1 -37714.18 -37832.52 0.1671491

mle

mle_rankK = NNLM::nnmf(A = sim$X, init = list(W0 = out$qg$qls_mean, H0 = t(out$qg$qfs_mean)),k = K, method = "lee", loss = "mkl", rel.tol = 1e-10, max.iter = 100)
lam_mle_rankK =  mle_rankK$W %*% mle_rankK$H
ll_train_mle_rankK  = sum(dpois(sim$X, lam_mle_rankK, log = T))
ll_val_mle_rankK  = sum(dpois(sim$Y, lam_mle_rankK, log = T))
rmse_mle_rankK = sqrt(mean((lam_mle_rankK - sim$Lam)^2))
data.frame(ll_train = ll_train_mle_rankK,  ll_val = ll_val_mle_rankK, rmse = rmse_mle_rankK)

   ll_train    ll_val      rmse
1 -36870.87 -38462.89 0.4485559

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] NNLM_0.4.2     ebpmf_0.1.0    testthat_2.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2          compiler_3.5.1      git2r_0.25.2       
 [4] workflowr_1.4.0     prettyunits_1.0.2   remotes_2.1.0      
 [7] tools_3.5.1         digest_0.6.21       pkgbuild_1.0.3     
[10] pkgload_1.0.2       evaluate_0.14       memoise_1.1.0      
[13] rlang_0.4.0         cli_1.1.0           rstudioapi_0.10    
[16] yaml_2.2.0          xfun_0.8            withr_2.1.2        
[19] stringr_1.4.0       knitr_1.25          gtools_3.8.1       
[22] desc_1.2.0          fs_1.3.1            devtools_2.2.1.9000
[25] rprojroot_1.3-2     glue_1.3.1          R6_2.4.0           
[28] processx_3.3.1      rmarkdown_1.13      sessioninfo_1.1.1  
[31] mixsqp_0.1-121      callr_3.2.0         magrittr_1.5       
[34] whisker_0.3-2       backports_1.1.5     ps_1.3.0           
[37] ellipsis_0.3.0      htmltools_0.3.6     usethis_1.5.1      
[40] assertthat_0.2.1    stringi_1.4.3       ebpm_0.0.0.9001    
[43] crayon_1.3.4

debug_ebpmf_point_gamma

zihao12

2019-10-22

rank 1

mle

ebpmf

rank-k:

mle