Last updated: 2020-10-24

Checks: 6 1

Knit directory: ebpmf_data_analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200511)

The command set.seed(20200511) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

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Cache: detected

The following chunks had caches available:
  • unnamed-chunk-3

To ensure reproducibility of the results, delete the cache directory ebpmf_wbg_simulate_big_data2_cache and re-run the analysis. To have workflowr automatically delete the cache directory prior to building the file, set delete_cache = TRUE when running wflow_build() or wflow_publish().

File paths: relative

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Repository version: 1b7e5b0

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The results in this page were generated with repository version 1b7e5b0. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


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    Modified:   topicView-app/app.R

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Rmd 1b7e5b0 zihao12 2020-10-24 simulate big data 2, with bigger signal for top words and docs

Introduction

  • I want to simulate a bigger dataset for ebpmf-wbg, where we know the ground truth.

  • In ebpmf_wbg_simulate_big_data.Rmd the data seems too hard to learn: the ampliying factor for key words/doc is 10, but for freq words/doc is 100. As a result, even starting from truth we get a model that completely ignores those weak signals from top words/doc.

  • This time I will make the amplifying factor the same for freq words/doc and top words/doc.

knitr::opts_chunk$set(message = FALSE, warning = FALSE, autodep = TRUE)
out_dir = "output/sim/v0.4.5/exper2"
set.seed(123)
library(Matrix)
source("code/init.R")
n = 1100
p = 2100
K = 50

r0 = 100
rl = 100
rf = 100
doc_length = 100

n_top_words = 20 ## number of top words per k
n_top_doc = 10 ## number of top documents per k

freq_word = (p-99):p
freq_doc = (n-99):n

p_ = p - length(freq_word)
n_ = n - length(freq_doc)
start_idx_word = seq(0, p_, by = round(p_/K))[1:K] + 1
start_idx_doc = seq(0, n_, by = round(n_/K))[1:K] + 1


f0 = (1/p) * runif(p, min = 0.8, max = 1.2)
l0 = doc_length * (1/n) * runif(n, min = 0.8, max = 1.2)
f0[freq_word] = r0 * f0[freq_word]
l0[freq_doc] = r0 * l0[freq_doc]

F =  matrix(runif(p*K, min = 0.8, max = 1.2), nrow = p)
L =  matrix(runif(n*K, min = 0.8, max = 1.2), nrow = n)

top_words = matrix(, nrow = K, ncol = n_top_words)
top_doc = matrix(, nrow = K, ncol = n_top_doc)

for(k in 1:K){
  idx = start_idx_word[k]:(start_idx_word[k]+n_top_words - 1)
  top_words[k,] = idx
  F[idx,k] = rf*F[idx,k]
  idx = start_idx_doc[k]:(start_idx_doc[k]+n_top_doc - 1)
  top_doc[k,] = idx
  L[idx,k] = rl*L[idx,k]
}

Lam = (l0 * L) %*% t(f0 * F)
X = Matrix(rpois(n = n*p, lambda = Lam), nrow = n, sparse = TRUE)


## sparsity of data matrix X
sum(X!=0)/(n *p)
[1] 0.0544381
## as a comparison, how many counts are in that block?
(K*n_top_doc*n_top_words + length(freq_doc)*length(freq_word))/(n*p)
[1] 0.008658009
## look at the distribution of nonzero X counts
quantile(X@x, seq(0,1,0.1))
  0%  10%  20%  30%  40%  50%  60%  70%  80%  90% 100% 
   1    1    1    1    1    1    1    1    2    3   45

When K is large enough (compared to the amplifying factor), we can expect l0, f0 to be basically okay.

l0_est = rowMeans(X)
f0_est = colMeans(X)

plot(l0_est)

plot(f0_est)

Initialization

fit_init = ebpmf.alpha::pmf(X = X, K = K, init = NULL, maxiter = 50)

Save simulation and initialization

init_ebpmf_wbg = initialize_qgl0f0w_from_LF.local(L = fit_init$L, F = fit_init$F)

writeMM(obj = X,file = sprintf("%s/docword.sim_bg_block_n%d_p%d_K%d.txt", out_dir, n, p, K))
NULL
saveRDS(list(l0 = l0, f0 = f0, 
             L = L, F = F,
             top_words = top_words, top_doc = top_doc), 
        file = sprintf("%s/truth.sim_bg_block_n%d_p%d_K%d.Rds", out_dir, n, p, K))
saveRDS(list(pmf = fit_init,
             ebpmf_wbg = init_ebpmf_wbg), 
        file = sprintf("%s/init.sim_bg_block_n%d_p%d_K%d.Rds", out_dir, n, p, K))

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.15.7

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] ebpmf.alpha_0.4.5 Matrix_1.2-17     workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      knitr_1.28      whisker_0.3-2   magrittr_1.5   
 [5] ebpm_0.0.1.2    lattice_0.20-38 R6_2.4.1        rlang_0.4.6    
 [9] stringr_1.4.0   tools_3.5.1     grid_3.5.1      xfun_0.8       
[13] irlba_2.3.3     git2r_0.26.1    htmltools_0.5.0 yaml_2.2.0     
[17] digest_0.6.25   rprojroot_1.3-2 mixsqp_0.3-46   later_1.1.0.1  
[21] promises_1.1.1  fs_1.3.1        glue_1.4.1      evaluate_0.14  
[25] rmarkdown_2.1   stringi_1.4.3   compiler_3.5.1  backports_1.1.7
[29] httpuv_1.5.4