Last updated: 2024-08-21

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Knit directory: fastTopics-experiments/analysis/

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Rmd a482257 Peter Carbonetto 2024-08-21 workflowr::wflow_publish("assess_fits_nips.Rmd", verbose = TRUE)
html 70d8b96 Peter Carbonetto 2024-07-29 Added ELBO plots to the assess_fits_nips analysis.
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html 42d2b01 Peter Carbonetto 2021-04-09 Added topic proportions scatterplot, for K=10, to assess_fits_nips
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html b2fe08f Peter Carbonetto 2021-04-07 Fixed a couple of the progress plots in the assess_fits_nips analysis.
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html 269a77b Peter Carbonetto 2021-04-07 Improved progress plots in assess_fits_nips analysis.
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Here we compare the quality of the fits obtained from the different updates (EM and SCD, with and without extrapolation), and with different numbers of topics, \(K\).

Load the packages used in the analysis below, as well as some additional functions for creating the plots.

library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/plot_functions.R")

Load the results of running fit_poisson_nmf on the NIPS data, with different algorithms, and for various settings of \(K\).

load("../output/nips/fits-nips.RData")
fits <- lapply(fits,poisson2multinom)

This plot shows the improvement in the log-likelihood as the rank, \(K\), is increased. The log-likelihoods are shown relative to the log-likelihood at \(K = 2\).

plot_loglik_vs_rank(fits) +
  theme_cowplot(font_size = 12)

Version Author Date
70d8b96 Peter Carbonetto 2024-07-29
269a77b Peter Carbonetto 2021-04-07

The next set of plots shows the improvement in the fit over time, for \(K\) from 2 to 12, using EM or SCD, with and without extrapolation. The quality of the fit is measured by the log-likelihood relative to the best log-likelihood that was identified among all methods compared.

prune_prefit_iters <- function (fit) {
  n <- nrow(fit$progress)
  fit$progress <- fit$progress[1000:n,]
  return(fit)
}
create_progress_plot <- function (fits, k, y = "loglik")
  plot_progress(fits,y = y,add.point.every = 100,shapes = 21,
                colors = c("dodgerblue","red","dodgerblue","red"),
                fills = c("dodgerblue","red","white","white")) +
  scale_y_continuous(trans = "log10",breaks = 10^seq(-8,8)) +
  guides(color = "none",fill = "none",size = "none",
         shape = "none",linetype = "none") +
  labs(x = "runtime (s)",title = paste("K =",k)) +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
fits <- lapply(fits,prune_prefit_iters)
p <- vector("list",12)
for (i in 2:12)
  p[[i]] <- create_progress_plot(fits[dat$k == i],i)
p[[2]] <- p[[2]] + scale_y_continuous()
p[[3]] <- p[[3]] + scale_y_continuous()
plot_grid(p[[2]],p[[3]],p[[4]],p[[5]],
          p[[6]],p[[7]],p[[8]],p[[9]],
          p[[10]],p[[11]],p[[12]],
          nrow = 3,ncol = 4)

Version Author Date
70d8b96 Peter Carbonetto 2024-07-29
b2fe08f Peter Carbonetto 2021-04-07
269a77b Peter Carbonetto 2021-04-07

These plots shows the evolution of the KKT residuals over time.

for (i in 2:12)
  p[[i]] <- create_progress_plot(fits[dat$k == i],i,y = "res")
plot_grid(p[[2]],p[[3]],p[[4]],p[[5]],
          p[[6]],p[[7]],p[[8]],p[[9]],
          p[[10]],p[[11]],p[[12]],
          nrow = 3,ncol = 4)

Version Author Date
70d8b96 Peter Carbonetto 2024-07-29
b2fe08f Peter Carbonetto 2021-04-07
269a77b Peter Carbonetto 2021-04-07

For the most part, the EM and CD algorithms achieve similar estimates in this data set. For example, for \(K = 10\), the difference in the topic model likelihoods between the EM and CD estimates is very small, and indeed the estimated topic proportions are nearly identical:

fit1 <- fits[["fit-nips-em-k=10"]]
fit2 <- fits[["fit-nips-scd-k=10"]]
pdat <- data.frame(x = as.vector(fit1$L),y = as.vector(fit2$L))
p1 <- ggplot(pdat,aes(x = x,y = y)) +
  geom_point(shape = 21,size = 2,color = "white",fill = "royalblue") +
  geom_abline(color = "black",linetype = "dotted") +
  labs(x = "EM estimate",y = "CD estimate") +
  theme_cowplot(font_size = 12)
print(p1)

Version Author Date
70d8b96 Peter Carbonetto 2024-07-29
7149b09 Peter Carbonetto 2021-04-09
07a8f8b Peter Carbonetto 2021-04-09

Finally, let’s have a look at the results of running LDA with the various initializations:

load("../output/nips/lda-nips.RData")
p <- vector("list",12)
runs   <- which(dat$k == 2)
p[[1]] <- create_elbo_plot(fits[runs],dat[runs,"runtime"],2)
for (i in 2:12) {
  runs   <- which(dat$k == i)
  p[[i]] <- create_elbo_plot(fits[runs],dat[runs,"runtime"],i) +
    guides(color = "none",linetype = "none")
}
plot_grid(p[[2]],p[[3]],p[[4]],p[[5]],
          p[[6]],p[[7]],p[[8]],p[[9]],
          p[[10]],p[[11]],p[[12]],p[[1]],
          nrow = 3,ncol = 4)

Version Author Date
70d8b96 Peter Carbonetto 2024-07-29

sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS Sonoma 14.5
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
# LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# time zone: America/Chicago
# tzcode source: internal
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_1.1.3      ggplot2_3.5.0      fastTopics_0.6-184
# 
# loaded via a namespace (and not attached):
#  [1] gtable_0.3.4        xfun_0.42           bslib_0.6.1        
#  [4] htmlwidgets_1.6.4   ggrepel_0.9.5       lattice_0.22-5     
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# [10] generics_0.1.3      parallel_4.3.3      tibble_3.2.1       
# [13] fansi_1.0.6         highr_0.10          pkgconfig_2.0.3    
# [16] Matrix_1.6-5        data.table_1.15.2   SQUAREM_2021.1     
# [19] RcppParallel_5.1.7  lifecycle_1.0.4     truncnorm_1.0-9    
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# [25] git2r_0.33.0        textshaping_0.3.7   progress_1.2.3     
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# [43] cachem_1.0.8        gtools_3.9.5        tidyselect_1.2.1   
# [46] digest_0.6.34       Rtsne_0.17          stringi_1.8.3      
# [49] dplyr_1.1.4         purrr_1.0.2         ashr_2.2-66        
# [52] labeling_0.4.3      rprojroot_2.0.4     fastmap_1.1.1      
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# [61] withr_3.0.0         prettyunits_1.2.0   scales_1.3.0       
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# [79] jsonlite_1.8.8      R6_2.5.1            systemfonts_1.0.6  
# [82] fs_1.6.3