Assess topic model fits in newsgroups data
Peter Carbonetto
Last updated: 2021-04-10
Checks: 6 1
Knit directory: fastTopics-experiments/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | b6c0c71 | Peter Carbonetto | 2021-04-09 | Another small adjustment the assess_fits_newsgroups scatterplots. |
| Rmd | 0ab6c04 | Peter Carbonetto | 2021-04-09 | workflowr::wflow_publish(“assess_fits_newsgroups.Rmd”) |
| html | 42cb17e | Peter Carbonetto | 2021-04-09 | Adjusted scatterplots in assess_fits_newsgroups analysis. |
| Rmd | 8f157b4 | Peter Carbonetto | 2021-04-09 | workflowr::wflow_publish(“assess_fits_newsgroups.Rmd”) |
| html | d1dadf8 | Peter Carbonetto | 2021-04-09 | Added loadings scatterplots to aassess_fits_newsgroups analysis. |
| Rmd | b37146b | Peter Carbonetto | 2021-04-09 | workflowr::wflow_publish(“assess_fits_newsgroups.Rmd”) |
| html | c679bdc | Peter Carbonetto | 2021-04-07 | Small fix to assess_fits_newsgroups output. |
| Rmd | db4870b | Peter Carbonetto | 2021-04-07 | workflowr::wflow_publish(“assess_fits_newsgroups.Rmd”) |
| html | ca4f229 | Peter Carbonetto | 2021-04-07 | Added max KKT residual progress plots to assess_fits_newsgroups |
| Rmd | b916251 | Peter Carbonetto | 2021-04-07 | workflowr::wflow_publish(“assess_fits_newsgroups.Rmd”) |
| Rmd | 7827775 | Peter Carbonetto | 2021-04-06 | Added plots-for-paper-1 code chunk to assess_fits_newsgroups.Rmd. |
| Rmd | 0f88216 | Peter Carbonetto | 2021-04-06 | A couple of small changes. |
| html | cb0de01 | Peter Carbonetto | 2021-04-06 | Added loglik progress plots to assess_fits_newsgroups analysis. |
| Rmd | 720e225 | Peter Carbonetto | 2021-04-06 | workflowr::wflow_publish(“assess_fits_newsgroups.Rmd”) |
| html | 51a6dd3 | Peter Carbonetto | 2021-04-06 | First build of the assess_fits_newsgroups analysis. |
| Rmd | b57553c | Peter Carbonetto | 2021-04-06 | workflowr::wflow_publish(“assess_fits_newsgroups.Rmd”) |
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(Matrix)
library(fastTopics)
library(ggplot2)
library(cowplot)
set.seed(1)Load the newsgroups data, and theresults of running fit_poisson_nmf on the “20 newsgroups” data, with different algorithms, and for various settings of \(K\).
load("../data/newsgroups.RData")
load("../output/newsgroups/fits-newsgroups.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)
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$progress[1000:n,]
fit$progress <- transform(fit$progress,timing = timing/60^2)
return(fit)
}
create_progress_plot <- function (fits, k, y = "loglik")
plot_progress(fits,y = y,add.point.every = 100,shapes = 21,
colors = c("dodgerblue","red","dodgerblue","red"),
fills = c("dodgerblue","red","white","white")) +
scale_y_continuous(trans = "log10",breaks = 10^seq(-8,8)) +
guides(color = "none",fill = "none",size = "none",
shape = "none",linetype = "none") +
labs(x = "runtime (h)",title = paste("K =",k)) +
theme_cowplot(font_size = 10) +
theme(plot.title = element_text(size = 10,face = "plain"))
fits <- lapply(fits,prune_prefit_iters)
p <- vector("list",12)
for (i in 2:12)
p[[i]] <- create_progress_plot(fits[dat$k == i],i)
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 |
|---|---|---|
| cb0de01 | Peter Carbonetto | 2021-04-06 |
These plots shows the evolution of the KKT residuals over time.
for (i in 2:12)
p[[i]] <- create_progress_plot(fits[dat$k == i],i,y = "res")
plot_grid(p[[2]],p[[3]],p[[4]],p[[5]],
p[[6]],p[[7]],p[[8]],p[[9]],
p[[10]],p[[11]],p[[12]],
nrow = 3,ncol = 4)
| Version | Author | Date |
|---|---|---|
| ca4f229 | Peter Carbonetto | 2021-04-07 |
An example in which the EM and (extrapolated) CD estimates largely agree:
topic_colors <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99",
"#e31a1c","#fdbf6f","#ff7f00","#cab2d6","#6a3d9a")
fit1 <- fits[["fit-newsgroups-em-k=9"]]
fit2 <- fits[["fit-newsgroups-scd-ex-k=9"]]
n <- nrow(fit1$L)
pdat <- data.frame(x = as.vector(fit1$L),
y = as.vector(fit2$L),
k = factor(rep(1:9,each = n)))
pdat <- pdat[sample(9*n),]
p1 <- ggplot(pdat,aes(x = x,y = y,fill = k)) +
geom_point(color = "white",shape = 21,size = 2) +
geom_abline(color = "black",linetype = "dotted") +
scale_fill_manual(values = topic_colors) +
labs(x = "EM estimate",y = "extrapolated CD estimate") +
theme_cowplot(font_size = 12)
print(p1)
An example in which the EM and (extrapolated) CD estimates greatly differ:
fit1 <- fits[["fit-newsgroups-em-k=10"]]
fit2 <- fits[["fit-newsgroups-scd-ex-k=10"]]
n <- nrow(fit1$L)
pdat <- data.frame(x = as.vector(fit1$L),
y = as.vector(fit2$L),
k = factor(rep(1:10,each = n)))
pdat <- pdat[sample(10*n),]
p2 <- ggplot(pdat,aes(x = x,y = y,fill = k)) +
geom_point(color = "white",shape = 21,size = 2) +
geom_abline(color = "black",linetype = "dotted") +
scale_fill_manual(values = topic_colors) +
labs(x = "EM estimate",y = "extrapolated CD estimate") +
theme_cowplot(font_size = 12)
print(p2)
Add text here.
set.seed(1)
topics <- factor(topics,
c("sci.med","rec.autos","rec.motorcycles","alt.atheism",
"soc.religion.christian","talk.religion.misc",
"rec.sport.baseball","rec.sport.hockey",
"talk.politics.mideast","talk.politics.guns","sci.crypt",
"talk.politics.misc","sci.space","sci.electronics",
"misc.forsale","comp.sys.ibm.pc.hardware",
"comp.sys.mac.hardware","comp.os.ms-windows.misc",
"comp.graphics","comp.windows.x"))
topic_colors <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99",
"#e31a1c","#fdbf6f","#ff7f00","#cab2d6","#6a3d9a")
fit <- fits[["fit-newsgroups-scd-ex-k=10"]]
p3 <- structure_plot(fit,grouping = topics,topics = 10:1,
colors = topic_colors[10:1],perplexity = 20,gap = 20,
verbose = FALSE)
print(p3)
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.7
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] cowplot_1.0.0 ggplot2_3.3.0 fastTopics_0.5-24 Matrix_1.2-18
#
# loaded via a namespace (and not attached):
# [1] ggrepel_0.9.0 Rcpp_1.0.5 invgamma_1.1
# [4] lattice_0.20-38 tidyr_1.0.0 prettyunits_1.1.1
# [7] assertthat_0.2.1 zeallot_0.1.0 rprojroot_1.3-2
# [10] digest_0.6.23 truncnorm_1.0-8 R6_2.4.1
# [13] backports_1.1.5 MatrixModels_0.4-1 evaluate_0.14
# [16] coda_0.19-3 httr_1.4.2 pillar_1.4.3
# [19] progress_1.2.2 rlang_0.4.5 lazyeval_0.2.2
# [22] data.table_1.12.8 irlba_2.3.3 SparseM_1.78
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# [28] Rtsne_0.15 stringr_1.4.0 htmlwidgets_1.5.1
# [31] munsell_0.5.0 mixsqp_0.3-44 compiler_3.6.2
# [34] httpuv_1.5.2 xfun_0.11 pkgconfig_2.0.3
# [37] SQUAREM_2017.10-1 mcmc_0.9-6 htmltools_0.4.0
# [40] tidyselect_0.2.5 tibble_2.1.3 workflowr_1.6.2.9000
# [43] quadprog_1.5-8 viridisLite_0.3.0 withr_2.1.2
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# [49] MASS_7.3-51.4 grid_3.6.2 jsonlite_1.6
# [52] gtable_0.3.0 lifecycle_0.1.0 git2r_0.26.1
# [55] magrittr_1.5 scales_1.1.0 RcppParallel_4.4.2
# [58] stringi_1.4.3 farver_2.0.1 fs_1.3.1
# [61] promises_1.1.0 vctrs_0.2.1 tools_3.6.2
# [64] glue_1.3.1 purrr_0.3.3 hms_0.5.2
# [67] yaml_2.2.0 colorspace_1.4-1 ashr_2.2-51
# [70] plotly_4.9.2 knitr_1.26 quantreg_5.54
# [73] MCMCpack_1.4-5