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(a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/site_libs/navigation-1.1/codefolding-lua.css b/docs/site_libs/navigation-1.1/codefolding-lua.css new file mode 100644 index 0000000..183b19e --- /dev/null +++ b/docs/site_libs/navigation-1.1/codefolding-lua.css @@ -0,0 +1,9 @@ +detaiks.chunk-details > summary.chunk-summary { + text-align: right; +} +details.chunk-details[open] > summary.chunk-summary::after { + content: "Hide"; +} +details.chunk-details[open] > summary.chunk-summary > span.chunk-summary-text { + display: none; +} diff --git a/docs/site_libs/navigation-1.1/codefolding.js b/docs/site_libs/navigation-1.1/codefolding.js index 8a0d36f..66e09df 100644 --- a/docs/site_libs/navigation-1.1/codefolding.js +++ b/docs/site_libs/navigation-1.1/codefolding.js @@ -19,6 +19,8 @@ window.initializeCodeFolding = function(show) { // select all R code blocks var rCodeBlocks = $('pre.r, pre.python, pre.bash, pre.sql, pre.cpp, pre.stan, pre.julia, pre.foldable'); rCodeBlocks.each(function() { + // skip if the block has fold-none class + if ($(this).hasClass('fold-none')) return; // create a collapsable div to wrap the code in var div = $('
    '); @@ -29,12 +31,14 @@ window.initializeCodeFolding = function(show) { $(this).detach().appendTo(div); // add a show code button right above - var showCodeText = $('' + (showThis ? 'Hide' : 'Code') + ''); + var showCodeText = $('' + (showThis ? 'Hide' : 'Show') + ''); var showCodeButton = $(''); showCodeButton.append(showCodeText); showCodeButton .attr('data-toggle', 'collapse') + .attr('data-bs-toggle', 'collapse') // BS5 .attr('data-target', '#' + id) + .attr('data-bs-target', '#' + id) // BS5 .attr('aria-expanded', showThis) .attr('aria-controls', id); @@ -53,7 +57,7 @@ window.initializeCodeFolding = function(show) { // * Change text // * add a class for intermediate states styling div.on('hide.bs.collapse', function () { - showCodeText.text('Code'); + showCodeText.text('Show'); showCodeButton.addClass('btn-collapsing'); }); div.on('hidden.bs.collapse', function () { diff --git a/docs/smallsim_hard.html b/docs/smallsim_hard.html new file mode 100644 index 0000000..ee9d11f --- /dev/null +++ b/docs/smallsim_hard.html @@ -0,0 +1,825 @@ + + + + + + + + + + + + + + +Toy example comparing EM vs. SCD + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + + + + + + + +

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    +Last updated: 2024-06-23 +

    +

    +Checks: 7 + 0 +

    +

    +Knit directory: +fastTopics-experiments/analysis/ +

    +

    +This reproducible R Markdown +analysis was created with workflowr (version +1.7.1). The Checks tab describes the reproducibility checks +that were applied when the results were created. The Past +versions tab lists the development history. +

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    +Rmd + +4e9dfbe + +Peter Carbonetto + +2024-06-23 + +I’ve arrived at a good ‘smallsim’ example. +
    +Rmd + +8a8630e + +Peter Carbonetto + +2024-06-23 + +Working on new smallsim_hard example. +
    +Rmd + +0bb4102 + +Peter Carbonetto + +2024-06-22 + +Made some improvements to the smallsim_hard example. +
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    Here we perform a small experiment with simulated data to illustrate +the behaviour of the EM and SCD algorithms for fitting Poisson NMF (and +topic models). The basic variational EM algorithm for LDA also struggles +with this data set.

    +

    Load the packages used in the analysis below, as well as some +additional functions used to simulate the data and generate the +results.

    +
    library(tm)
    +library(topicmodels)
    +library(fastTopics)
    +library(mvtnorm)
    +library(ggplot2)
    +library(cowplot)
    +source("../code/smallsim_functions.R")
    +

    Simulate a \(100 \times 400\) counts +matrix from a multinomial topic model with \(K += 6\) topics.

    +
    set.seed(4)
    +n <- 100
    +m <- 400
    +k <- 6
    +F <- simulate_factors(m,k)
    +out <- simulate_loadings(n,k)
    +L <- out$L
    +major_topic <- out$major_topic
    +s <- simulate_sizes(n)
    +X <- simulate_multinom_counts(L,F,s)
    +cols <- which(colSums(X > 0) > 0)
    +F <- F[cols,]
    +X <- X[,cols]
    +

    We fit the multinomial topic model by performing (i) 180 EM updates +(without extrapolation), or (ii) 180 SCD updates (with extrapolation). +Both of the fits are initialized by running 20 EM updates.

    +
    control <- list(extrapolate = FALSE,numiter = 4)
    +fit0 <- fit_poisson_nmf(X,k,numiter=20,method="em",control=control)
    +fit1 <- fit_poisson_nmf(X,fit0=fit0,numiter=180,method="em",control=control)
    +fit2 <- fit_poisson_nmf(X,fit0=fit0,numiter=80,method="scd",control=control)
    +control$extrapolate <- TRUE
    +fit2 <- fit_poisson_nmf(X,fit0=fit2,numiter=100,method="scd",control=control)
    +

    EM and SCD produce quite different estimates, and among the two, the +SCD estimates are much closer to the truth.

    +
    topic_colors <- c("dodgerblue","darkorange","forestgreen","darkblue",
    +                  "gold","skyblue")
    +loadings_order <- order(major_topic,L[,1])
    +k_set <- c(1,3,5,2,4,6)
    +p1 <- simdata_structure_plot(L,loadings_order,topic_colors,title = "true")
    +p2 <- simdata_structure_plot(poisson2multinom(fit1)$L,loadings_order,
    +                             topic_colors[k_set],title = "EM")
    +p3 <- simdata_structure_plot(poisson2multinom(fit2)$L,loadings_order,
    +                             topic_colors[k_set],title = "SCD")
    +plot_grid(p1,p2,p3,nrow = 3,ncol = 1)
    +

    +

    Indeed, the SCD estimates also improve upon the EM estimates in terms +of log-likelihood, with a total improvement of 600 log-likelihood +units,

    +
    c("em" = sum(loglik_multinom_topic_model(X,fit1)),
    +  "scd" = sum(loglik_multinom_topic_model(X,fit2)))
    +#        em       scd 
    +# -31465.61 -30874.44
    +

    or an average of 6 log-likelihood units per document,

    +
    c("em" = sum(loglik_multinom_topic_model(X,fit1)),
    +  "scd" = sum(loglik_multinom_topic_model(X,fit2)))/n
    +#        em       scd 
    +# -314.6561 -308.7444
    +

    What is reassuring is that if we continue to perform the EM updates, +we eventually arrive at the same solution as SCD. But SCD is able to +“rescue” the EM estimates much more quickly after performing just a few +SCD updates.

    +
    control$extrapolate <- FALSE
    +fit3 <- fit_poisson_nmf(X,fit0=fit1,numiter=600,method="em",control=control)
    +control$extrapolate <- TRUE
    +fit2 <- fit_poisson_nmf(X,fit0=fit2,numiter=600,method="scd",control=control)
    +fit4 <- fit_poisson_nmf(X,fit0=fit1,numiter=600,method="scd",control=control)
    +fit1 <- poisson2multinom(fit1)
    +fit2 <- poisson2multinom(fit2)
    +fit3 <- poisson2multinom(fit3)
    +fit4 <- poisson2multinom(fit4)
    +# loadings_scatterplot(F[,k_set],fit1$F,topic_colors,"true","em")
    +# loadings_scatterplot(F[,k_set],fit2$F,topic_colors,"true","scd")
    +pdat <- rbind(data.frame(iter   = 1:800,
    +                         ll     = fit2$progress$loglik.multinom,
    +                         method = "scd"),
    +              data.frame(iter   = 1:800,
    +                         ll     = fit3$progress$loglik.multinom,
    +                         method = "em"),
    +              data.frame(iter   = 1:800,
    +                         ll     = fit4$progress$loglik.multinom,
    +                         method = "em+scd"))
    +pdat <- transform(pdat,ll = max(ll) - ll + 0.1)
    +ggplot(pdat,aes(x = iter,y = ll,color = method)) +
    +  geom_line(linewidth = 0.75) +
    +  scale_x_continuous(breaks = seq(0,800,100)) +
    +  scale_y_continuous(trans = "log10",breaks = 10^seq(-1,4)) +
    +  scale_color_manual(values = c("dodgerblue","darkorange","magenta")) +
    +  labs(x = "iteration",y = "loglik difference") +
    +  theme_cowplot(font_size = 10)
    +

    +
    +

    + +

    +
    +
    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      mvtnorm_1.2-4      fastTopics_0.6-175
    +# [5] topicmodels_0.2-16 tm_0.7-13          NLP_0.2-1         
    +# 
    +# 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     
    +#  [7] quadprog_1.5-8      vctrs_0.6.5         tools_4.3.3        
    +# [10] generics_0.1.3      stats4_4.3.3        parallel_4.3.3     
    +# [13] tibble_3.2.1        fansi_1.0.6         highr_0.10         
    +# [16] pkgconfig_2.0.3     Matrix_1.6-5        data.table_1.15.2  
    +# [19] SQUAREM_2021.1      RcppParallel_5.1.7  lifecycle_1.0.4    
    +# [22] truncnorm_1.0-9     farver_2.1.1        compiler_4.3.3     
    +# [25] stringr_1.5.1       git2r_0.33.0        progress_1.2.3     
    +# [28] munsell_0.5.0       RhpcBLASctl_0.23-42 httpuv_1.6.14      
    +# [31] htmltools_0.5.7     sass_0.4.8          lazyeval_0.2.2     
    +# [34] yaml_2.3.8          plotly_4.10.4       crayon_1.5.2       
    +# [37] tidyr_1.3.1         later_1.3.2         pillar_1.9.0       
    +# [40] jquerylib_0.1.4     whisker_0.4.1       uwot_0.1.16        
    +# [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] slam_0.1-50         purrr_1.0.2         dplyr_1.1.4        
    +# [52] ashr_2.2-66         labeling_0.4.3      rprojroot_2.0.4    
    +# [55] fastmap_1.1.1       grid_4.3.3          colorspace_2.1-0   
    +# [58] cli_3.6.2           invgamma_1.1        magrittr_2.0.3     
    +# [61] utf8_1.2.4          withr_3.0.0         prettyunits_1.2.0  
    +# [64] scales_1.3.0        promises_1.2.1      httr_1.4.7         
    +# [67] rmarkdown_2.26      workflowr_1.7.1     hms_1.1.3          
    +# [70] modeltools_0.2-23   pbapply_1.7-2       evaluate_0.23      
    +# [73] knitr_1.45          viridisLite_0.4.2   irlba_2.3.5.1      
    +# [76] rlang_1.1.3         Rcpp_1.0.12         mixsqp_0.3-54      
    +# [79] glue_1.7.0          xml2_1.3.6          jsonlite_1.8.8     
    +# [82] R6_2.5.1            fs_1.6.3
    +
    + + + + + + + + + +
    + + + + + + + + + + + + + + +