news_ebpmf_wbg_k20
Zihao
2020-09-27
Last updated: 2020-10-07
Checks: 6 1
Knit directory: ebpmf_data_analysis/
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Modified: topicView-app/app.R
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/news_ebpmf_wbg_k20.Rmd) and HTML (docs/news_ebpmf_wbg_k20.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2cee0af | zihao12 | 2020-10-07 | add a plot to ebpmf data analysis |
| html | 881d3d6 | zihao12 | 2020-10-05 | Build site. |
| Rmd | 8fd7d46 | zihao12 | 2020-10-05 | add more analysis on nips and news data |
Introduction
I applied ebpmf_wbg (with \(k = 20\)) to news dataset. let’s see the results.
#setwd("~/Desktop/git/ebpmf_data_analysis/")
library(Matrix)
# Warning: package 'Matrix' was built under R version 3.5.2
library(dplyr)
# Warning: package 'dplyr' was built under R version 3.5.2
library(fastTopics)
library(ggplot2)
# Warning: package 'ggplot2' was built under R version 3.5.2
library(cowplot)
# Warning: package 'cowplot' was built under R version 3.5.2
library(CountClust)
# Warning: package 'CountClust' was built under R version 3.5.2
source("code/plots.R")
source("code/util.R")
source("code/misc.R")
# Warning: package 'pheatmap' was built under R version 3.5.2
set.seed(123)
version = "v0.4.5"
method_name = "ebpmf_wbg"
data_name = "news"
dir_name = "News"
K = 20
maxiter = 2000load data and fitted model
Note that: \(X_{ij} \sim Pois(\sum_k w_k l_{i0} l_{ik} f_{j0} f_{jk})\). I use \(\text{loading}_{ik} = w_k l_{ik}\) then scale it so that each row sums to 1. There are other ways to get a meaningful loading…
X = read_news_bag_of_words(sprintf("data/%s/docword.%s.txt", dir_name, data_name))
vocab = readLines(sprintf("data/%s/vocab.%s.txt", dir_name, data_name))
labels = readLines(sprintf("data/%s/topics.%s.txt", dir_name, data_name))
## data size
print(dim(X))
## percentage of nonzero elements
print(length(X@x)/(nrow(X) * ncol(X)))
## unique topics (20 of them, same as K used in the model)
print(unique(labels))
# [1] 18774 55911
# [1] 0.002314951
# [1] "alt.atheism" "comp.graphics"
# [3] "comp.os.ms-windows.misc" "comp.sys.ibm.pc.hardware"
# [5] "comp.sys.mac.hardware" "comp.windows.x"
# [7] "misc.forsale" "rec.autos"
# [9] "rec.motorcycles" "rec.sport.baseball"
# [11] "rec.sport.hockey" "sci.crypt"
# [13] "sci.electronics" "sci.med"
# [15] "sci.space" "soc.religion.christian"
# [17] "talk.politics.guns" "talk.politics.mideast"
# [19] "talk.politics.misc" "talk.religion.misc"model = readRDS(sprintf("output/%s/%s/%s_%s_initLF50_K%d_maxiter%d.Rds", dir_name, version, data_name, method_name, K, maxiter))
L_scaled = model$qg$qls_mean %*% diag(model$w)
L_scaled = L_scaled/rowSums(L_scaled)
fit = list(L = L_scaled)Meaning of topics:
See topicView (more info to add)
Visualization of \(L\)
Hierarchical Clustering
## use code from https://stephenslab.github.io/count-clustering/project/src/gtex_v6_structure_genes.html
rows <- c()
for(label in unique(labels)){
rows <- c(rows, sample(which(labels == label), size = 50))
}
omega = fit$L[rows, ]
colnames(omega) <- c(1:NCOL(omega))
rownames(omega) <- c(1:NROW(omega))
#find sample orders in hierarchical clustering
docweights_per_tissue_mean <- apply(omega, 2,
function(x) { tapply(x, labels[rows], mean) })
ordering <- heatmap(docweights_per_tissue_mean)$rowInd
| Version | Author | Date |
|---|---|---|
| 881d3d6 | zihao12 | 2020-10-05 |
Structure Plot
levels_reordered <- unique(labels)[ordering]
annotation <- data.frame(
sample_id = paste0("X", 1:length(labels[rows])),
tissue_label = factor(labels[rows], levels = rev(levels_reordered)))
StructureGGplot(omega = omega, annotation = annotation,
palette = c(RColorBrewer::brewer.pal(12, "Paired"), RColorBrewer::brewer.pal(8, "Accent")),
yaxis_label = "",
order_sample = TRUE,
split_line = list(split_lwd = .1,
split_col = "white"),
axis_tick = list(axis_ticks_length = .1,
axis_ticks_lwd_y = .5,
axis_ticks_lwd_x = .5,
axis_label_size = 5,
axis_label_face="bold"))
| Version | Author | Date |
|---|---|---|
| 881d3d6 | zihao12 | 2020-10-05 |
t-SNE plots
p12 <- my_tsne_plot(fit,k = 1:K,num_threads = 8,verbose = FALSE)
print(p12)
| Version | Author | Date |
|---|---|---|
| 881d3d6 | zihao12 | 2020-10-05 |
PCA plots
lams = svd(x = L_scaled)$d
plot(1:length(lams), lams/sum(lams), xlab = "eigen idx", ylab = "prop of variance explained")
| Version | Author | Date |
|---|---|---|
| 881d3d6 | zihao12 | 2020-10-05 |
PCA plots (Show the density of the points by “hexbin” plots)
bins <- c(0,1,5,10,100,Inf)
p4 <- pca_hexbin_plot(fit,1:2,bins = bins) + guides(fill = "none")
p5 <- pca_hexbin_plot(fit,3:4,bins = bins) + guides(fill = "none")
p6 <- pca_hexbin_plot(fit,5:6,bins = bins) + guides(fill = "none")
plot_grid(p4,p5,p6,nrow = 1,ncol = 3)
| Version | Author | Date |
|---|---|---|
| 881d3d6 | zihao12 | 2020-10-05 |
Show where each topic lies in PC1-PC2 space
p9 <- my_pca_plot(fit,pcs = 1:2,k = 1:K)
print(p9)
| Version | Author | Date |
|---|---|---|
| 881d3d6 | zihao12 | 2020-10-05 |
about g
\(l_{ik} \sim \pi_{kl} Ga(1/\phi_{kl}, 1/\phi_{kl})\). Note \(Var(X) = \phi, X \sim Ga(1/\phi, 1/\phi)\), and for very big \(\phi\), the mass is almost all on 0.
get_prior_summary(model$qg$gls)
get_prior_summary(model$qg$gfs)
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] pheatmap_1.0.12 Rtsne_0.15 CountClust_1.10.1 cowplot_0.9.4
# [5] ggplot2_3.3.0 fastTopics_0.3-177 dplyr_0.8.1 Matrix_1.2-17
# [9] workflowr_1.6.2
#
# loaded via a namespace (and not attached):
# [1] maptpx_1.9-5 nlme_3.1-140 mcmc_0.9-5 fs_1.3.1
# [5] RColorBrewer_1.1-2 progress_1.2.2 httr_1.4.1 rprojroot_1.3-2
# [9] tools_3.5.1 backports_1.1.7 R6_2.4.1 irlba_2.3.3
# [13] vegan_2.5-5 lazyeval_0.2.2 mgcv_1.8-28 colorspace_1.4-1
# [17] permute_0.9-5 nnet_7.3-12 withr_2.2.0 tidyselect_0.2.5
# [21] prettyunits_1.1.1 compiler_3.5.1 git2r_0.26.1 quantreg_5.40
# [25] SparseM_1.77 plotly_4.9.2.1 labeling_0.3 slam_0.1-45
# [29] scales_1.1.1 hexbin_1.28.1 SQUAREM_2017.10-1 quadprog_1.5-8
# [33] stringr_1.4.0 digest_0.6.25 rmarkdown_2.1 MCMCpack_1.4-3
# [37] pkgconfig_2.0.3 htmltools_0.5.0 limma_3.38.3 highr_0.8
# [41] htmlwidgets_1.3 rlang_0.4.6 farver_2.0.3 jsonlite_1.6.1
# [45] gtools_3.8.2 magrittr_1.5 modeltools_0.2-22 Rcpp_1.0.5
# [49] munsell_0.5.0 ape_5.3 lifecycle_0.2.0 stringi_1.4.3
# [53] whisker_0.3-2 yaml_2.2.0 MASS_7.3-51.4 flexmix_2.3-15
# [57] plyr_1.8.4 grid_3.5.1 parallel_3.5.1 promises_1.1.1
# [61] ggrepel_0.8.2 crayon_1.3.4 lattice_0.20-38 splines_3.5.1
# [65] hms_0.4.2 knitr_1.28 pillar_1.4.4 boot_1.3-22
# [69] reshape2_1.4.3 stats4_3.5.1 picante_1.8.2 glue_1.4.1
# [73] evaluate_0.14 data.table_1.12.2 RcppParallel_4.4.3 vctrs_0.3.0
# [77] httpuv_1.5.4 MatrixModels_0.4-1 gtable_0.3.0 purrr_0.3.4
# [81] tidyr_0.8.3 assertthat_0.2.1 xfun_0.8 coda_0.19-3
# [85] later_1.1.0.1 viridisLite_0.3.0 tibble_3.0.1 cluster_2.1.0
# [89] ellipsis_0.3.1