applications_kos
zihao12
2020-04-29
Last updated: 2020-05-01
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Knit directory: ebpmf_demo/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 27dec57 | zihao12 | 2020-05-01 | update app kos |
| html | 23bd3e5 | zihao12 | 2020-04-30 | Build site. |
| Rmd | 70b3375 | zihao12 | 2020-04-30 | update applications_kos |
| html | e29a1ee | zihao12 | 2020-04-30 | Build site. |
| Rmd | 5eba64a | zihao12 | 2020-04-30 | applications_kos.Rmd |
Introduction
- I applied Poisson Matrix Factorization to analyze a corpus of Daily Kos Blog dataset.
- The data is downloaded from Bag of Words. I run
NNLM::nnmffor \(1000\) iterations with \(20\) topics, usingscdalgorithm.
- I mainly look at \(f_{jK}\) of the fitted model.
- Note that, I use \(L \frac{f_{jk}}{\sum_{j} f_{jk}}\) in place of \(f_{jk}\) for better intrepretation. \(L\) is the average document length.
rm(list = ls())
library(Matrix)Warning: package 'Matrix' was built under R version 3.5.2source("code/misc.R")
library(pheatmap)Warning: package 'pheatmap' was built under R version 3.5.2set.seed(123)
data_dir = "~/Desktop/data/text"
data_name = "docword.kos.mtx"
model_name = "docword.kos_nnmf_K20_maxiter1000.Rds"
dict_name = "vocab.kos.txt"
Y = readMM(file= sprintf("%s/%s", data_dir, data_name))
Y = as.matrix(Y)
dict = read.csv(sprintf("%s/%s", data_dir, dict_name), header = FALSE)
dict = as.vector(dict[,1])
model = readRDS(sprintf("%s/%s", data_dir, model_name))
L = model$W
F = t(model$H)
s_k = colSums(L)
dim(L)[1] 3430 20dim(F)[1] 6906 20n = nrow(Y)
p = ncol(Y)
K = ncol(L)
## scale l,f into multinomial model
lf = poisson2multinom(F = F, L = L)scale F
doc_length_med = median(rowSums(Y))
doc_length_med[1] 111F = t(t(F)/colSums(F))
F = doc_length_med * Flook at the meaning of each topic
let’s look at the weight distribution in each topic (multinomial model). It seems that the top \(0.002\) words take up most weight.
par(mfrow = c(5,4))
for(k in 1:K){
probs = seq(0, 1, 0.002)
#top_words = order(lf$F[,1], decreasing = TRUE)[1:13]
plot(probs, quantile(lf$F[,k], probs = probs))
}
Below I show the top \(0.002\) words in each topic (per column)
n_word = round(0.002 * p)
topic_df <- matrix(,nrow = n_word, ncol = K)
for(k in 1:K){
topic_df[,k] = dict[order(lf$F[,k], decreasing = TRUE)[1:n_word]]
}
colnames(topic_df) <- 1:K
topic_df 1 2 3 4 5
[1,] "november" "states" "dean" "bush" "senate"
[2,] "voting" "state" "edwards" "kerry" "race"
[3,] "kerry" "election" "kerry" "president" "house"
[4,] "account" "nader" "clark" "john" "republican"
[5,] "republicans" "vote" "primary" "general" "elections"
[6,] "house" "party" "democratic" "bushs" "democrats"
[7,] "vote" "ballot" "lieberman" "campaign" "seat"
[8,] "senate" "general" "poll" "cheney" "state"
[9,] "electoral" "florida" "gephardt" "debate" "gop"
[10,] "governor" "voters" "iowa" "kerrys" "democratic"
[11,] "poll" "republican" "kucinich" "george" "carson"
[12,] "polls" "votes" "results" "war" "primary"
[13,] "bush" "ohio" "sharpton" "iraq" "democrat"
[14,] "election" "democratic" "numbers" "speech" "south"
6 7 8 9
[1,] "bush" "time" "bush" "delay"
[2,] "administration" "people" "tax" "republicans"
[3,] "intelligence" "ive" "jobs" "house"
[4,] "house" "ill" "health" "committee"
[5,] "white" "meteor" "economy" "court"
[6,] "president" "blades" "administration" "republican"
[7,] "commission" "oct" "billion" "texas"
[8,] "report" "general" "year" "bill"
[9,] "iraq" "youre" "budget" "senate"
[10,] "terrorism" "dkos" "economic" "democrats"
[11,] "cia" "night" "cuts" "law"
[12,] "security" "live" "million" "federal"
[13,] "officials" "heres" "care" "ethics"
[14,] "weapons" "lot" "job" "investigation"
10 11 12 13 14
[1,] "bush" "rights" "november" "campaign" "war"
[2,] "years" "marriage" "poll" "party" "military"
[3,] "oil" "gay" "house" "million" "iraq"
[4,] "administration" "people" "polls" "money" "abu"
[5,] "energy" "amendment" "electoral" "democratic" "rumsfeld"
[6,] "space" "issue" "governor" "ads" "ghraib"
[7,] "science" "women" "account" "democrats" "american"
[8,] "blades" "political" "republicans" "dnc" "general"
[9,] "meteor" "reagan" "senate" "candidates" "people"
[10,] "research" "party" "trouble" "political" "defense"
[11,] "policy" "american" "ground" "bush" "iraqi"
[12,] "environmental" "conservative" "turnout" "national" "soldiers"
[13,] "cell" "president" "contact" "republicans" "pentagon"
[14,] "gas" "vote" "duderino" "election" "prisoners"
15 16 17 18 19
[1,] "iraq" "bush" "bush" "district" "media"
[2,] "war" "service" "poll" "race" "news"
[3,] "iraqi" "kerry" "kerry" "senate" "bloggers"
[4,] "troops" "military" "percent" "house" "internet"
[5,] "american" "bushs" "polls" "elections" "press"
[6,] "forces" "war" "voters" "candidates" "fox"
[7,] "killed" "national" "polling" "campaign" "book"
[8,] "military" "guard" "results" "races" "conservative"
[9,] "baghdad" "vietnam" "general" "candidate" "blogs"
[10,] "soldiers" "draft" "numbers" "dkos" "radio"
[11,] "saddam" "veterans" "lead" "money" "coverage"
[12,] "bush" "records" "race" "republican" "political"
[13,] "country" "texas" "vote" "obama" "blog"
[14,] "attacks" "duty" "undecided" "dozen" "tom"
20
[1,] "dean"
[2,] "iowa"
[3,] "democratic"
[4,] "campaign"
[5,] "primary"
[6,] "endorsement"
[7,] "howard"
[8,] "gephardt"
[9,] "people"
[10,] "deans"
[11,] "unions"
[12,] "union"
[13,] "candidate"
[14,] "edwards" Let’s look at the structure of \(f_{jk}\).
most frequent words
Note that the most frequent words (like “a”, “the”)seem to have been eliminated …
freq_order = order(colSums(Y)) ## increasing order
#idx = sample(x = 1:p, size = 9, replace = FALSE)
idx = freq_order[(p-15):p]
par(mfrow = c(4,4))
for(j in idx){
plot(F[j,], xlab = "topic index", ylab = "f_jk",
main = sprintf("f_jK for word `%s`", dict[j]))
}
rarest words
Also note that words that occurred less than ten times are also eliminated
idx = freq_order[1:16]
par(mfrow = c(4,4))
for(j in idx){
plot(F[j,], xlab = "topic index", ylab = "f_jk",
main = sprintf("f_jK for word `%s`", dict[j]))
}
| Version | Author | Date |
|---|---|---|
| 23bd3e5 | zihao12 | 2020-04-30 |
other words
get some words that are neither most often nor rare
idx = sample(x = 1:p, size = 16, replace = FALSE) ## hope it won't coincide with previous choices
par(mfrow = c(4,4))
for(j in idx){
plot(F[j,], xlab = "topic index", ylab = "f_jk",
main = sprintf("f_jK for word `%s`", dict[j]))
}
| Version | Author | Date |
|---|---|---|
| 23bd3e5 | zihao12 | 2020-04-30 |
most important words in a topic
par(mfrow = c(5,4))
for(k in 1:K){
j = which.max(lf$F[,k])
plot(F[j,], xlab = "topic index", ylab = "f_jk",
main = sprintf("f_jK for `%s` in topic %d", dict[j], k))
}
look at top words using pheatmap
#par(mfrow = c(5,4))
n_top_word = round(0.002 * p)
for(k in 1:K){
print(sprintf("topic %d", k))
word_idx = order(lf$F[,k], decreasing = TRUE)[1:n_top_word]
F_sub = F[word_idx, ]
rownames(F_sub) = dict[word_idx]
colnames(F_sub) = paste("Topic", 1:K, sep = "")
pheatmap(F_sub)
}[1] "topic 1"
[1] "topic 2"
[1] "topic 3"
[1] "topic 4"
[1] "topic 5"
[1] "topic 6"
[1] "topic 7"
[1] "topic 8"
[1] "topic 9"
[1] "topic 10"
[1] "topic 11"
[1] "topic 12"
[1] "topic 13"
[1] "topic 14"
[1] "topic 15"
[1] "topic 16"
[1] "topic 17"
[1] "topic 18"
[1] "topic 19"
[1] "topic 20"
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14
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 Matrix_1.2-17
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 knitr_1.28 whisker_0.3-2 magrittr_1.5
[5] workflowr_1.5.0 munsell_0.5.0 colorspace_1.4-1 lattice_0.20-38
[9] R6_2.4.0 stringr_1.4.0 tools_3.5.1 grid_3.5.1
[13] gtable_0.3.0 xfun_0.8 git2r_0.26.1 htmltools_0.3.6
[17] yaml_2.2.0 digest_0.6.22 rprojroot_1.3-2 RColorBrewer_1.1-2
[21] later_0.8.0 promises_1.0.1 fs_1.3.1 glue_1.3.1
[25] evaluate_0.14 rmarkdown_2.1 stringi_1.4.3 compiler_3.5.1
[29] scales_1.0.0 backports_1.1.5 httpuv_1.5.1