kos_K20_ebpmf.alpha_v0.3.9
zihao12
2020-05-11
Last updated: 2020-05-16
Checks: 7 0
Knit directory: ebpmf_data_analysis/
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| File | Version | Author | Date | Message |
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| Rmd | 3e38a38 | zihao12 | 2020-05-16 | analysis for results in v0.3.9 |
Introduction
- I apply
ebpmf.alpha(version 0.3.9) to KOS dataset. I use \(K = 20\). The data has \(n = 3430,p = 6906\) and sparsity around \(98\) percent.
- Besides, I also apply to
PMF(lee’s, but I implemented a version for sparse data) to the same dataset with the same initialization. In each iteration,ebpmf_bgdoes two things: MLE for prior and updates posterior. The second part has almost the same computation as inPMF.
model
\[\begin{align} & X_{ij} = \sum_k Z_{ijk}\\ & Z_{ijk} \sim Pois(l_{i0} f_{j0} l_{ik} f_{jk})\\ & l_{ik} \sim g_{L, k}(.), f_{jk} \sim g_{F, k}(.) \end{align}\]For details see ebpmf_bg
prior options
I use gamma mixture \(\sum_l \pi_{l} Ga(1/\phi_l, 1/\phi_l)\) as prior for both \(L, F\). Note that each grid component has \(E = 1, Var = \phi_L\)
initialization
I initialized with 50 runs of NNLM::nnmf (scd). Then I used medians of each row of \(L, F\) as \(l_{i0}, f_{j0}\), and \(l_{ik} = l^0_{ik}/l_{i0}, f_{jk} = f^0_{jk}/f_{j0}\).
library(pheatmap)Warning: package 'pheatmap' was built under R version 3.5.2library(gridExtra)
source("code/misc.R")
output_dir = "output/uci_BoW/v0.3.9/"
data_dir = "data/uci_BoW/"
model_name = "kos_ebpmf_bg_initLF50_K20_maxiter5000.Rds"
model_pmf_name = "kos_pmf_initLF50_K20_maxiter5000.Rds"
dict_name = "vocab.kos.txt"
model = readRDS(sprintf("%s/%s", output_dir, model_name))
model_pmf = readRDS(sprintf("%s/%s", output_dir, model_pmf_name))
dict = read.csv(sprintf("%s/%s", data_dir, dict_name), header = FALSE)[,1]
dict = as.vector(dict)ELBO and runtime
plot(model$ELBO, xlab = "niter", ylab = "elbo")
## see when it "converges"
plot(model$ELBO[1:200], xlab = "niter", ylab = "elbo")
## ebpmf_bg runtime per iteration
model$runtime/length(model$ELBO) user system elapsed
9.8347284 0.0247896 9.8627436 ## pmf runtime per iteration
model_pmf$runtime/length(model_pmf$log_liks) user system elapsed
5.0026214 0.0133454 5.0174608 look at priors in ebpmf_bg
\(g_L\)
get_prior_summary(model$qg$gls)
\(g_F\)
get_prior_summary(model$qg$gfs)
Look at quantile of topics
quantile of \(f_{J0}\) (ebpmf_bg)
f = model$f0
probs = seq(0, 1, 0.002)
plot(probs, quantile(f, probs = probs), main = sprintf("topic %d",0))
quantile of \(f_{Jk} (k > 0)\) (ebpmf_bg)
K = length(model$qg$gls)
par(mfrow = c(5,4))
for(k in 1:K){
f = model$qg$qfs_mean[,k]
probs = seq(0, 1, 0.002)
plot(probs, quantile(f, probs = probs), main = sprintf("topic %d",k))
}
quantile of \(f_{J0} f_{Jk}\) (ebpmf_bg) (scaled to multinom)
lf = poisson2multinom(F = model$f0 * model$qg$qfs_mean,
L = model$l0 * model$qg$qls_mean)
K = length(model$qg$gls)
par(mfrow = c(5,4))
for(k in 1:K){
f = lf$F[,k]
probs = seq(0, 1, 0.002)
plot(probs, quantile(f, probs = probs), main = sprintf("topic %d",k))
}
quantile of \(f_{Jk}\) (PMF) (scaled to multinom)
lf_pmf = poisson2multinom(F = model_pmf$F, L = model_pmf$L)
par(mfrow = c(5,4))
for(k in 1:K){
f = lf_pmf$F[,k]
probs = seq(0, 1, 0.002)
plot(probs, quantile(f, probs = probs), main = sprintf("topic %d",k))
}
look at \(s_k\) (ebpmf_bg)
\(s_k := \sum_i l_i0 \bar{l}_{ik}\). I make \(\sum_j f_{j0} = 1\) for interpretability.
d = sum(model$f0)
s_k = colSums(d * model$l0 * model$qg$qls_mean)
names(s_k) <- paste("Topic", 1:K, sep = "")
step = 5
for(i in 1:round(K/step)){
print(round(s_k[((i-1)*step + 1):(i*step)]))
}Topic1 Topic2 Topic3 Topic4 Topic5
6459 32515 19832 48767 26860
Topic6 Topic7 Topic8 Topic9 Topic10
23802 20122 21157 22346 24018
Topic11 Topic12 Topic13 Topic14 Topic15
36992 7804 32226 33707 41961
Topic16 Topic17 Topic18 Topic19 Topic20
24284 31774 25470 41837 31661 look at top words for topics
show_topic <- function(k, other_var){
K = ncol(F)
n_top_word = other_var$n_top_word
F = other_var$F
word_idx = order(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,
cluster_rows=FALSE, cluster_cols=FALSE,
silent = TRUE,
main = sprintf("topic %d", k))[[4]]
}top words in \(\bar{f}_{Jk}\) (ebpmf_bg)
K_sub = 1:K
p = length(model$l0)
n_top_word = round(0.002 * p)
F = model$qg$qfs_mean[, K_sub]
other_var = list(n_top_word = n_top_word,F = F)
gs = lapply(K_sub, FUN = show_topic, other_var = other_var)
grid.arrange(grobs = gs, ncol = 4)
top words in \(f_{J0}\bar{f}_{Jk}\) (ebpmf_bg) (scaled to multinom).
F = lf$F[, K_sub]
other_var = list(n_top_word = n_top_word,F = F)
gs = lapply(K_sub, FUN = show_topic, other_var = other_var)
grid.arrange(grobs = gs, ncol = 4)
top words in \(f_{jk}\) (PMF) (scaled to multinom).
F = lf_pmf$F[, K_sub]
other_var = list(n_top_word = n_top_word,F = F)
gs = lapply(K_sub, FUN = show_topic, other_var = other_var)
grid.arrange(grobs = gs, ncol = 4)
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] gridExtra_2.3 pheatmap_1.0.12
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 knitr_1.28 whisker_0.3-2 magrittr_1.5
[5] workflowr_1.6.2 munsell_0.5.0 colorspace_1.4-1 R6_2.4.0
[9] stringr_1.4.0 tools_3.5.1 grid_3.5.1 gtable_0.3.0
[13] xfun_0.8 git2r_0.26.1 htmltools_0.3.6 yaml_2.2.0
[17] digest_0.6.22 rprojroot_1.3-2 RColorBrewer_1.1-2 later_0.8.0
[21] promises_1.0.1 fs_1.3.1 glue_1.3.1 evaluate_0.14
[25] rmarkdown_2.1 stringi_1.4.3 compiler_3.5.1 scales_1.0.0
[29] backports_1.1.5 httpuv_1.5.1