knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
MMPCA is a package to perform clustering of count data based on the mixture of multinomial PCA model. It integrates a dimension reduction aspect by factorizing the multinomial parameters in a latent space, like Latent Dirichlet Allocation of Blei et. al. It specially conceived for low sample high-dimensional data. Due to the intensive nature of the greedy algorithm, it is not suited for large sample size.
The package contains attached data in BBCmsg. It consists in 4 text document already preprocessed with the tm package. It is mostly useful for the
Start by generating data from the MMPCA model with a particular $\theta^\star$ and $\beta^\star$. For more detail, check experimental section of the paper.
N = 200 L = 250 simu <- simulate_BBC(N, L, epsilon = 0, lambda = 1) Ytruth <- simu$Ytruth
Then perform clustering
t0 <- system.time(res <- mmpca_clust(simu$dtm.full, Q = 6, K = 4, Yinit = 'random', method = 'BBCVEM', max.epochs = 7, keep = 1, verbose = 2, nruns = 2, mc.cores = 2) ) print(t0)
tab <- knitr::kable(table(res@clustering, Ytruth), format = 'markdown') print(tab) cat('Final ARI is ', aricode::ARI(res@clustering, Ytruth))
Other visualization are also accessible from the
plot function. Which takes several arguments
cond = requireNamespace("ggplot2", quietly = TRUE) & requireNamespace("dplyr", quietly = TRUE) & requireNamespace("tidytext", quietly = TRUE)
ggtopics <- plot(res, type = 'topics') print(ggtopics)
ggbound <- plot(res, type = 'bound') print(ggbound)
The package contains a convenient wrapper around
mmpca_clust() which performs model selection over a grid of values for $(K,Q)$. Here is the results for
Qs = 5:7 and
Ks = 3:5.
t1 <- system.time(res <- mmpca_clust_modelselect(simu$dtm.full, Qs = 5:7, Ks = 3:5, Yinit = 'kmeans_lda', init.beta = 'lda', method = 'BBCVEM', max.epochs = 7, nruns = 3, verbose = 1) ) print(t1) best_model = res$models print(best_model)
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