topicmodels.etm (version 0.1.1)

summary.ETM: Project ETM embeddings using UMAP

Description

Uses the uwot package to map the word embeddings and the center of the topic embeddings to a 2-dimensional space

Usage

# S3 method for ETM
summary(object, type = c("umap"), n_components = 2, top_n = 20, ...)

Value

a list with elements

• center: a matrix with the embeddings of the topic centers

• words: a matrix with the embeddings of the words

• embed_2d: a data.frame which contains a lower dimensional presentation in 2D of the topics and the top_n words associated with the topic, containing columns type, term, cluster (the topic number), rank, beta, x, y, weight; where type is either 'words' or 'centers', x/y contain the lower dimensional positions in 2D of the word and weight is the emitted beta scaled to the highest beta within a topic where the topic center always gets weight 0.8

Arguments

object

object of class ETM

type

character string with the type of summary to extract. Defaults to 'umap', no other summary information currently implemented.

n_components

the dimension of the space to embed into. Passed on to umap. Defaults to 2.

top_n

passed on to predict.ETM to get the top_n most relevant words for each topic in the 2-dimensional space

...

further arguments passed onto umap

See Also

umap, ETM

Examples

if(require(torch) && torch::torch_is_installed() && require(uwot))
{

library(torch)
library(topicmodels.etm)
library(uwot)
path     <- system.file(package = "topicmodels.etm", "example", "example_etm.ckpt")
model    <- torch_load(path)
n_epochs <- NULL
# \dontshow{
  n_epochs <- 5
# }
overview <- summary(model, 
                    metric = "cosine", n_neighbors = 15, n_epochs = n_epochs,
                    fast_sgd = FALSE, n_threads = 1, verbose = TRUE) 
overview$center
overview$embed_2d
# \dontshow{
}
# End of main if statement running only if the torch is properly installed
# }