In news-r/gensimr: Topic Modelling

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
reticulate::use_virtualenv("../env")

Preprocessing

First we preprocess the corpus using example data, a tiny corpus of 9 documents. Reproducing the tutorial on corpora and vector spaces.

library(gensimr)

set.seed(42) # rerproducability

# sample data
data(corpus, package = "gensimr")
print(corpus)

# preprocess corpus
docs <- prepare_documents(corpus)

docs[[1]] # print first preprocessed document 

Once preprocessed we can build a dictionary.

dictionary <- corpora_dictionary(docs)

A dictionary essentially assigns an integer to each term.

doc2bow simply applies the method of the same name to every documents (see example below); it counts the number of occurrences of each distinct word, converts the word to its integer word id and returns the result as a sparse vector.

# native method to a single document
dictionary$doc2bow(docs[[1]])

# apply to all documents
corpus_bow <- doc2bow(dictionary, docs)

Then serialise to matrix market format, the function returns the path to the file (this is saved on disk for efficiency), if no path is passed then a temp file is created. Here we set auto_delete to FALSE otherwise the corpus is deleted after first use. Note this means you should manually delete it with delete_mmcorpus.

(corpus_mm <- serialize_mmcorpus(corpus_bow, auto_delete = FALSE))

Then initialise a model, we're going to use a Latent Similarity Indexing method later on (model_lsi) which requires td-idf.

tfidf <- model_tfidf(corpus_mm)

We can then use the model to transform our original corpus.

corpus_transformed <- wrap(tfidf, corpus_bow)

Finally, we can build models, the number of topics of model_* functions defautls to 2, which is too low for what we generally would do with gensimr but works for the low number of documents we have. Below we reproduce bits and bobs of the topics and transformation.

Latent Similarity Index

Note that we use the transformed corpus.

lsi <- model_lsi(corpus_transformed, id2word = dictionary, num_topics = 2L)
lsi$print_topics()

We can then wrap the model around the corpus to extract further information, below we extract how each document contribute to each dimension (topic).

wrapped_corpus <- wrap(lsi, corpus_transformed)
(wrapped_corpus_docs <- get_docs_topics(wrapped_corpus))
plot(wrapped_corpus_docs$dimension_1_y, wrapped_corpus_docs$dimension_2_y)

wrapped_corpus <- wrap(lsi, corpus_transformed)
(wrapped_corpus_docs <- get_docs_topics(wrapped_corpus))
par(bg = '#f4f1e6')
plot(wrapped_corpus_docs$dimension_1_y, wrapped_corpus_docs$dimension_2_y)

Random Projections

Note that we use the transformed corpus.

rp <- model_rp(corpus_transformed, id2word = dictionary, num_topics = 2L)

wrapped_corpus <- wrap(rp, corpus_transformed)
wrapped_corpus_docs <- get_docs_topics(wrapped_corpus)
plot(wrapped_corpus_docs$dimension_1_y, wrapped_corpus_docs$dimension_2_y)

rp <- model_rp(corpus_transformed, id2word = dictionary, num_topics = 2L)

wrapped_corpus <- wrap(rp, corpus_transformed)
wrapped_corpus_docs <- get_docs_topics(wrapped_corpus)
par(bg = '#f4f1e6')
plot(wrapped_corpus_docs$dimension_1_y, wrapped_corpus_docs$dimension_2_y)

Latent Dirichlet Allocation

Note that we use the original, non-transformed corpus.

lda <- model_lda(corpus_transformed, id2word = dictionary, num_topics = 2L)
lda_topics <- lda$get_document_topics(corpus_bow)
wrapped_corpus_docs <- get_docs_topics(lda_topics)
plot(wrapped_corpus_docs$dimension_1_y, wrapped_corpus_docs$dimension_2_y)

lda <- model_lda(corpus_mm, id2word = dictionary, num_topics = 2L)
lda_topics <- lda$get_document_topics(corpus_bow)
wrapped_corpus_docs <- get_docs_topics(lda_topics)
par(bg = '#f4f1e6')
plot(wrapped_corpus_docs$dimension_1_y, wrapped_corpus_docs$dimension_2_y)

Hierarchical Dirichlet Process

hdp <- model_hdp(corpus_mm, id2word = dictionary)
reticulate::py_to_r(hdp$show_topic(topic_id = 1L, topn = 5L))

Fasttext

ft <- model_fasttext(size = 4L, window = 3L, min_count = 1L)
ft$build_vocab(sentences = unname(docs))
ft$train(sentences = unname(docs), total_examples = length(docs), epochs = 10L)

# most similar
ft$wv$most_similar(positive = c('computer', 'human'), negative = c('interface'))

# odd one out
ft$wv$doesnt_match(c("human", "computer", "interface", "tree"))

# similarity score
ft$wv$similarity('computer', 'human')

Author-topic model

First we build the model.

# authors of corpus
data("authors", package = "gensimr")

auth2doc <- auth2doc(authors, name, document)

# create temp to hold serialized data
temp <- tempfile("serialized")

# build model
atmodel <- model_at(
  corpus_mm, 
  id2word = dictionary, 
  author2doc = auth2doc, 
  num_topics = 2L, 
  serialized = TRUE,
  serialization_path = temp
)

# delete temp
unlink(temp, recursive = TRUE)

Then extract the topics for each author.

atmodel$get_author_topics("jack") # native for single author 

# apply to all authors
get_author_topics(atmodel)

Log Entropy

log_entropy <- model_logentropy(corpus_bow)
wrap(log_entropy, corpus_bow)

Clean up, delete the corpus.

delete_mmcorpus(corpus_mm)

news-r/gensimr documentation built on Jan. 9, 2021, 5:55 a.m.