text2vec (version 0.6.4)

LatentDirichletAllocation: Creates Latent Dirichlet Allocation model.

Description

Creates Latent Dirichlet Allocation model. At the moment only 'WarpLDA' is implemented. WarpLDA, an LDA sampler which achieves both the best O(1) time complexity per token and the best O(K) scope of random access. Our empirical results in a wide range of testing conditions demonstrate that WarpLDA is consistently 5-15x faster than the state-of-the-art Metropolis-Hastings based LightLDA, and is comparable or faster than the sparsity aware F+LDA.

Usage

LatentDirichletAllocation
LDA

Format

R6Class object.

Fields

topic_word_distribution

distribution of words for each topic. Available after model fitting with model$fit_transform() method.

components

unnormalized word counts for each topic-word entry. Available after model fitting with model$fit_transform() method.

Usage

For usage details see Methods, Arguments and Examples sections.

lda = LDA$new(n_topics = 10L, doc_topic_prior = 50 / n_topics, topic_word_prior = 1 / n_topics)
lda$fit_transform(x, n_iter = 1000, convergence_tol = 1e-3, n_check_convergence = 10, progressbar = interactive())
lda$transform(x, n_iter = 1000, convergence_tol = 1e-3, n_check_convergence = 5, progressbar = FALSE)
lda$get_top_words(n = 10, topic_number = 1L:private$n_topics, lambda = 1)

Methods

$new(n_topics, doc_topic_prior = 50 / n_topics, # alpha topic_word_prior = 1 / n_topics, # beta method = "WarpLDA")

Constructor for LDA model. For description of arguments see Arguments section.

$fit_transform(x, n_iter, convergence_tol = -1, n_check_convergence = 0, progressbar = interactive())

fit LDA model to input matrix x and transforms input documents to topic space. Result is a matrix where each row represents corresponding document. Values in a row form distribution over topics.

$transform(x, n_iter, convergence_tol = -1, n_check_convergence = 0, progressbar = FALSE)

transforms new documents into topic space. Result is a matrix where each row is a distribution of a documents over latent topic space.

$get_top_words(n = 10, topic_number = 1L:private$n_topics, lambda = 1)

returns "top words" for a given topic (or several topics). Words for each topic can be sorted by probability of chance to observe word in a given topic (lambda = 1) and by "relevance" which also takes into account frequency of word in corpus (lambda < 1). From our experience in most cases setting 0.2 < lambda < 0.4 works well. See http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf for details.

$plot(lambda.step = 0.1, reorder.topics = FALSE, ...)

plot LDA model using https://cran.r-project.org/package=LDAvis package. ... will be passed to LDAvis::createJSON and LDAvis::serVis functions

Arguments

lda

A LDA object

x

An input document-term matrix (should have column names = terms). CSR RsparseMatrix used internally, other formats will be tried to convert to CSR via as() function call.

n_topics

integer desired number of latent topics. Also knows as K

doc_topic_prior

numeric prior for document-topic multinomial distribution. Also knows as alpha

topic_word_prior

numeric prior for topic-word multinomial distribution. Also knows as eta

n_iter

integer number of sampling iterations while fitting model

n_iter_inference

integer number iterations used when sampling from converged model for inference. In other words number of samples from distribution after burn-in.

n_check_convergence

defines how often calculate score to check convergence

convergence_tol

numeric = -1 defines early stopping strategy. We stop fitting when one of two following conditions will be satisfied: (a) we have used all iterations, or (b) score_previous_check / score_current < 1 + convergence_tol

Examples

if (FALSE) {
library(text2vec)
data("movie_review")
N = 500
tokens = word_tokenizer(tolower(movie_review$review[1:N]))
it = itoken(tokens, ids = movie_review$id[1:N])
v = create_vocabulary(it)
v = prune_vocabulary(v, term_count_min = 5, doc_proportion_max = 0.2)
dtm = create_dtm(it, vocab_vectorizer(v))
lda_model = LDA$new(n_topics = 10)
doc_topic_distr = lda_model$fit_transform(dtm, n_iter = 20)
# run LDAvis visualisation if needed (make sure LDAvis package installed)
# lda_model$plot()
}