在R中使用textmineR软件包制作的LDA模型上，如何测量困惑度评分?

问题描述

我已经使用textmineR包在R中创建了LDA主题模型，如下所示.

I've made a LDA topic model in R, using the textmineR package, it looks as follows.

## get textmineR dtm
dtm2 <- CreateDtm(doc_vec = dat2$fulltext, # character vector of documents
                 ngram_window = c(1, 2),
                 doc_names = dat2$names,
                 stopword_vec = c(stopwords::stopwords("da"), custom_stopwords),
                 lower = T, # lowercase - this is the default value
                 remove_punctuation = T, # punctuation - this is the default
                 remove_numbers = T, # numbers - this is the default
                 verbose = T,
                 cpus = 4)



dtm2 <- dtm2[, colSums(dtm2) > 2]
dtm2 <- dtm2[, str_length(colnames(dtm2)) > 2]


############################################################
## RUN & EXAMINE TOPIC MODEL
############################################################

# Draw quasi-random sample from the pc
set.seed(34838)

model2 <- FitLdaModel(dtm = dtm2,
                     k = 8,
                     iterations = 500,
                     burnin = 200,
                     alpha = 0.1,
                     beta = 0.05,
                     optimize_alpha = TRUE,
                     calc_likelihood = TRUE,
                     calc_coherence = TRUE,
                     calc_r2 = TRUE,
                     cpus = 4)

然后的问题是:1.我应该使用哪个函数来获取textmineR软件包中的困惑度分数?我似乎找不到一个.
2.如何衡量不同主题数(k)的复杂度得分?

The questions are then:1. Which function should i apply to get the perplexity scores in the textmineR package? I can't seem to find one.
2. how do i measure complexity scores for different numbers of topics(k)?

推荐答案

所要求的:除非您自己明确编程，否则无法使用textmineR计算困惑. TBH，我从来没有见过用可能性和连贯性无法获得的困惑的价值，所以我没有实现它.

As asked: there's no way to calculate perplexity with textmineR unless you explicitly program it yourself. TBH, I've never seen value of perplexity that you couldn't get with likelihood and coherence, so I didn't implement it.

但是，text2vec软件包确实有一个实现.参见以下示例:

However, the text2vec package does have an implementation. See below for example:

library(textmineR)

# model ships with textmineR as example
m <- nih_sample_topic_model

# dtm ships with textmineR as example
d <- nih_sample_dtm

# get perplexity
p <- text2vec::perplexity(X = d,
                          topic_word_distribution = m$phi,
                          doc_topic_distribution = m$theta)

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