我如何测量在R中使用textmineR软件包制作的LDA模型的困惑度分数？

Question

我使用textmineR包在R中制作了LDA主题模型，它看起来如下。

## 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.如何测量不同主题数（k）的复杂度得分？

Answer 1

根据要求：除非您自己明确编程，否则无法使用textmineR计算困惑。 TBH，我从未见过无法通过可能性和连贯性获得困惑的价值，因此我没有实现它。

但是，text2vec包确实有一个实现。参见以下示例：

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)