Nothing
context("mallet-train-model")
data(sotu)
test_that(desc="mallet.import",{
skip_on_cran()
expect_silent({
sotu.instances <-
mallet.import(id.array = row.names(sotu),
text.array = sotu[["text"]],
stoplist = mallet_stoplist_file_path("en"),
token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")
})
expect_is(sotu.instances, "jobjRef")
expect_silent({
sotu.instances <-
mallet.import(text.array = sotu[["text"]])
})
})
test_that(desc="Train model",{
skip_on_cran()
expect_silent(
sotu.instances <-
mallet.import(id.array = row.names(sotu),
text.array = sotu[["text"]],
stoplist = mallet_stoplist_file_path("en"),
token.regexp = "\\p{L}[\\p{L}\\p{P}]+\\p{L}")
)
expect_silent(
topic.model <- MalletLDA(num.topics=5, alpha.sum = 1, beta = 0.1)
)
expect_is(topic.model, "jobjRef")
expect_silent(
topic.model$loadDocuments(sotu.instances)
)
expect_silent(
topic.model$train(20)
)
})
test_that(desc="Train model 2 (with stopwords)",{
skip_on_cran()
expect_silent(
sotu.instances <-
mallet.import(text.array = sotu[["text"]])
)
expect_silent(
topic.model <- MalletLDA(num.topics=5, alpha.sum = 1, beta = 0.1)
)
expect_is(topic.model, "jobjRef")
expect_silent(
topic.model$loadDocuments(sotu.instances)
)
expect_silent(
topic.model$train(20)
)
})
test_that(desc="Issue #2 bug",{
skip_on_cran()
documents <- data.frame(id=as.character(1:5), text=c("this is an example document",
"here is another example document",
"another small example",
"this document is quite long compared to the other examples",
"finally here is the last example"),
stringsAsFactors=FALSE)
mallet.instances <- mallet.import(documents$id, documents$text,
stoplist = c("the", "of"),
token.regexp = "[\\p{L}]+")
topic.model <- MalletLDA(num.topics=2)
topic.model$loadDocuments(mallet.instances)
vocabulary <- topic.model$getVocabulary()
word.freqs <- mallet.word.freqs(topic.model)
topic.model$setAlphaOptimization(20, 50)
topic.model$train(200)
doc.topics <- mallet.doc.topics(topic.model, smoothed=FALSE, normalized=FALSE)
topic.words <- mallet.topic.words(topic.model, smoothed=FALSE, normalized=FALSE)
expect_equal(sum(topic.words), sum(doc.topics))
res <- cbind(correct.counts = apply(topic.words, 2, sum), word.freqs)
expect_equal(res$correct.counts, res$word.freq)
}
)
test_that(desc="Test that hclust works as expected",{
skip_on_cran()
expect_silent(
sotu.instances <-
mallet.import(text.array = sotu[["text"]])
)
expect_silent(
topic.model <- MalletLDA(num.topics=5, alpha.sum = 1, beta = 0.1)
)
expect_silent(
topic.model$loadDocuments(sotu.instances)
)
expect_silent(
topic.model$train(20)
)
expect_silent(dt <- mallet.doc.topics(topic.model))
expect_silent(tw <- mallet.topic.words(topic.model))
expect_silent(
res <- mallet.topic.hclust(dt, tw)
)
})
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