Nothing
test_that("jst execution correct, unsupervised, with docvars", {
# The type of tokens doesn't matter for the model, so
# no cleaning here whatsoever. Reduce to 10 documents
# for reduced runtime.
data <- quanteda::dfm(quanteda::data_corpus_inaugural)
data <- quanteda::dfm_sample(data, 10)
model_rjst <- jst_reversed(data)
expect_true(is.JST_reversed.result(model_rjst))
top20 <- top20words(model_rjst)
theta <- get_parameter(model_rjst, "theta")
pi <- get_parameter(model_rjst, "pi")
phi <- get_parameter(model_rjst, "phi")
expect_is(theta, "data.frame")
expect_equal(nrow(theta), quanteda::ndoc(data)) #Equal length
expect_equal(ncol(theta), ncol(quanteda::docvars(data)) + 10)
# docvars + 10 topic
expect_is(pi, "data.frame")
expect_equal(ncol(pi), 1 + ncol(quanteda::docvars(data)) + 1 + 3)
# docid + docvars + topic + num topics
expect_equal(nrow(pi), quanteda::ndoc(data) * 10)
# one row per topic per document (ndoc * 10)
expect_is(phi, "data.frame")
expect_equal(nrow(phi), quanteda::nfeat(data) * 10 * 3) #num features * 10 topic * 3 senti
expect_equal(ncol(phi), 4)
})
test_that("jst execution correct, unsupervised, without docvars", {
# The type of tokens doesn't matter for the model, so
# no cleaning here whatsoever. Reduce to 10 documents
# for reduced runtime.
data <- quanteda::dfm(quanteda::data_corpus_inaugural)
quanteda::docvars(data) <- NULL
data <- quanteda::dfm_sample(data, 10)
model_rjst <- jst_reversed(data)
expect_true(is.JST_reversed.result(model_rjst))
top20 <- top20words(model_rjst)
theta <- get_parameter(model_rjst, "theta")
pi <- get_parameter(model_rjst, "pi")
phi <- get_parameter(model_rjst, "phi")
expect_is(theta, "data.frame")
expect_equal(nrow(theta), quanteda::ndoc(data)) #Equal length
expect_equal(ncol(theta), ncol(quanteda::docvars(data)) + 10)
# docvars + 10 topic
expect_is(pi, "data.frame")
expect_equal(ncol(pi), 1 + ncol(quanteda::docvars(data)) + 1 + 3)
# docid + docvars + topic + num topics
expect_equal(nrow(pi), quanteda::ndoc(data) * 10)
# one row per topic per document (ndoc * 10)
expect_is(phi, "data.frame")
expect_equal(nrow(phi), quanteda::nfeat(data) * 10 * 3) #num features * 10 topic * 3 senti
expect_equal(ncol(phi), 4)
})
test_that("jst execution correct, supervised, with docvars", {
# The type of tokens doesn't matter for the model, so
# no cleaning here whatsoever. Reduce to 10 documents
# for reduced runtime.
data <- quanteda::dfm(quanteda::data_corpus_inaugural)
data <- quanteda::dfm_sample(data, 10)
model_rjst <- jst_reversed(data, paradigm())
expect_true(is.JST_reversed.result(model_rjst))
top20 <- top20words(model_rjst)
top20 <- top20words(model_rjst)
theta <- get_parameter(model_rjst, "theta")
pi <- get_parameter(model_rjst, "pi")
phi <- get_parameter(model_rjst, "phi")
expect_is(theta, "data.frame")
expect_equal(nrow(theta), quanteda::ndoc(data)) #Equal length
expect_equal(ncol(theta), ncol(quanteda::docvars(data)) + 10)
# docvars + 10 topic
expect_is(pi, "data.frame")
expect_equal(ncol(pi), 1 + ncol(quanteda::docvars(data)) + 1 + 3)
# docid + docvars + topic + num topics
expect_equal(nrow(pi), quanteda::ndoc(data) * 10)
# one row per topic per document (ndoc * 10)
expect_is(phi, "data.frame")
expect_equal(nrow(phi), quanteda::nfeat(data) * 10 * 3) #num features * 10 topic * 3 senti
expect_equal(ncol(phi), 4)
})
test_that("jst execution correct, supervised, without docvars", {
# The type of tokens doesn't matter for the model, so
# no cleaning here whatsoever. Reduce to 10 documents
# for reduced runtime.
data <- quanteda::dfm(quanteda::data_corpus_inaugural)
quanteda::docvars(data) <- NULL
data <- quanteda::dfm_sample(data, 10)
model_rjst <- jst_reversed(data, paradigm())
expect_true(is.JST_reversed.result(model_rjst))
top20 <- top20words(model_rjst)
top20 <- top20words(model_rjst)
theta <- get_parameter(model_rjst, "theta")
pi <- get_parameter(model_rjst, "pi")
phi <- get_parameter(model_rjst, "phi")
expect_is(theta, "data.frame")
expect_equal(nrow(theta), quanteda::ndoc(data)) #Equal length
expect_equal(ncol(theta), ncol(quanteda::docvars(data)) + 10)
# docvars + 10 topic
expect_is(pi, "data.frame")
expect_equal(ncol(pi), 1 + ncol(quanteda::docvars(data)) + 1 + 3)
# docid + docvars + topic + num topics
expect_equal(nrow(pi), quanteda::ndoc(data) * 10)
# one row per topic per document (ndoc * 10)
expect_is(phi, "data.frame")
expect_equal(nrow(phi), quanteda::nfeat(data) * 10 * 3) #num features * 10 topic * 3 senti
expect_equal(ncol(phi), 4)
})
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