context("testing JST")
toks <- ECB_press_conferences_tokens[1:10]

JST <- JST(toks, lexicon = LoughranMcDonald)

test_that("JST works", {
  expect_output(print(JST), "A JST model with 3 sentiments and 5 topics. Currently fitted by 0 Gibbs sampling iterations.")
  tabl <- table(JST$vocabulary$lexicon)
  expect_length(tabl, 3)
  expect_gt(sum(tabl), 0)
  JST <- fit(JST, 2, displayProgress = FALSE)
  expect_true(check_integrity(JST, fast = FALSE))
  expect_s3_class(JST, "JST")
  expect_equal(attr(JST, "reversed"), FALSE)

  JST2 <- fit(JST, 2, nChains = 2, displayProgress = FALSE)
  JST2 <- fit(JST2, 2, displayProgress = FALSE)
  expect_identical(attr(JST2, "containedClass"), "JST")

  expect_true(all(!sapply(JST2, function(x) attr(x, "reversed"))))
  expect_true(all(sapply(JST2, check_integrity, fast = FALSE)))
})

JST <- fit(JST, 2, displayProgress = FALSE)

test_that("functions works", {
  expect_s3_class(topWords(JST), "data.table")
  expect_true(is.matrix(topWords(JST, output = "matrix")))
  expect_silent(p <- topWords(JST, output = "plot"))
  expect_s3_class(p, "ggplot")
  expect_silent(print(p))
  expect_silent(m <- melt(JST))
  expect_s3_class(m, "data.table")
  # expect_equal(ncol(m), 6)
  skip_if_not_installed("plotly")
  expect_silent(p <- plot(JST))
  expect_s3_class(p, "plotly")
  expect_silent(print(p))
})

test_that("test convergence", {
  vocab <- generateVocab(nTopics = 3, nSentiments = 2, nWords = 3, nCommonWords = 1, hierarchy = "JST")
  toks <- generateDocuments(vocab, nDocs = 100, L1prior = .1, L2prior = 5, nWords = 100, nClass = 1)
  lex <- generatePartialLexicon(toks, Sindex = 1:2)

  ## With lexicon
  retry <- 0
  convergence <- FALSE
  while (convergence == FALSE && retry < 5) {
    JST <- JST(toks, lex, K = 3, S = 2, gamma = .1, alpha = 5)
    JST <- fit(JST, 100, nChains = 2, displayProgress = FALSE)
    count <- 0
    while (convergence == FALSE && count < 30) {
      JST <- fit(JST, 100, nChains = 2, displayProgress = FALSE)
      count <- count + 1
      if (all(distToGenerative(JST, vocab) < .15)) convergence <- TRUE
    }
    retry <- retry + 1
  }
  sapply(distToGenerative(JST, vocab), expect_lte, .15)

  ## No lexicon
  retry <- 0
  convergence <- FALSE
  while (convergence == FALSE && retry < 10) {
    JST <- JST(toks, K = 3, S = 2, gamma = .1, alpha = 5)
    JST <- fit(JST, 100, nChains = 2, displayProgress = FALSE)
    expect_message(distToGenerative(JST, vocab), "No lexicon detected, allowing")
    count <- 0
    while (convergence == FALSE && count < 30) {
      JST <- fit(JST, 100, nChains = 2, displayProgress = FALSE)
      count <- count + 1
      if (all(suppressMessages(distToGenerative(JST, vocab)) < .15))
        convergence <- TRUE
    }
    retry <- retry + 1
  }
  sapply(suppressMessages(distToGenerative(JST, vocab)), expect_lte, .15)
})

#
#
# topWords(JST$chain1, output = "plot")
# topWords(JST$chain2, output = "plot")
#
# e <- JST$chain2
# e <- fit(e, 10000)
# topWords(e, output = "plot")
# plot(e$logLikelihood)
# rebuild_zw(as.sentopicmodel(JST$chain2))
# rebuild_zw(as.sentopicmodel(e))