library(dplyr)

data("cheese_abundance")

nc <- sample(2:9, size = 1)
new_names <- paste0("hc_", 1:nc)


clustering <-
  cheese_abundance %>%
  select(where(is.numeric)) %>%
  as.matrix() %>%
  t() %>%
  dist(method = "manhattan") %>%
  hclust(method = "ward.D2") %>%
  cutree(k = nc)

rnd_asv <- sample(names(clustering), size = 1)
rnd_asv_group <- names(clustering[clustering == clustering[rnd_asv]])


test_that("step_aggregate_hclust works", {

  rec <-
    recipe(~ ., data = cheese_abundance) %>%
    step_aggregate_hclust(all_numeric_predictors(),
                          n_clusters = nc, fun_agg = sum,
                          prefix = "hc_", dist_metric = "manhattan",
                          linkage_method = "ward.D2")

  expect_equal(nrow(tidy(rec, 1)), 1)

  prepped <- prep(rec)
  hclust_tidy <- tidy(prepped, 1)

  expect_equal(nrow(hclust_tidy), ncol(cheese_abundance) - 3)


 # check set equality on random cluster
  expect_setequal(
    hclust_tidy %>%
      filter(aggregate %in%
               filter(hclust_tidy, terms == rnd_asv)$aggregate) %>%
      pull(terms),
    rnd_asv_group)

  # check equality on groups cardinal
  expect_setequal(
    hclust_tidy %>%
      count(aggregate) %>%
      pull(n) %>%
      sort(),
    clustering %>%
      table() %>%
      sort() %>%
      unname())


  expect_setequal(summary(prepped)$role, "predictor")

  baked <- bake(prepped, new_data = NULL)

  expect_equal(colnames(baked),
               c("sample", "cheese", "rind_type", new_names))
  expect_equal(rowSums(baked[, -c(1:3)]), rowSums(cheese_abundance[, -c(1:3)]))

  cl <- sample(new_names, size = 1)
  asv_in_cl <-
    hclust_tidy %>%
    filter(aggregate == cl) %>%
    pull(terms)


  expect_equal(baked[[cl]],
               cheese_abundance %>%
                 select(all_of(asv_in_cl)) %>%
                 rowSums())

  ## keep_original_cols

  baked2 <-
    recipe(~ ., data = cheese_abundance) %>%
    step_aggregate_hclust(all_numeric_predictors(),
                          n_clusters = nc, fun_agg = sum,
                          prefix = "hc_", dist_metric = "manhattan",
                          linkage_method = "ward.D2",
                          keep_original_cols = TRUE) %>%
    prep() %>%
    bake(new_data = NULL)

  expect_equal(colnames(baked2),
               c(colnames(cheese_abundance), new_names))


  expect_invisible(recipes_pkg_check(required_pkgs.step_aggregate_hclust()))

})


test_that("step_aggregate_hclust throws errors", {

  expect_error(
    recipe(~ ., data = cheese_abundance) %>%
      step_aggregate_hclust(all_numeric_predictors(),
                            n_clusters = nc, fun_agg = sum,
                            dist_metric = "hello") %>%
      prep(),
    '`dist_metric` must be one of "euclidean",'
  )

  expect_error(
    recipe(~ ., data = cheese_abundance) %>%
      step_aggregate_hclust(all_numeric_predictors(),
                            n_clusters = nc, fun_agg = sum,
                            linkage_method = "hello") %>%
      prep(),
    '`linkage_method` must be one of "ward.D",'
  )


})