test_that("fitting", {
set.seed(1234)
spec <- hier_clust(num_clusters = 3) |>
set_engine("stats")
expect_no_error(
res <- fit(spec, ~., mtcars)
)
expect_no_error(
res <- fit_xy(spec, mtcars)
)
})
test_that("predicting", {
set.seed(1234)
spec <- hier_clust(num_clusters = 3) |>
set_engine("stats")
res <- fit(spec, ~., iris)
preds <- predict(res, iris[c(25, 75, 125), ])
expect_identical(
preds,
tibble::tibble(.pred_cluster = factor(paste0("Cluster_", 1:3)))
)
})
test_that("all levels are preserved with 1 row predictions", {
set.seed(1234)
spec <- hier_clust(num_clusters = 3) |>
set_engine("stats")
res <- fit(spec, ~., mtcars)
preds <- predict(res, mtcars[1, ])
expect_identical(
levels(preds$.pred_cluster),
paste0("Cluster_", 1:3)
)
})
test_that("extract_centroids() works", {
set.seed(1234)
spec <- hier_clust(num_clusters = 3) |>
set_engine("stats")
res <- fit(spec, ~., iris)
centroids <- extract_centroids(res)
expect_identical(
colnames(centroids),
c(
".cluster",
"Sepal.Length",
"Sepal.Width",
"Petal.Length",
"Petal.Width",
"Speciesversicolor",
"Speciesvirginica"
)
)
expect_identical(
centroids$.cluster,
factor(c("Cluster_1", "Cluster_2", "Cluster_3"))
)
})
test_that("extract_cluster_assignment() works", {
set.seed(1234)
spec <- hier_clust(num_clusters = 3) |>
set_engine("stats")
res <- fit(spec, ~., iris)
clusters <- extract_cluster_assignment(res)
expected <- vctrs::vec_cbind(
tibble::tibble(
.cluster = factor(paste0("Cluster_", cutree(res$fit, k = 3)))
)
)
expect_identical(
clusters,
expected
)
})
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