tests/testthat/test-k_means_diagnostics.R

In tidyclust: A Common API to Clustering

test_that("kmeans sse metrics work", {
  kmeans_fit_stats <- k_means(num_clusters = mtcars[1:3, ]) %>%
    set_engine("stats", algorithm = "MacQueen") %>%
    fit(~., mtcars)

  kmeans_fit_ClusterR <- k_means(num_clusters = 3) %>%
    set_engine("ClusterR", CENTROIDS = as.matrix(mtcars[1:3, ])) %>%
    fit(~., mtcars)

  km_orig <- kmeans(mtcars, centers = mtcars[1:3, ], algorithm = "MacQueen")
  km_orig_2 <- ClusterR::KMeans_rcpp(
    data = mtcars,
    clusters = 3,
    CENTROIDS = as.matrix(mtcars[1:3, ])
  )

  expect_equal(sse_within(kmeans_fit_stats)$wss,
    c(42877.103, 76954.010, 7654.146), # hard coded because of order
    tolerance = 0.005
  )

  expect_equal(
    sse_within_total_vec(kmeans_fit_stats),
    km_orig$tot.withinss,
    tolerance = 0.005
  )
  expect_equal(
    sse_total_vec(kmeans_fit_stats),
    km_orig$totss,
    tolerance = 0.005
  )
  expect_equal(
    sse_ratio_vec(kmeans_fit_stats),
    km_orig$tot.withinss / km_orig$totss,
    tolerance = 0.005
  )

  expect_equal(sse_within(kmeans_fit_ClusterR)$wss,
    c(42877.103, 56041.432, 4665.041), # hard coded because of order
    tolerance = 0.005
  )

  expect_equal(
    sse_within_total_vec(kmeans_fit_ClusterR),
    sum(km_orig_2$WCSS_per_cluster),
    tolerance = 0.005
  )
  expect_equal(
    sse_total_vec(kmeans_fit_ClusterR),
    sse_total_vec(kmeans_fit_stats),
    tolerance = 0.005
  )
  expect_equal(
    sse_ratio_vec(kmeans_fit_ClusterR),
    0.1661624,
    tolerance = 0.005
  )
})

test_that("kmeans sse metrics work on new data", {
  kmeans_fit_stats <- k_means(num_clusters = mtcars[1:3, ]) %>%
    set_engine("stats", algorithm = "MacQueen") %>%
    fit(~., mtcars)

  new_data <- mtcars[1:4, ]

  expect_equal(sse_within(kmeans_fit_stats, new_data)$wss,
    c(2799.21, 12855.17),
    tolerance = 0.005
  )

  expect_equal(
    sse_within_total_vec(kmeans_fit_stats, new_data),
    15654.38,
    tolerance = 0.005
  )
  expect_equal(
    sse_total_vec(kmeans_fit_stats, new_data),
    32763.7,
    tolerance = 0.005
  )
  expect_equal(
    sse_ratio_vec(kmeans_fit_stats, new_data),
    15654.38 / 32763.7,
    tolerance = 0.005
  )
})

test_that("kmeans sihouette metrics work", {
  kmeans_fit_stats <- k_means(num_clusters = mtcars[1:3, ]) %>%
    set_engine("stats", algorithm = "MacQueen") %>%
    fit(~., mtcars)

  kmeans_fit_ClusterR <- k_means(num_clusters = 3) %>%
    set_engine("ClusterR", CENTROIDS = as.matrix(mtcars[1:3, ])) %>%
    fit(~., mtcars)

  new_data <- mtcars[1:4, ]

  dists <- mtcars %>%
    as.matrix() %>%
    dist()

  expect_equal(
    names(silhouette(kmeans_fit_stats, dists = dists)),
    names(silhouette(kmeans_fit_ClusterR, dists = dists))
  )

  expect_equal(
    silhouette_avg_vec(kmeans_fit_stats, dists = dists),
    0.4993742,
    tolerance = 0.005
  )
  expect_equal(
    silhouette_avg_vec(kmeans_fit_ClusterR, dists = dists),
    0.5473414,
    tolerance = 0.005
  )
})

test_that("kmeans sihouette metrics work with new data", {
  kmeans_fit_stats <- k_means(num_clusters = mtcars[1:3, ]) %>%
    set_engine("stats", algorithm = "MacQueen") %>%
    fit(~., mtcars)

  kmeans_fit_ClusterR <- k_means(num_clusters = 3) %>%
    set_engine("ClusterR", CENTROIDS = as.matrix(mtcars[1:3, ])) %>%
    fit(~., mtcars)

  new_data <- mtcars[1:4, ]

  expect_equal(
    names(silhouette(kmeans_fit_stats, new_data = new_data)),
    names(silhouette(kmeans_fit_ClusterR, new_data = new_data))
  )

  expect_equal(
    silhouette_avg_vec(kmeans_fit_stats, new_data = new_data),
    0.5176315,
    tolerance = 0.005
  )
  expect_equal(
    silhouette_avg_vec(kmeans_fit_ClusterR, new_data = new_data),
    0.5176315,
    tolerance = 0.005
  )
})