tests/testthat/test_binary.R

In rnndescent: Nearest Neighbor Descent Method for Approximate Nearest Neighbors

library(rnndescent)
context("Binary data tests")

# Regression test for a bug in the Jaccard distance calculation where the empty
# data would not be replaced with maximally distance points

# Data with identical rows and many items which are maximally dissimilar from
# each other, i.e. they have 0 overlap. Because of the identical rows and many
# degenerate distances we just the distance matrices are equal but not the index
# matrices (but we do want to avoid 0 indices occurring)

badjaccard <- matrix(
  c(
    0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
    0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0,
    0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
    1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1,
    0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1,
    0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
    1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,
    0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,
    0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
    0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0,
    0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,
    1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
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    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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    0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
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    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
  ),
  nrow = 30
)

badjaccardb <- badjaccard > 0.5
badjaccards <- Matrix::drop0(badjaccard)

test_that("jaccard", {
  metric <- "jaccard"
  # dense
  d_res <- brute_force_knn(badjaccard,
    k = 5,
    metric = metric,
    use_alt_metric = FALSE
  )
  da_res <- brute_force_knn(badjaccard,
    k = 5,
    metric = metric,
    use_alt_metric = TRUE
  )
  expect_false(0 %in% d_res$idx)
  expect_false(0 %in% da_res$idx)
  expect_equal(d_res$dist, da_res$dist, tol = 1e-7)

  # sparse
  s_res <- brute_force_knn(badjaccards,
    k = 5,
    metric = metric,
    use_alt_metric = FALSE
  )
  sa_res <- brute_force_knn(badjaccards,
    k = 5,
    metric = metric,
    use_alt_metric = TRUE
  )
  expect_false(0 %in% s_res$idx)
  expect_false(0 %in% sa_res$idx)
  expect_equal(s_res$dist, sa_res$dist, tol = 1e-7)
})

test_that("hellinger", {
  metric <- "hellinger"
  # dense
  d_res <- brute_force_knn(badjaccard,
    k = 5,
    metric = metric,
    use_alt_metric = FALSE
  )
  da_res <- brute_force_knn(badjaccard,
    k = 5,
    metric = metric,
    use_alt_metric = TRUE
  )
  expect_false(0 %in% d_res$idx)
  expect_false(0 %in% da_res$idx)
  expect_equal(d_res$dist, da_res$dist, tol = 1e-7)

  # sparse
  s_res <- brute_force_knn(badjaccards,
    k = 5,
    metric = metric,
    use_alt_metric = FALSE
  )
  sa_res <- brute_force_knn(badjaccards,
    k = 5,
    metric = metric,
    use_alt_metric = TRUE
  )
  expect_false(0 %in% s_res$idx)
  expect_false(0 %in% sa_res$idx)
  expect_equal(s_res$dist, sa_res$dist, tol = 1e-7)
})

test_that("cosine", {
  metric <- "cosine"
  # dense
  d_res <- brute_force_knn(badjaccard,
    k = 5,
    metric = metric,
    use_alt_metric = FALSE
  )
  da_res <- brute_force_knn(badjaccard,
    k = 5,
    metric = metric,
    use_alt_metric = TRUE
  )
  expect_false(0 %in% d_res$idx)
  expect_false(0 %in% da_res$idx)
  expect_equal(d_res$dist, da_res$dist, tol = 1e-7)

  # sparse
  s_res <- brute_force_knn(badjaccards,
    k = 5,
    metric = metric,
    use_alt_metric = FALSE
  )
  sa_res <- brute_force_knn(badjaccards,
    k = 5,
    metric = metric,
    use_alt_metric = TRUE
  )
  expect_false(0 %in% s_res$idx)
  expect_false(0 %in% sa_res$idx)
  expect_equal(s_res$dist, sa_res$dist, tol = 1e-7)
})

# test non-alt distances dense vs sparse vs binary
for (metric in c(
  "braycurtis",
  "canberra",
  "chebyshev",
  "correlation",
  "cosine",
  "dice",
  "euclidean",
  "hamming",
  "hellinger",
  "jaccard",
  "jensenshannon",
  "kulsinski",
  "manhattan",
  "rogerstanimoto",
  "russellrao",
  "sokalmichener",
  "sokalsneath",
  "spearmanr",
  "symmetrickl",
  "tsss",
  "yule"
)) {
  test_that(metric, {
    # dense
    d_res <- brute_force_knn(badjaccard, k = 5, metric = metric)
    expect_false(0 %in% d_res$idx)

    # sparse
    s_res <- brute_force_knn(badjaccards, k = 5, metric = metric)
    expect_false(0 %in% s_res$idx)

    # sparse should equal dense
    tol = if (metric %in% c("correlation", "cosine", "jensenshannon")) 1e-5 else 1e-7
    if (metric == "symmetrickl") {
      # KL has quite large distances (> 10) so the tolerance needs to be larger
      tol <- 1e-3
    }
    expect_equal(d_res$dist, s_res$dist, tol = tol)

    if (metric %in% c(
      "dice",
      "hamming",
      "jaccard",
      "kulsinski",
      "rogerstanimoto",
      "russellrao",
      "sokalmichener",
      "sokalsneath",
      "yule"
    )) {
      # binary
      b_res <- brute_force_knn(badjaccardb, k = 5, metric = metric)
      expect_false(0 %in% b_res$idx)

      # binary should equal dense
      expect_equal(d_res$dist, b_res$dist, tol = 1e-7)
    }
  })
}