tests/testthat/test_mixed_distances.R

In jlmelville/uwot: The Uniform Manifold Approximation and Projection (UMAP) Method for Dimensionality Reduction

library(uwot)
context("mixed distance calculations")

set.seed(1337)
res <- umap(iris10,
  n_neighbors = 4, n_epochs = 2, init = "spca",
  verbose = FALSE, n_threads = 0
)
expect_ok_matrix(res)

set.seed(1337)
resmli <- umap(iris10,
  n_neighbors = 4, n_epochs = 2, init = "spca",
  metric = list("euclidean" = 1:4),
  verbose = FALSE, n_threads = 0
)
expect_equal(resmli, res)

set.seed(1337)
resmls <- umap(iris10,
  n_neighbors = 4, n_epochs = 2, init = "spca",
  metric = list("euclidean" = c(
    "Sepal.Length", "Sepal.Width",
    "Petal.Length", "Petal.Width"
  )),
  verbose = FALSE, n_threads = 0
)
expect_equal(resmls, res)

set.seed(1337)
jiris10 <- jitter(iris10)
metric2 <- list(
  "euclidean" = c(1, 2),
  "euclidean" = c("Petal.Length", "Petal.Width")
)
reseuc2 <- umap(jiris10,
  n_neighbors = 4, n_epochs = 2, init = "spca",
  metric = metric2,
  verbose = FALSE, n_threads = 0,
  ret_nn = TRUE, ret_model = TRUE
)
expect_ok_matrix(reseuc2$embedding)
expect_equal(reseuc2$metric, metric2)
expect_is(reseuc2$nn, "list")
expect_equal(names(reseuc2$nn), c("euclidean", "euclidean"))
expect_is_nn(reseuc2$nn[[1]], 10, 4)
expect_is_nn(reseuc2$nn[[2]], 10, 4)
expect_ok_matrix(umap_transform(jiris10, reseuc2))

i10factor <- factor(c(rep("foo", 3), rep("bar", 3), rep("baz", 4)))

res_y2 <- umap(iris10[, -1],
  y = cbind(i10factor, iris$Sepal.Length[1:10]),
  n_neighbors = 4, n_epochs = 2, init = "spca",
  verbose = FALSE, n_threads = 0
)
expect_ok_matrix(res_y2)

nafactor <- as.factor(c(levels(iris$Species)[
  c(rep(1, 3), rep(2, 3), rep(3, 3))
], NA))
iris10c <- cbind(data.frame(iris10), nafactor)
rescat <- umap(iris10c,
  metric = list("euclidean" = 1:4, "categorical" = "nafactor"),
  n_neighbors = 4, n_epochs = 2, init = "spca", verbose = FALSE,
  n_threads = 0
)
expect_ok_matrix(rescat)


irismixed <- data.frame(iris10, ynum, ynum2, ycat, ycat2)
resmixed <- umap(irismixed,
  n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
  init = "rand", verbose = FALSE, n_threads = 1,
  metric = list(
    "euclidean" = 1:4, "euclidean" = 5, "cosine" = 6,
    "categorical" = c("ycat", "ycat2")
  )
)
expect_ok_matrix(resmixed)

irismixed <- data.frame(iris10, ynum, ynum2, ycat, ycat2)
resmixed <- umap(irismixed,
  n_neighbors = 4, n_epochs = 2, learning_rate = 0.5, min_dist = 0.001,
  init = "rand", verbose = FALSE, n_threads = 1,
  metric = list(
    "euclidean" = 1:4, "euclidean" = 5, "cosine" = 6,
    "categorical" = c("ycat", "ycat2")
  ),
  ret_model = TRUE, ret_nn = TRUE
)
expect_ok_matrix(resmixed$embedding)
expect_ok_matrix(umap_transform(irismixed, resmixed, n_threads = 1))
expect_equal(names(resmixed$nn), c("euclidean", "euclidean", "cosine"))

# #20: allow matrix column for categorical
int_column <- c(1, 2, 3, 4, 4, 4, 2, 1, 2, 1)
irisic <- cbind(iris10, int_column)
resic <- umap(irisic,
  n_neighbors = 4, n_epochs = 2, learning_rate = 0.5,
  init = "spca", verbose = FALSE, n_threads = 0,
  metric = list("euclidean" = 1:4, "categorical" = 5)
)
expect_ok_matrix(resic)