tests/testthat/test_spectral.R

In umap: Uniform Manifold Approximation and Projection

## tests for spectral analysis using coos

## ############################################################################
## Tests for counting of connected components using coo

test_that("count components in single-component", {
  mat <- matrix(0, ncol=6, nrow=6)
  mat[1,c(2,3)] <- 1
  mat[2, c(3,4)] <- 1
  mat[3, c(4,5)] <- 1
  mat[4, c(5,6)] <- 1
  mat[5, c(6)] <- 1
  mat <- mat + t(mat)
  result <- concomp.coo(coo(mat))
  expect_equal(result$n.components, 1)
  expect_equal(result$components, rep(0, 6))
})

test_that("count components, one two roughly equal-size components", {
  mat <- matrix(0, ncol=6, nrow=6)
  mat[1,c(2,3)] <- 1
  mat[2, c(3)] <- 1
  mat[4, c(5,6)] <- 1
  mat[5, c(6)] <- 1
  mat <- mat + t(mat)
  result <- concomp.coo(coo(mat))
  expect_equal(result$n.components, 2)
  expect_equal(result$components, c(rep(0, 3), rep(1, 3)))
})

test_that("count components, one disjoint element", {
  mat <- matrix(0, ncol=6, nrow=6)
  mat[1,c(2,3)] <- 1
  mat[2, c(3)] <- 1
  mat[4, c(5)] <- 1
  mat <- mat + t(mat)
  result <- concomp.coo(coo(mat))
  expect_equal(result$n.components, 3)
  expect_equal(result$components, c(rep(0, 3), rep(1, 2), 2))
})

test_that("count components, null matrix", {
  mat <- matrix(0, ncol=6, nrow=6)
  result <- concomp.coo(coo(mat))
  expect_equal(result$n.components, 6)
  expect_equal(result$components, 0:5)
})

## ############################################################################
## Tests for constructing identity matrix

test_that("simple identity coo", {
  result <- identity.coo(4)
  expected <- reduce.coo(coo(diag(4)))
  expect_equal(result, expected)
})

test_that("identity complains when names and n.elements don't match", {
  expect_error(identity.coo(4, letters[1:2]))
  expect_error(identity.coo(4, NA))
})

test_that("identity with names", {
  result <- identity.coo(4, letters[1:4])
  mat <- diag(4)
  rownames(mat) <- colnames(mat) <- letters[1:4]
  expected <- reduce.coo(coo(mat))
  expect_equal(result, expected)
})

## ############################################################################
## Tests for constructing Laplacian

test_that("laplacian of matrix", {
  mat <- matrix(0, ncol=6, nrow=6)
  mat[1,c(2,3)] <- 1
  mat[2, c(3,4)] <- 1
  mat[3, c(4,5)] <- 1
  mat[4, c(5,6)] <- 1
  mat[5, c(6)] <- 1
  mat <- mat + t(mat)

  ## compute laplacian using coo representation
  result <- laplacian.coo(coo(mat))
  
  ## construct expected laplacian matrix
  degrees <- apply(mat, 1, sum)
  D <- diag(1/sqrt(degrees))
  I <- diag(nrow(mat))
  L <- I - (D %*% mat %*% D)
  expected <- reduce.coo(coo(L))

  expect_equal(result, expected)
})

test_that("laplacian of graph matrix", {
  ## create a large matrix with some numbers
  mat <- matrix(abs(rnorm(20*20)), ncol=20)
  diag(mat) <- 0
  
  ## compute a laplacian using coo methods
  result <- laplacian.coo(coo(mat))
  
  ## construct expected laplacian matrix using matrix methods
  degrees <- apply(mat, 1, sum)
  D <- diag(1/sqrt(degrees))
  I <- diag(nrow(mat))
  L <- I - (D %*% mat %*% D)
  expected <- reduce.coo(coo(L))
  
  expect_equal(result, expected, tolerance=1e-3)
})

test_that("laplacian of matrix with zeros", {
  mat <- matrix(1:36, ncol=6, nrow=6)
  mat[3,] <- 0
  matcoo <- reduce.coo(coo(mat))
  expect_error(laplacian.coo(matcoo))
})

## ############################################################################
## Tests for subsetting coo matrices

test_that("subset coo detects early exit", {
  mat <- matrix(1:9, ncol=3, nrow=3)
  diag(mat) <- 0
  c1 <- coo(mat)
  result <- subset.coo(c1, 1:3)
  expect_equal(result, c1)
})

test_that("subset operates and produces a new coo", {
  mat <- matrix(1:16, ncol=4, nrow=4)
  diag(mat) <- 0
  c1 <- coo(mat)
  keep <- c(1,3)
  result <- subset.coo(c1, keep)
  expected <- mat[keep, keep]
  expect_equal(result, coo(expected))
})