tests/testthat/test_c_sparse.R

In elbamos/largeVis: High-Quality Visualizations of Large, High-Dimensional Datasets

context("sparse")
set.seed(1974)
dat <- as.matrix(iris[, 1:4])
dat <- scale(dat)
dupes <- which(duplicated(dat))
dat <- dat[-dupes, ]
dat <- t(dat)

neighbors <- randomProjectionTreeSearch(dat,
																				K = 5,
																				n_trees = 10,
																				tree_threshold = 20,
																				max_iter = 10,
																				verbose = FALSE,
																				threads = 1)

test_that("buildEdgeMatrix are the same", {
	edges1 <- buildEdgeMatrix(data = dat, neighbors = neighbors, verbose = FALSE)
  edges2 <- buildEdgeMatrix(data = Matrix::Matrix(dat, sparse = TRUE), neighbors = neighbors, verbose = FALSE)
  score <- sum(edges1$x - edges2$x)
  expect_lt(score, 1, label = "Euclidean")

	edges1 <- buildEdgeMatrix(data = dat, neighbors = neighbors, verbose = FALSE, distance_method = "Cosine")
	edges2 <- buildEdgeMatrix(data = Matrix::Matrix(dat, sparse = TRUE), neighbors = neighbors, verbose = FALSE, distance_method = "Cosine")
	score <- sum(edges1$x - edges2$x)
	expect_lt(score, 1, label = "Cosine")
})

M <- 5
mat <- Matrix::sparseMatrix(i = rep(1:nrow(dat), ncol(dat)),
														j = rep(1:ncol(dat), each = nrow(dat)),
														x = as.vector(dat))
d = as.matrix(dist(t(as.matrix(mat)), method = "euclidean"))

test_that("sparseDistances", {
  index_matrix <- matrix(c(
    rep(0:(ncol(dat) - 1), ncol(dat)),
    rep(0:(ncol(dat) - 1), each = ncol(dat))
  ), ncol = 2, byrow = FALSE)
  new_distances <- distance(mat,
                            as.vector(index_matrix[, 2]),
                            as.vector(index_matrix[, 1]),
                            "Euclidean", threads = 2,
                            verbose = FALSE)
  diffs <- as.vector(d) - new_distances
  expect_lt(sum(diffs), 1e-10)
})

test_that("Can determine sparse iris neighbors accurately", {
  bests <- apply(d, MARGIN = 1, FUN = function(x) order(x)[1:(M + 1)])
  bests <- bests[-1,] - 1
  neighbors <- randomProjectionTreeSearch(mat,
                                          K = M,
                                          n_trees = 20,
                                          max_iter = 2,
                                          tree_threshold = 30,
  																				seed = 1974, threads = 2,
                                          verbose = FALSE)
  expect_lte(sum(neighbors - bests, na.rm = TRUE), 5)
})