tests/testthat/test_helpers.R

context("helper functions")
library(igraph)
library(magrittr)
library(Matrix)
test_that("majorization gap is correct", {
  g <- graph.empty(n = 11, directed = FALSE)
  g <- add_edges(g, c(
    1, 11, 2, 4, 3, 5, 3, 11, 4, 8, 5, 9, 5, 11, 6, 7, 6, 8,
    6, 10, 6, 11, 7, 9, 7, 10, 7, 11, 8, 9, 8, 10, 9, 10
  ))

  expect_equal(round(majorization_gap(g), 7), 0.3529412)
  expect_equal(majorization_gap(g, norm = FALSE), 6)

  tg <- threshold_graph(20, 0.2)
  expect_equal(round(majorization_gap(tg), 7), 0)
  expect_equal(majorization_gap(tg, norm = FALSE), 0)
})

test_that("spectral gap is correct", {
  g <- graph.star(10, "undirected")

  expect_equal(spectral_gap(g, method = "frac"), 1)
  expect_equal(spectral_gap(g, method = "abs"), 3)
  expect_error(spectral_gap(g, method = "hello"))
})

test_that("compare_ranks is correct", {
  tg <- threshold_graph(20, 0.3)
  dc <- degree(tg)
  cc <- closeness(tg)
  res <- compare_ranks(dc, cc)
  expect_equal(res$discordant, 0)
  expect_equal(res$right, 0)
  expect_equal(res$left, 0)
  expect_equal(res$concordant + res$ties, 190)

  expect_error(compare_ranks(1:5, 1:7))
})

test_that("transitive_reduction is correct", {
  P <- matrix(1, 10, 10)
  P[lower.tri(P, diag = TRUE)] <- 0
  T_red <- transitive_reduction(P)
  expect_equal(sum(T_red), 9)
})

test_that("is_preserved is correct", {
  library(igraph)
  g <- graph.empty(n = 11, directed = FALSE)
  g <- add_edges(g, c(
    1, 11, 2, 4, 3, 5, 3, 11, 4, 8, 5, 9, 5, 11, 6, 7, 6, 8,
    6, 10, 6, 11, 7, 9, 7, 10, 7, 11, 8, 9, 8, 10, 9, 10
  ))

  P <- neighborhood_inclusion(g, sparse = FALSE)
  expect_equal(is_preserved(P, degree(g)), TRUE)
  expect_equal(is_preserved(P, closeness(g)), TRUE)
  expect_equal(is_preserved(P, betweenness(g)), TRUE)
  expect_equal(is_preserved(P, rowSums(P)), FALSE)
})


test_that("rank_intervals is correct", {
  library(igraph)
  library(magrittr)
  P <- matrix(c(0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, rep(0, 10)), 5, 5, byrow = TRUE)
  res <- rank_intervals(P)

  expect_equal(res$min_rank, c(1, 1, 2, 3, 3))
  expect_equal(res$max_rank, c(2, 4, 4, 5, 5))
  expect_equal(res$mid_point, c(1.5, 2.5, 3.0, 4.0, 4.0))
})

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netrankr documentation built on Sept. 27, 2022, 1:07 a.m.