tests/testthat/test-ACMTF_modelSelection.R

In CMTFtoolbox: Create (Advanced) Coupled Matrix and Tensor Factorization Models

test_that("the function works normally", {
  set.seed(123)

  I = 10
  J = 5
  K = 3
  df = array(rnorm(I*J*K), c(I,J,K))
  df2 = array(rnorm(I*J*K), c(I,J,K))
  datasets = list(df, df2)
  modes = list(c(1,2,3), c(1,4,5))

  expect_no_error(ACMTF_modelSelection(datasets, modes, maxNumComponents=2, nstart=2, cvFolds=2, rel_tol=0.5, abs_tol=0.5))
})

test_that("the function works when jack-knifing", {
  set.seed(123)

  I = 10
  J = 5
  K = 3
  df = array(rnorm(I*J*K), c(I,J,K))
  df2 = array(rnorm(I*J*K), c(I,J,K))
  datasets = list(df, df2)
  modes = list(c(1,2,3), c(1,4,5))

  expect_no_error(ACMTF_modelSelection(datasets, modes, maxNumComponents=2, nstart=2, cvFolds=10, rel_tol=0.5, abs_tol=0.5))
})

test_that("running in parallel works", {
  skip_on_cran()

  set.seed(123)

  I = 10
  J = 5
  K = 3
  df = array(rnorm(I*J*K), c(I,J,K))
  df2 = array(rnorm(I*J*K), c(I,J,K))
  datasets = list(df, df2)
  modes = list(c(1,2,3), c(1,4,5))

  expect_no_error(ACMTF_modelSelection(datasets, modes, maxNumComponents=2, nstart=2, cvFolds=2, rel_tol=0.5, abs_tol=0.5, numCores=2))
})