revdep/checks.noindex/greta.dynamics/old/greta.dynamics.Rcheck/tests/testthat/test_iterate_matrix.R

In greta-dev/greta: Simple and Scalable Statistical Modelling in R

context("iteration functions")

test_that("single iteration works", {
  skip_if_not(greta:::check_tf_version())
  source("helpers.R")

  n <- 10
  mat <- randu(n, n)
  init <- rep(1, n)
  niter <- 50
  tol <- 1e-6
  test_tol <- tol * 10

  # r version
  r_iterates <- r_iterate_matrix(
    matrix = mat,
    state = init,
    niter = niter,
    tol = tol
  )

  target_lambda <- r_iterates$lambda
  target_stable <- r_iterates$stable_distribution
  target_states <- r_iterates$all_states

  # greta version
  iterates <- iterate_matrix(
    matrix = mat,
    initial_state = init,
    niter = niter,
    tol = tol
  )

  lambda <- iterates$lambda
  stable <- iterates$stable_distribution
  states <- iterates$all_states
  converged <- iterates$converged
  iterations <- iterates$iterations

  greta_lambda <- calculate(lambda)[[1]]
  difference <- abs(greta_lambda - target_lambda)
  expect_true(difference < test_tol)

  greta_stable <- calculate(stable)[[1]]
  difference <- abs(greta_stable - target_stable)
  expect_true(all(difference < test_tol))

  greta_states <- calculate(states)[[1]]
  difference <- abs(greta_states - target_states)
  expect_true(all(difference < test_tol))

  greta_converged <- calculate(converged)[[1]]
  expect_true(greta_converged == 1)

  greta_iterations <- calculate(iterations)[[1]]
  expect_lt(greta_iterations, niter)
})

test_that("vectorised matrix iteration works", {
  skip_if_not(greta:::check_tf_version())
  source("helpers.R")

  n <- 10
  n_mat <- 20
  mat <- randu(n_mat, n, n)
  init <- rep(1, n)
  niter <- 50
  tol <- 1e-6
  test_tol <- tol * 10
  mat_list <- lapply(seq_len(n_mat), function(i) mat[i, , ])

  # r version
  target_iterates <- lapply(mat_list,
    r_iterate_matrix,
    state = init,
    niter = niter,
    tol = tol
  )

  target_lambdas <- sapply(target_iterates, function(x) x$lambda)
  target_stable <- t(sapply(target_iterates, function(x) x$stable_distribution))

  iterates <- iterate_matrix(mat,
    initial_state = init,
    niter = niter,
    tol = tol
  )
  greta_lambdas <- calculate(iterates$lambda)[[1]]
  greta_stable <- calculate(iterates$stable_distribution)[[1]]
  dim(greta_stable) <- dim(greta_stable)[1:2]

  difference <- abs(greta_lambdas - target_lambdas)
  expect_true(all(difference < test_tol))

  difference <- abs(greta_stable - target_stable)
  expect_true(all(difference < test_tol))
})

test_that("vectorised initial_state iteration works", {
  skip_if_not(greta:::check_tf_version())
  source("helpers.R")

  n <- 10
  n_mat <- 20
  mat <- randu(n, n)
  init <- randu(n_mat, n, 1)
  niter <- 50
  tol <- 1e-6
  test_tol <- tol * 10
  init_list <- lapply(seq_len(n_mat), function(i) init[i, , ])

  # r version
  target_iterates <- lapply(
    init_list,
    function(init) {
      r_iterate_matrix(
        mat,
        init,
        niter = niter,
        tol = tol
      )
    }
  )

  target_lambdas <- sapply(target_iterates, function(x) x$lambda)
  target_stable <- t(sapply(target_iterates, function(x) x$stable_distribution))

  iterates <- iterate_matrix(mat,
    initial_state = init,
    niter = niter,
    tol = tol
  )
  greta_lambdas <- calculate(iterates$lambda)[[1]]
  greta_stable <- calculate(iterates$stable_distribution)[[1]]
  dim(greta_stable) <- dim(greta_stable)[1:2]

  difference <- abs(greta_lambdas - target_lambdas)
  expect_true(all(difference < test_tol))

  difference <- abs(greta_stable - target_stable)
  expect_true(all(difference < test_tol))
})

test_that("dynamics module errors informatively", {
  source("helpers.R")

  n <- 10
  m <- 3

  bad_mat <- randu(m, m + 1)
  bad_state <- randu(m, 2)
  bad_matrices1 <- randu(n, m, m, 1)
  bad_matrices2 <- randu(n, m, m - 1)

  good_mat <- randu(m, m)
  good_state <- randu(m, 1)
  good_states <- randu(n, m, 1)
  good_matrices <- randu(n, m, m)

  mismatched_state <- randu(m + 1, 1)

  # wrongly shaped matrix
  expect_error(
    iterate_matrix(
      matrix = bad_mat,
      initial_state = good_state
    ),
    "matrix must be a two-dimensional square greta array"
  )

  expect_error(
    iterate_matrix(
      matrix = bad_matrices1,
      initial_state = good_state
    ),
    "^matrix and state must be either two- or three-dimensional"
  )

  expect_error(
    iterate_matrix(
      matrix = bad_matrices1,
      initial_state = good_states
    ),
    "^matrix and state must be either two- or three-dimensional"
  )

  expect_error(
    iterate_matrix(
      matrix = bad_matrices2,
      initial_state = good_state
    ),
    "^each matrix must be a two-dimensional square greta array"
  )

  expect_error(
    iterate_matrix(
      matrix = bad_matrices2,
      initial_state = good_states
    ),
    "^each matrix must be a two-dimensional square greta array"
  )

  # wrongly shaped state
  expect_error(
    iterate_matrix(
      matrix = good_mat,
      initial_state = bad_state
    ),
    "initial_state must be either a column vector, or a 3D array"
  )

  expect_error(
    iterate_matrix(
      matrix = good_matrices,
      initial_state = bad_state
    ),
    "initial_state must be either a column vector, or a 3D array"
  )

  # mismatched matrix and state
  expect_error(
    iterate_matrix(
      matrix = good_mat,
      initial_state = mismatched_state
    ),
    "length of each initial_state must match the dimension"
  )

  expect_error(
    iterate_matrix(
      matrix = good_matrices,
      initial_state = mismatched_state
    ),
    "length of each initial_state must match the dimension"
  )
})

test_that("convergence tolerance works", {
  skip_if_not(greta:::check_tf_version())
  source("helpers.R")

  n <- 10
  niter <- 100

  # with an identity matrix it should converge instantly
  iterates <- iterate_matrix(matrix = diag(n))
  converged <- calculate(iterates$converged)[[1]]
  expect_true(converged == 1)

  # with tolerance of 0, it should time out
  iterates <- iterate_matrix(matrix = randu(n, n), tol = 0)
  converged <- calculate(iterates$converged)[[1]]
  expect_false(converged == 1)
})

test_that("iteration works in mcmc", {
  skip_if_not(greta:::check_tf_version())
  source("helpers.R")

  n <- 10
  n_site <- 30

  # non-batched case
  mat <- uniform(0, 1, dim = c(n, n))
  iterates <- iterate_matrix(matrix = mat)
  lambda <- iterates$lambda
  m <- model(lambda)
  draws <- mcmc(m, warmup = 100, n_samples = 100, verbose = FALSE)
  expect_s3_class(draws, "mcmc.list")

  # batched case
  mat <- uniform(0, 1, dim = c(n_site, n, n))
  iterates <- iterate_matrix(matrix = mat)
  lambda <- iterates$lambda
  m <- model(lambda)
  draws <- mcmc(m, warmup = 100, n_samples = 100, verbose = FALSE)
  expect_s3_class(draws, "mcmc.list")
})

test_that("iteration doesn't underflow", {
  skip_if_not(greta:::check_tf_version())

  mat <- matrix(0, 2, 2)
  mat[1, 2] <- 0.9
  states <- iterate_matrix(mat, niter = 3, tol = -Inf)
  lambda <- calculate(states$lambda)[[1]]
  stable <- calculate(states$stable_distribution)[[1]]
  expect_true(!is.na(lambda))
  expect_true(all(!is.na(stable)))
})

test_that("iteration doesn't overflow", {
  skip_if_not(greta:::check_tf_version())

  mat <- matrix(1e100, 2, 2)
  mat[1, 2] <- 1e200
  states <- iterate_matrix(mat, niter = 5, tol = -Inf)
  lambda <- calculate(states$lambda)[[1]]
  stable <- calculate(states$stable_distribution)[[1]]
  expect_true(!is.na(lambda))
  expect_true(all(!is.na(stable)))
})