tests/testthat/test-normalize.R

In cytominer: Methods for Image-Based Cell Profiling

context("normalize")

test_that("`standardize' standardizes data", {
  set.seed(123)

  generate_matrix <- function(cvec, svec) {
    n <- 30

    m <- matrix(runif(n * 2), n, 2) %>% scale(.)

    m %<>% sweep(., 2, svec, FUN = "*") %>% sweep(., 2, cvec, FUN = "+")

    m[1, 1] <- NA

    cbind(scale(m), m) %>% as.data.frame()
  }

  data <-
    dplyr::bind_rows(
      generate_matrix(rnorm(2), rnorm(2)^2) %>%
        dplyr::mutate(g1 = "a", g2 = "x"),
      generate_matrix(rnorm(2), rnorm(2)^2) %>%
        dplyr::mutate(g1 = "a", g2 = "y"),
      generate_matrix(rnorm(2), rnorm(2)^2) %>%
        dplyr::mutate(g1 = "b", g2 = "x"),
      generate_matrix(rnorm(2), rnorm(2)^2) %>%
        dplyr::mutate(g1 = "b", g2 = "y")
    )

  data %<>% dplyr::mutate(g3 = seq(nrow(data)))

  data_normalized <-
    data %>%
    dplyr::select(g1, g2, g3, V1, V2) %>%
    dplyr::rename(x = V1, y = V2)

  data <-
    data %>%
    dplyr::select(g1, g2, g3, V3, V4) %>%
    dplyr::rename(x = V3, y = V4)

  db <- DBI::dbConnect(RSQLite::SQLite(),
    ":memory:",
    loadable.extensions = TRUE
  )

  data <- dplyr::copy_to(db, data)

  expect_lt(
    mean(
      abs(
        normalize(
          population = data,
          variables = c("x", "y"),
          strata = c("g1", "g2"),
          sample = data,
          operation = "standardize"
        ) %>%
          dplyr::collect() %>%
          dplyr::arrange(g3) %>%
          dplyr::select(x, y) %>%
          as.matrix() -
          data_normalized %>%
          dplyr::arrange(g3) %>%
          dplyr::select(x, y) %>%
          as.matrix()
      ),
      na.rm = TRUE
    ),
    .Machine$double.eps * 1000000
  )

  # test after collecting so that data.frame -specific scale function is tested
  expect_lt(
    mean(
      abs(
        normalize(
          population = data %>% dplyr::collect(),
          variables = c("x", "y"),
          strata = c("g1", "g2"),
          sample = data,
          operation = "standardize"
        ) %>%
          dplyr::collect() %>%
          dplyr::arrange(g3) %>%
          dplyr::select(x, y) %>%
          as.matrix() -
          data_normalized %>%
          dplyr::arrange(g3) %>%
          dplyr::select(x, y) %>%
          as.matrix()
      ),
      na.rm = TRUE
    ),
    .Machine$double.eps * 1000000
  )

  expect_error(
    normalize(
      population = data %>% dplyr::collect(),
      variables = c("x", "y"),
      strata = c("g1", "g2"),
      sample = data,
      operation = "dummy"
    ),
    paste0("undefined operation 'dummy'")
  )

  DBI::dbDisconnect(db)
})


test_that("`robustize' standardizes data", {
  set.seed(123)

  generate_matrix <- function(cvec, svec) {
    n <- 30

    m <- matrix(runif(n * 2), n, 2) %>% scale(.)

    m %<>% sweep(., 2, svec, FUN = "*") %>% sweep(., 2, cvec, FUN = "+")

    m[1, 1] <- NA

    median_vec <- apply(m, 2, median, na.rm = TRUE)

    mad_vec <- apply(m, 2, mad, na.rm = TRUE)

    cbind(scale(m, median_vec, mad_vec), m) %>% as.data.frame()
  }

  data <-
    dplyr::bind_rows(
      generate_matrix(rnorm(2), rnorm(2)^2) %>%
        dplyr::mutate(g1 = "a", g2 = "x"),
      generate_matrix(rnorm(2), rnorm(2)^2) %>%
        dplyr::mutate(g1 = "a", g2 = "y"),
      generate_matrix(rnorm(2), rnorm(2)^2) %>%
        dplyr::mutate(g1 = "b", g2 = "x"),
      generate_matrix(rnorm(2), rnorm(2)^2) %>%
        dplyr::mutate(g1 = "b", g2 = "y")
    )

  data %<>% dplyr::mutate(g3 = seq(nrow(data)))

  data_normalized <-
    data %>%
    dplyr::select(g1, g2, g3, V1, V2) %>%
    dplyr::rename(x = V1, y = V2)

  data <-
    data %>%
    dplyr::select(g1, g2, g3, V3, V4) %>%
    dplyr::rename(x = V3, y = V4)

  db <- DBI::dbConnect(RSQLite::SQLite(),
    ":memory:",
    loadable.extensions = TRUE
  )

  data <- dplyr::copy_to(db, data)

  expect_lt(
    mean(
      abs(
        normalize(
          population = data,
          variables = c("x", "y"),
          strata = c("g1", "g2"),
          sample = data,
          operation = "robustize"
        ) %>%
          dplyr::collect() %>%
          dplyr::arrange(g3) %>%
          dplyr::select(x, y) %>%
          as.matrix() -
          data_normalized %>%
          dplyr::arrange(g3) %>%
          dplyr::select(x, y) %>%
          as.matrix()
      ),
      na.rm = TRUE
    ),
    .Machine$double.eps * 1000000
  )

  # test after collecting so that data.frame -specific scale function is tested
  expect_lt(
    mean(
      abs(
        normalize(
          population = data %>% dplyr::collect(),
          variables = c("x", "y"),
          strata = c("g1", "g2"),
          sample = data %>% dplyr::collect(),
          operation = "robustize"
        ) %>%
          dplyr::collect() %>%
          dplyr::arrange(g3) %>%
          dplyr::select(x, y) %>%
          as.matrix() -
          data_normalized %>%
          dplyr::arrange(g3) %>%
          dplyr::select(x, y) %>%
          as.matrix()
      ),
      na.rm = TRUE
    ),
    .Machine$double.eps * 1000000
  )

  DBI::dbDisconnect(db)
})