tests/testthat/test-YeoJohnson.R

In recipes: Preprocessing and Feature Engineering Steps for Modeling

library(testthat)
library(recipes)

n <- 20
set.seed(1)
ex_dat <- data.frame(
  x1 = exp(rnorm(n, mean = .1)),
  x2 = 1 / rnorm(n),
  x3 = rep(1:2, each = n / 2),
  x4 = rexp(n)
)

## from `car` package
exp_lambda <- c(
  x1 = -0.2727204451,
  x2 = 1.139292543,
  x3 = NA,
  x4 = -1.012702061
)
exp_dat <- structure(list(
  x1 = c(
    0.435993557749438, 0.754696454247318, 0.371327932207827,
    1.46113017436327, 0.82204097731098, 0.375761562702297, 0.89751975937422,
    1.02175936118846, 0.940739811377902, 0.54984302797741, 1.41856737837093,
    0.850587387615876, 0.437701618670981, 0.112174615510591, 1.21942112715274,
    0.654589551748501, 0.666780580127795, 1.12625135443351, 1.0636850911955,
    0.949680956411546
  ),
  x2 = c(
    1.15307873387121, 1.36532999080347,
    17.4648439780388, -0.487746797875704, 1.74452440065935, -13.3640721541574,
    -5.35805967319061, -0.653901985285932, -1.90735599477338, 2.65253432454371,
    0.76771137336975, -7.79484535687973, 2.87484976680907, -13.8738947581599,
    -0.696856395842167, -2.17745353101028, -2.28384276604207, -12.7261652971783,
    0.95585544349634, 1.40099012093008
  ),
  x3 = c(
    1L, 1L, 1L, 1L, 1L,
    1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
  ),
  x4 = c(
    0.49061104973894, 0.49670370366879, 0.338742419511653,
    0.663722100577351, 0.296260662322359, 0.681346128666408,
    0.757581280603711, 0.357148961119583, 0.371872889850153,
    0.49239057672598, 0.173259524331095, 0.235933290139909, 0.52297977893566,
    0.434927187456966, 0.0822501770191215, 0.523479652016858,
    0.197977570919824, 0.608108816144845, 0.821913792446345,
    0.300608495427594
  )
),
.Names = c("x1", "x2", "x3", "x4"),
row.names = c(
  NA,
  -20L
),
class = "data.frame"
)

test_that("simple YJ trans", {
  rec <- recipe(~., data = ex_dat) %>%
    step_YeoJohnson(x1, x2, x3, x4, id = "")

  yj_tibble_un <-
    tibble(
      terms = c("x1", "x2", "x3", "x4"),
      value = rep(na_dbl, 4),
      id = ""
    )
  expect_equal(yj_tibble_un, tidy(rec, number = 1))

  rec_trained <- prep(rec, training = ex_dat, verbose = FALSE)
  rec_trans <- bake(rec_trained, new_data = ex_dat)

  expect_equal(names(exp_lambda)[!is.na(exp_lambda)], names(rec_trained$steps[[1]]$lambdas))
  expect_equal(exp_lambda[!is.na(exp_lambda)], rec_trained$steps[[1]]$lambdas, tolerance = .001)
  expect_equal(as.matrix(exp_dat), as.matrix(rec_trans), tolerance = .05)
})

test_that("missing data", {
  ex_dat$x1[1] <- NA
  rec_true <- recipe(~., data = ex_dat) %>%
    step_YeoJohnson(x1, x2, x3, x4)

  expect_error(prep(rec_true, training = ex_dat, verbose = FALSE), NA)

  rec_false <- recipe(~., data = ex_dat) %>%
    step_YeoJohnson(x1, x2, x3, x4, na_rm = FALSE)

  expect_snapshot(error = TRUE,
    prep(rec_false, training = ex_dat, verbose = FALSE)
  )
})

# Infrastructure ---------------------------------------------------------------

test_that("bake method errors when needed non-standard role columns are missing", {
  rec <- recipe(~., data = ex_dat) %>%
    step_YeoJohnson(x1, x2, x3, x4, id = "") %>%
    update_role(x1, x2, x3, x4, new_role = "potato") %>%
    update_role_requirements(role = "potato", bake = FALSE)

  rec_trained <- prep(rec, training = ex_dat, verbose = FALSE)

  expect_error(bake(rec_trained, new_data = ex_dat[, 1:2]),
               class = "new_data_missing_column")
})

test_that("empty printing", {
  rec <- recipe(mpg ~ ., mtcars)
  rec <- step_YeoJohnson(rec)

  expect_snapshot(rec)

  rec <- prep(rec, mtcars)

  expect_snapshot(rec)
})

test_that("empty selection prep/bake is a no-op", {
  rec1 <- recipe(mpg ~ ., mtcars)
  rec2 <- step_YeoJohnson(rec1)

  rec1 <- prep(rec1, mtcars)
  rec2 <- prep(rec2, mtcars)

  baked1 <- bake(rec1, mtcars)
  baked2 <- bake(rec2, mtcars)

  expect_identical(baked1, baked2)
})

test_that("empty selection tidy method works", {
  rec <- recipe(mpg ~ ., mtcars)
  rec <- step_YeoJohnson(rec)

  expect <- tibble(terms = character(), value = double(), id = character())

  expect_identical(tidy(rec, number = 1), expect)

  rec <- prep(rec, mtcars)

  expect_identical(tidy(rec, number = 1), expect)
})

test_that("printing", {
  rec <- recipe(~., data = ex_dat) %>%
    step_YeoJohnson(x1, x2, x3, x4)

  expect_snapshot(print(rec))
  expect_snapshot(prep(rec))
})