tests/testthat/test-lencode_bayes.R

In topepo/embed: Extra Recipes for Encoding Predictors

source(testthat::test_path("make_example_data.R"))
source(testthat::test_path("test-helpers.R"))

opts <- list(seed = 34677, chains = 2, iter = 500)

omit_warning <- function(pattern) {
  function(x) {
    if (any(grepl(pattern, x))) {
      return(NULL)
    }
    x
  }
}

test_that("factor encoded predictor", {
  skip_on_cran()
  skip_if_not_installed("rstanarm")
  skip_if_not_installed("Matrix", "1.6-2")

  expect_snapshot(
    transform = omit_warning("^(The largest R-hat is|Bulk Effective|Tail Effective)"),
    {
      class_test <- recipe(x2 ~ ., data = ex_dat) %>%
        step_lencode_bayes(x3,
          outcome = vars(x2),
          verbose = FALSE,
          options = opts
        ) %>%
        prep(training = ex_dat, retain = TRUE)
    }
  )
  tr_values <- bake(class_test, new_data = NULL)$x3
  new_values <- bake(class_test, new_data = new_dat)
  expect_snapshot(
    new_values_ch <- bake(class_test, new_data = new_dat_ch)
  )
  key <- class_test$steps[[1]]$mapping
  td_obj <- tidy(class_test, number = 1)

  expect_equal("x3", names(key))

  expect_equal(
    length(unique(ex_dat$x3)) + 1,
    nrow(key$x3)
  )
  expect_true(sum(key$x3$..level == "..new") == 1)

  expect_true(is.numeric(tr_values))

  expect_equal(
    new_values$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_ch$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_ch$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values_ch$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )

  expect_equal(
    td_obj$level,
    key$x3$..level
  )
  expect_equal(
    td_obj$value,
    key$x3$..value
  )
})

test_that("character encoded predictor", {
  skip_on_cran()
  skip_if_not_installed("rstanarm")
  skip_if_not_installed("Matrix", "1.6-2")
  
  expect_snapshot(
    transform = omit_warning("^(The largest R-hat is|Bulk Effective|Tail Effective)"),
    class_test <- recipe(x2 ~ ., data = ex_dat_ch) %>%
      step_lencode_bayes(x3,
        outcome = vars(x2),
        verbose = FALSE,
        options = opts,
        id = "id"
      ) %>%
      prep(
        training = ex_dat_ch, retain = TRUE,
        options = opts
      )
  )
  tr_values <- bake(class_test, new_data = NULL)$x3
  new_values <- bake(class_test, new_data = new_dat_ch)
  new_values_fc <- bake(class_test, new_data = new_dat)
  key <- class_test$steps[[1]]$mapping
  td_obj <- tidy(class_test, number = 1)

  expect_equal("x3", names(key))

  expect_equal(
    length(unique(ex_dat$x3)) + 1,
    nrow(key$x3)
  )
  expect_true(sum(key$x3$..level == "..new") == 1)

  expect_true(is.numeric(tr_values))

  expect_equal(
    new_values$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_fc$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_fc$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values_fc$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )

  expect_equal(
    td_obj$level,
    key$x3$..level
  )
  expect_equal(
    td_obj$value,
    key$x3$..value
  )
})

test_that("factor encoded predictor", {
  skip_on_cran()
  skip_if_not_installed("rstanarm")
  skip_if_not_installed("Matrix", "1.6-2")
  
  expect_snapshot(
    transform = omit_warning("^(The largest R-hat is|Bulk Effective|Tail Effective)"),
    {
      set.seed(8283)
      reg_test <- recipe(x1 ~ ., data = ex_dat) %>%
        step_lencode_bayes(x3,
          outcome = vars(x1),
          verbose = FALSE,
          options = opts
        ) %>%
        prep(training = ex_dat, retain = TRUE)
    }
  )

  tr_values <- bake(reg_test, new_data = NULL)$x3
  new_values <- bake(reg_test, new_data = new_dat)
  expect_snapshot(
    new_values_ch <- bake(reg_test, new_data = new_dat_ch)
  )
  td_obj <- tidy(reg_test, number = 1)

  key <- reg_test$steps[[1]]$mapping

  expect_equal("x3", names(key))

  expect_equal(
    length(unique(ex_dat$x3)) + 1,
    nrow(key$x3)
  )
  expect_true(sum(key$x3$..level == "..new") == 1)

  expect_true(is.numeric(tr_values))

  expect_equal(
    new_values$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_ch$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_ch$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values_ch$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )

  expect_equal(
    td_obj$level,
    key$x3$..level
  )
  expect_equal(
    td_obj$value,
    key$x3$..value
  )
})

test_that("character encoded predictor", {
  skip_on_cran()
  skip_if_not_installed("rstanarm")
  skip_if_not_installed("Matrix", "1.6-2")
  
  expect_snapshot(
    transform = omit_warning("^(The largest R-hat is|Bulk Effective|Tail Effective)"),
    {
      set.seed(8283)
      reg_test <- recipe(x1 ~ ., data = ex_dat_ch) %>%
        step_lencode_bayes(x3,
          outcome = vars(x1),
          verbose = FALSE,
          options = opts
        ) %>%
        prep(training = ex_dat_ch, retain = TRUE)
    }
  )

  tr_values <- bake(reg_test, new_data = NULL)$x3
  new_values <- bake(reg_test, new_data = new_dat_ch)
  new_values_fc <- bake(reg_test, new_data = new_dat)
  key <- reg_test$steps[[1]]$mapping
  td_obj <- tidy(reg_test, number = 1)

  expect_equal("x3", names(key))

  expect_equal(
    length(unique(ex_dat$x3)) + 1,
    nrow(key$x3)
  )
  expect_true(sum(key$x3$..level == "..new") == 1)

  expect_true(is.numeric(tr_values))

  expect_equal(
    new_values$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_fc$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_fc$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values_fc$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )

  expect_equal(
    td_obj$level,
    key$x3$..level
  )
  expect_equal(
    td_obj$value,
    key$x3$..value
  )
})

test_that("Works with passing family ", {
  skip_on_cran()
  skip_if_not_installed("rstanarm")
  skip_if_not_installed("Matrix", "1.6-2")

  ex_dat_poisson <- ex_dat %>%
    mutate(outcome = rpois(n(), 5))

  expect_snapshot(
    transform = omit_warning("^(Bulk Effective|Tail Effective)"),
    {
      class_test <- recipe(outcome ~ ., data = ex_dat_poisson) %>%
        step_lencode_bayes(x3,
          outcome = vars(outcome),
          verbose = FALSE,
          options = c(opts, family = stats::poisson)
        ) %>%
        prep(training = ex_dat_poisson, retain = TRUE)
    }
  )
  tr_values <- bake(class_test, new_data = NULL)$x3
  new_values <- bake(class_test, new_data = new_dat)
  expect_snapshot(
    new_values_ch <- bake(class_test, new_data = new_dat_ch)
  )
  key <- class_test$steps[[1]]$mapping
  td_obj <- tidy(class_test, number = 1)

  expect_equal("x3", names(key))

  expect_equal(
    length(unique(ex_dat$x3)) + 1,
    nrow(key$x3)
  )
  expect_true(sum(key$x3$..level == "..new") == 1)

  expect_true(is.numeric(tr_values))

  expect_equal(
    new_values$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_ch$x3[1],
    key$x3$..value[key$x3$..level == "..new"]
  )
  expect_equal(
    new_values_ch$x3[2],
    key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]]
  )
  expect_equal(
    new_values_ch$x3[3],
    key$x3$..value[key$x3$..level == "..new"]
  )

  expect_equal(
    td_obj$level,
    key$x3$..level
  )
  expect_equal(
    td_obj$value,
    key$x3$..value
  )
})

test_that("case weights", {
  skip_on_cran()
  skip_if_not_installed("rstanarm")
  skip_if_not_installed("Matrix", "1.6-2")

  wts_int <- rep(c(0, 1), times = c(100, 400))

  ex_dat_cw <- ex_dat %>%
    mutate(wts = importance_weights(wts_int))

  expect_snapshot(
    transform = omit_warning("^^(Bulk Effective|Tail Effective|The largest)"),
    {
      class_test <- recipe(x2 ~ ., data = ex_dat_cw) %>%
        step_lencode_bayes(x3,
          outcome = vars(x2),
          verbose = FALSE,
          options = opts
        ) %>%
        prep(training = ex_dat_cw, retain = TRUE)

      junk <- capture.output(
        ref_mod <- rstanarm::stan_glmer(
          formula = x2 ~ (1 | value),
          data = ex_dat_cw %>% transmute(value = x3, x2),
          family = binomial(),
          na.action = na.omit,
          seed = 34677,
          chains = 2,
          iter = 500,
          weights = wts_int,
        )
      )
    }
  )

  expect_equal(
    -coef(ref_mod)$value[[1]],
    slice_head(class_test$steps[[1]]$mapping$x3, n = -1)$..value
  )

  expect_snapshot(class_test)
})

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

test_that("bake method errors when needed non-standard role columns are missing", {
  skip_if_not_installed("rstanarm")
  skip_if_not_installed("Matrix", "1.6-2")
  
  rec <- recipe(x2 ~ ., data = ex_dat) %>%
    step_lencode_bayes(x3, outcome = vars(x2)) %>%
    update_role(x3, 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[, -3]),
    class = "new_data_missing_column"
  )
})

test_that("empty printing", {
  rec <- recipe(mpg ~ ., mtcars)
  rec <- step_lencode_bayes(rec, outcome = vars(mpg))
  
  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_lencode_bayes(rec1, outcome = vars(mpg))
  
  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_lencode_bayes(rec, outcome = vars(mpg))
  
  expect <- tibble(
    terms = character(),
    level = 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", {
  skip_if_not_installed("rstanarm")
  skip_if_not_installed("Matrix", "1.6-2")
  
  rec <- recipe(x2 ~ ., data = ex_dat) %>%
    step_lencode_bayes(x3,
                       outcome = vars(x2),
                       verbose = FALSE,
                       options = opts
    )
  
  expect_snapshot(print(rec))
  expect_snapshot(
    prep(rec),
    transform = omit_warning("^(Bulk Effective|Tail Effective|The largest)")
  )
})