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_snapshot(
error = TRUE,
bake(rec_trained, new_data = ex_dat[, -3])
)
})
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)")
)
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.