source(testthat::test_path("make_example_data.R"))
source(testthat::test_path("test-helpers.R"))
test_that("factor encoded predictor", {
class_test <- recipe(x2 ~ ., data = ex_dat) %>%
step_lencode_glm(x3, outcome = vars(x2), id = "id") %>%
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$..value
)
})
test_that("character encoded predictor", {
class_test <- recipe(x2 ~ ., data = ex_dat_ch) %>%
step_lencode_glm(x3, outcome = vars(x2)) %>%
prep(training = ex_dat_ch, retain = TRUE)
tr_values <- bake(class_test, new_data = NULL)$x3
expect_snapshot(
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", {
reg_test <- recipe(x1 ~ ., data = ex_dat) %>%
step_lencode_glm(x3, outcome = vars(x1)) %>%
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$..value
)
})
test_that("character encoded predictor", {
reg_test <- recipe(x1 ~ ., data = ex_dat_ch) %>%
step_lencode_glm(x3, outcome = vars(x1)) %>%
prep(training = ex_dat_ch, retain = TRUE)
tr_values <- bake(reg_test, new_data = NULL)$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("bad args", {
three_class <- iris
three_class$fac <- rep(letters[1:3], 50)
three_class$logical <- rep(c(TRUE, FALSE), 75)
expect_snapshot(
error = TRUE,
recipe(Species ~ ., data = three_class) %>%
step_lencode_glm(Sepal.Length, outcome = vars(Species)) %>%
prep(training = three_class, retain = TRUE)
)
})
test_that("case weights", {
wts_int <- rep(c(0, 1), times = c(100, 400))
ex_dat_cw <- ex_dat %>%
mutate(wts = importance_weights(wts_int))
class_test <- recipe(x2 ~ ., data = ex_dat_cw) %>%
step_lencode_glm(x3, outcome = vars(x2), id = "id") %>%
prep(training = ex_dat_cw, retain = TRUE)
ref_mod <- glm(
x2 ~ 0 + x3,
data = ex_dat_cw,
family = binomial,
na.action = na.omit, weights = ex_dat_cw$wts
)
expect_equal(
-unname(coef(ref_mod)),
slice_head(class_test$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", {
rec <- recipe(x2 ~ ., data = ex_dat) %>%
step_lencode_glm(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_glm(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_glm(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_glm(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", {
rec <- recipe(x2 ~ ., data = ex_dat_ch) %>%
step_lencode_glm(x3, outcome = vars(x2))
expect_snapshot(print(rec))
expect_snapshot(prep(rec))
})
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