source(testthat::test_path("make_example_data.R"))
source(testthat::test_path("test-helpers.R"))
# Stops noisy tensorflow messages
withr::local_envvar(TF_CPP_MIN_LOG_LEVEL = "2")
test_that("factor encoded predictor", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
class_test <- recipe(x2 ~ ., data = ex_dat) %>%
step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0), id = "id") %>%
prep(training = ex_dat, retain = TRUE)
tr_values <- bake(class_test, new_data = NULL, contains("embed"))
new_values <- bake(class_test, new_data = new_dat, contains("embed"))
expect_snapshot(
new_values_ch <- bake(class_test, new_data = new_dat_ch, contains("embed"))
)
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_equal(3, ncol(key$x3))
expect_true(sum(key$x3$..level == "..new") == 1)
expect_true(all(vapply(tr_values, is.numeric, logical(1))))
expect_equal(
new_values[1, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values[2, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values[3, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_ch[1, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_ch[2, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_ch[3, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
td_obj$level,
key$x3$..level
)
expect_equal(
td_obj %>% select(contains("emb")) %>% setNames(letters[1:2]),
key$x3 %>% select(contains("emb")) %>% setNames(letters[1:2])
)
})
test_that("character encoded predictor", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
class_test <- recipe(x2 ~ ., data = ex_dat_ch) %>%
step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0)) %>%
prep(training = ex_dat_ch, retain = TRUE)
tr_values <- bake(class_test, new_data = NULL, contains("embed"))
new_values <- bake(class_test, new_data = new_dat, contains("embed"))
new_values_fc <- bake(class_test, new_data = new_dat, contains("embed"))
key <- class_test$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$x3)
)
expect_equal(3, ncol(key$x3))
expect_true(sum(key$x3$..level == "..new") == 1)
expect_true(all(vapply(tr_values, is.numeric, logical(1))))
expect_equal(
new_values[1, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values[2, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values[3, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_fc[1, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_fc[2, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_fc[3, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
td_obj$level,
key$x3$..level
)
expect_equal(
td_obj %>% select(contains("emb")) %>% setNames(letters[1:2]),
key$x3 %>% select(contains("emb")) %>% setNames(letters[1:2])
)
})
test_that("factor encoded predictor", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
class_test <- recipe(x1 ~ ., data = ex_dat) %>%
step_embed(x3, outcome = vars(x1), options = embed_control(verbose = 0)) %>%
prep(training = ex_dat, retain = TRUE)
tr_values <- bake(class_test, new_data = NULL, contains("embed"))
new_values <- bake(class_test, new_data = new_dat, contains("embed"))
expect_snapshot(
new_values_ch <- bake(class_test, new_data = new_dat_ch, contains("embed"))
)
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_equal(3, ncol(key$x3))
expect_true(sum(key$x3$..level == "..new") == 1)
expect_true(all(vapply(tr_values, is.numeric, logical(1))))
expect_equal(
new_values[1, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values[2, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values[3, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_ch[1, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_ch[2, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
new_values_ch[3, ] %>% setNames(letters[1:2]),
key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]),
ignore_attr = TRUE
)
expect_equal(
td_obj$level,
key$x3$..level
)
expect_equal(
td_obj %>% select(contains("emb")) %>% setNames(letters[1:2]),
key$x3 %>% select(contains("emb")) %>% setNames(letters[1:2])
)
})
test_that("character encoded predictor", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
class_test <- recipe(x1 ~ ., data = ex_dat_ch) %>%
step_embed(x3, outcome = vars(x1), num_terms = 5, options = embed_control(verbose = 0)) %>%
prep(training = ex_dat_ch, retain = TRUE)
tr_values <- bake(class_test, new_data = NULL, contains("embed"))
new_values <- bake(class_test, new_data = new_dat, contains("embed"))
new_values_fc <- bake(class_test, new_data = new_dat, contains("embed"))
key <- class_test$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$x3)
)
expect_equal(6, ncol(key$x3))
expect_true(sum(key$x3$..level == "..new") == 1)
expect_true(all(vapply(tr_values, is.numeric, logical(1))))
expect_equal(
new_values[1, ] %>% setNames(letters[1:5]),
key$x3[key$x3$..level == "..new", -6] %>% setNames(letters[1:5]),
ignore_attr = TRUE
)
expect_equal(
new_values[2, ] %>% setNames(letters[1:5]),
key$x3[key$x3$..level == levels(ex_dat$x3)[1], -6] %>% setNames(letters[1:5]),
ignore_attr = TRUE
)
expect_equal(
new_values[3, ] %>% setNames(letters[1:5]),
key$x3[key$x3$..level == "..new", -6] %>% setNames(letters[1:5]),
ignore_attr = TRUE
)
expect_equal(
new_values_fc[1, ] %>% setNames(letters[1:5]),
key$x3[key$x3$..level == "..new", -6] %>% setNames(letters[1:5]),
ignore_attr = TRUE
)
expect_equal(
new_values_fc[2, ] %>% setNames(letters[1:5]),
key$x3[key$x3$..level == levels(ex_dat$x3)[1], -6] %>% setNames(letters[1:5]),
ignore_attr = TRUE
)
expect_equal(
new_values_fc[3, ] %>% setNames(letters[1:5]),
key$x3[key$x3$..level == "..new", -6] %>% setNames(letters[1:5]),
ignore_attr = TRUE
)
expect_equal(
td_obj$level,
key$x3$..level
)
expect_equal(
td_obj %>% select(contains("emb")) %>% setNames(letters[1:5]),
key$x3 %>% select(contains("emb")) %>% setNames(letters[1:5])
)
})
test_that("bad args", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
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_embed(Sepal.Length, outcome = vars(Species)) %>%
prep(training = three_class, retain = TRUE)
)
})
test_that("check_name() is used", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
dat <- ex_dat
dat$x3_embed_1 <- dat$x3
rec <- recipe(~., data = dat) %>%
step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0))
expect_snapshot(
error = TRUE,
prep(rec, training = dat)
)
})
test_that("tunable", {
rec <-
recipe(~., data = mtcars) %>%
step_embed(all_predictors(), outcome = "mpg")
rec_param <- tunable.step_embed(rec$steps[[1]])
expect_equal(rec_param$name, c("num_terms", "hidden_units"))
expect_true(all(rec_param$source == "recipe"))
expect_true(is.list(rec_param$call_info))
expect_equal(nrow(rec_param), 2)
expect_equal(
names(rec_param),
c("name", "call_info", "source", "component", "component_id")
)
})
# Infrastructure ---------------------------------------------------------------
test_that("bake method errors when needed non-standard role columns are missing", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
rec <- recipe(x2 ~ ., data = ex_dat) %>%
step_embed(
x3,
outcome = vars(x2),
options = embed_control(verbose = 0),
id = "id"
) %>%
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", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
rec <- recipe(mpg ~ ., mtcars)
rec <- step_embed(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_embed(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_embed(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("keep_original_cols works", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
new_names <- c("x2", "x3_embed_1", "x3_embed_2")
rec <- recipe(x2 ~ x3, data = ex_dat) %>%
step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0),
keep_original_cols = FALSE)
rec <- prep(rec)
res <- bake(rec, new_data = NULL)
expect_equal(
colnames(res),
new_names
)
rec <- recipe(x2 ~ x3, data = ex_dat) %>%
step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0),
keep_original_cols = TRUE)
rec <- prep(rec)
res <- bake(rec, new_data = NULL)
expect_equal(
colnames(res),
c("x3", new_names)
)
})
test_that("keep_original_cols - can prep recipes with it missing", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
rec <- recipe(x2 ~ x3, data = ex_dat) %>%
step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0))
rec$steps[[1]]$keep_original_cols <- NULL
expect_snapshot(
rec <- prep(rec)
)
expect_no_error(
bake(rec, new_data = ex_dat)
)
})
test_that("printing", {
skip_on_cran()
skip_if_not_installed("keras")
skip_if(!embed:::is_tf_available())
rec <- recipe(x2 ~ ., data = ex_dat_ch) %>%
step_embed(x3, outcome = vars(x2))
expect_snapshot(print(rec))
expect_snapshot(prep(rec))
})
test_that("tunable is setup to works with extract_parameter_set_dials", {
skip_if_not_installed("dials")
rec <- recipe(~., data = mtcars) %>%
step_embed(
all_predictors(),
outcome = "mpg",
num_terms = hardhat::tune(),
hidden_units = hardhat::tune()
)
params <- extract_parameter_set_dials(rec)
expect_s3_class(params, "parameters")
expect_identical(nrow(params), 2L)
})
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