source(testthat::test_path("test-helpers.R"))
iris_dat <- iris
iris_dat[, 1:4] <- scale(iris_dat[, 1:4])
split <- seq.int(1, 150, by = 9)
tr <- iris_dat[-split, ]
te <- iris_dat[split, ]
test_that("factor outcome", {
skip_if_not_installed("irlba", "2.3.5.2")
set.seed(11)
supervised <-
recipe(Species ~ ., data = tr) %>%
step_umap(all_predictors(), outcome = vars(Species), num_comp = 2) %>%
prep(training = tr)
direct_mod <-
withr::with_seed(
supervised$steps[[1]]$seed[1],
uwot::umap(
X = tr[, 1:4],
y = tr$Species,
n_neighbors = 15,
n_components = 2,
learning_rate = 1,
min_dist = 0.01,
verbose = FALSE,
n_threads = 1,
ret_model = TRUE
)
)
expect_equal(
direct_mod$embedding,
supervised$steps[[1]]$object$embedding,
ignore_attr = TRUE
)
# predictions:
direct_pred <-
withr::with_seed(
supervised$steps[[1]]$seed[2],
uwot::umap_transform(model = direct_mod, X = te[, 1:4])
)
colnames(direct_pred) <- paste0("umap_", 1:2)
expect_equal(
direct_pred,
bake(supervised, new_data = te, composition = "matrix", all_predictors()),
ignore_attr = TRUE
)
})
test_that("numeric outcome", {
skip_if_not_installed("irlba", "2.3.5.2")
set.seed(11)
supervised <-
recipe(Sepal.Length ~ ., data = tr[, -5]) %>%
step_umap(all_predictors(), outcome = vars(Sepal.Length), num_comp = 2) %>%
prep(training = tr[, -5])
direct_mod <-
withr::with_seed(
supervised$steps[[1]]$seed[1],
uwot::umap(
X = tr[, 2:4],
y = tr$Sepal.Length,
n_neighbors = 15,
n_components = 2,
learning_rate = 1,
min_dist = 0.01,
verbose = FALSE,
n_threads = 1,
ret_model = TRUE
)
)
expect_equal(
direct_mod$embedding,
supervised$steps[[1]]$object$embedding,
ignore_attr = TRUE
)
# predictions:
direct_pred <-
withr::with_seed(
supervised$steps[[1]]$seed[2],
uwot::umap_transform(model = direct_mod, X = te[, 2:4])
)
colnames(direct_pred) <- paste0("umap_", 1:2)
expect_equal(
direct_pred,
bake(supervised, new_data = te[, -5], composition = "matrix", all_predictors()),
ignore_attr = TRUE
)
})
test_that("metric argument works", {
skip_if_not_installed("irlba", "2.3.5.2")
set.seed(11)
unsupervised <-
recipe(~., data = tr[, -5]) %>%
step_umap(
all_predictors(),
num_comp = 3, min_dist = .2, learn_rate = .2, metric = "hamming"
) %>%
prep(training = tr[, -5])
direct_mod <-
withr::with_seed(
unsupervised$steps[[1]]$seed[1],
uwot::umap(
X = tr[, -5],
n_neighbors = 15,
n_components = 3,
metric = "hamming",
learning_rate = .2,
min_dist = 0.2,
verbose = FALSE,
n_threads = 1,
ret_model = TRUE
)
)
expect_equal(
direct_mod$embedding,
unsupervised$steps[[1]]$object$embedding,
ignore_attr = TRUE
)
# predictions:
direct_pred <-
withr::with_seed(
unsupervised$steps[[1]]$seed[2],
uwot::umap_transform(model = direct_mod, X = te[, -5])
)
colnames(direct_pred) <- paste0("umap_", 1:3)
expect_equal(
direct_pred,
bake(unsupervised, new_data = te[, -5], composition = "matrix", all_predictors()),
ignore_attr = TRUE
)
})
test_that("no outcome", {
skip_if_not_installed("irlba", "2.3.5.2")
set.seed(11)
unsupervised <-
recipe(~., data = tr[, -5]) %>%
step_umap(all_predictors(), num_comp = 3, min_dist = .2, learn_rate = .2) %>%
prep(training = tr[, -5])
direct_mod <-
withr::with_seed(
unsupervised$steps[[1]]$seed[1],
uwot::umap(
X = tr[, -5],
n_neighbors = 15,
n_components = 3,
learning_rate = .2,
min_dist = 0.2,
verbose = FALSE,
n_threads = 1,
ret_model = TRUE
)
)
expect_equal(
direct_mod$embedding,
unsupervised$steps[[1]]$object$embedding,
ignore_attr = TRUE
)
# predictions:
direct_pred <-
withr::with_seed(
unsupervised$steps[[1]]$seed[2],
uwot::umap_transform(model = direct_mod, X = te[, -5])
)
colnames(direct_pred) <- paste0("umap_", 1:3)
expect_equal(
direct_pred,
bake(unsupervised, new_data = te[, -5], composition = "matrix", all_predictors()),
ignore_attr = TRUE
)
})
test_that("check_name() is used", {
skip_if_not_installed("irlba", "2.3.5.2")
dat <- tr
dat$UMAP1 <- dat$Species
rec <- recipe(Species ~ ., data = dat) %>%
step_umap(all_predictors(), num_comp = 2)
expect_snapshot(
error = TRUE,
prep(rec, training = dat)
)
})
test_that("tunable", {
rec <-
recipe(~., data = mtcars) %>%
step_umap(all_predictors())
rec_param <- tunable.step_umap(rec$steps[[1]])
expect_equal(
rec_param$name,
c("num_comp", "neighbors", "min_dist", "learn_rate", "epochs", "initial", "target_weight")
)
expect_true(all(rec_param$source == "recipe"))
expect_true(is.list(rec_param$call_info))
expect_equal(nrow(rec_param), 7L)
expect_equal(
names(rec_param),
c("name", "call_info", "source", "component", "component_id")
)
})
test_that("backwards compatible for initial and target_weight args (#213)", {
skip_if_not_installed("irlba", "2.3.5.2")
rec <- recipe(Species ~ ., data = tr) %>%
step_umap(all_predictors(), num_comp = 2)
exp_res <- prep(rec)
rec$steps[[1]]$initial <- NULL
rec$steps[[1]]$target_weight <- NULL
expect_identical(
prep(rec),
exp_res
)
})
# Infrastructure ---------------------------------------------------------------
test_that("bake method errors when needed non-standard role columns are missing", {
skip_if_not_installed("irlba", "2.3.5.2")
rec <- recipe(Species ~ ., data = tr) %>%
step_umap(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) %>%
update_role(Petal.Width, new_role = "potato") %>%
update_role_requirements(role = "potato", bake = FALSE)
rec_trained <- prep(rec, training = tr, verbose = FALSE)
expect_snapshot(
error = TRUE,
bake(rec_trained, new_data = tr[, -4])
)
})
test_that("empty printing", {
rec <- recipe(mpg ~ ., mtcars)
rec <- step_umap(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_umap(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_umap(rec)
expect <- tibble(terms = character(), 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_if_not_installed("irlba", "2.3.5.2")
new_names <- c("UMAP1", "UMAP2", "UMAP3")
rec <- recipe(~., data = tr[, -5]) %>%
step_umap(all_predictors(),
num_comp = 3, min_dist = .2, learn_rate = .2,
keep_original_cols = FALSE)
rec <- prep(rec)
res <- bake(rec, new_data = NULL)
expect_equal(
colnames(res),
new_names
)
rec <- recipe(~., data = tr[, -5]) %>%
step_umap(all_predictors(),
num_comp = 3, min_dist = .2, learn_rate = .2,
keep_original_cols = TRUE)
rec <- prep(rec)
res <- bake(rec, new_data = NULL)
expect_equal(
colnames(res),
c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", new_names)
)
})
test_that("keep_original_cols - can prep recipes with it missing", {
skip_if_not_installed("irlba", "2.3.5.2")
rec <- recipe(~ mpg, mtcars) %>%
step_umap(all_predictors())
rec$steps[[1]]$keep_original_cols <- NULL
expect_snapshot(
rec <- prep(rec)
)
expect_no_error(
bake(rec, new_data = mtcars)
)
})
test_that("printing", {
skip_if_not_installed("irlba", "2.3.5.2")
rec <- recipe(~., data = tr[, -5]) %>%
step_umap(all_predictors())
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_umap(
all_predictors(),
num_comp = hardhat::tune(),
neighbors = hardhat::tune(),
min_dist = hardhat::tune(),
learn_rate = hardhat::tune(),
epochs = hardhat::tune(),
initial = hardhat::tune(),
target_weight = hardhat::tune()
)
params <- extract_parameter_set_dials(rec)
expect_s3_class(params, "parameters")
expect_identical(nrow(params), 7L)
})
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