Nothing
library(testthat)
library(recipes)
dat <- data.frame(
dbl1 = rep(c(NA, 1), times = c(0, 100)),
dbl2 = rep(c(NA, 1), times = c(25, 75)),
dbl3 = rep(c(NA, 1), times = c(50, 50)),
dbl4 = rep(c(NA, 1), times = c(75, 25)),
dbl5 = rep(c(NA, 1), times = c(100, 0)),
chr1 = rep(c(NA, "A"), times = c(10, 90)),
chr2 = rep(c(NA, "A"), times = c(90, 10))
)
test_that("high filter", {
rec <- recipe(~., data = dat)
filtering <- rec %>%
step_filter_missing(all_predictors(), threshold = .2)
filtering_trained <- prep(filtering, training = dat, verbose = FALSE)
removed <- c("dbl2", "dbl3", "dbl4", "dbl5", "chr2")
expect_equal(filtering_trained$steps[[1]]$removals, removed)
})
test_that("low filter", {
rec <- recipe(~., data = dat)
filtering <- rec %>%
step_filter_missing(all_predictors(), threshold = 0.8)
filtering_trained <- prep(filtering, training = dat, verbose = FALSE)
expect_equal(filtering_trained$steps[[1]]$removals, c("dbl5", "chr2"))
})
test_that("Remove all columns with missing data", {
rec <- recipe(~., data = dat)
filtering <- rec %>%
step_filter_missing(all_predictors(), threshold = 0)
filtering_trained <- prep(filtering, training = dat, verbose = FALSE)
removed <- c("dbl2", "dbl3", "dbl4", "dbl5", "chr1", "chr2")
expect_equal(filtering_trained$steps[[1]]$removals, removed)
})
test_that("tunable", {
rec <-
recipe(~., data = iris) %>%
step_filter_missing(all_predictors())
rec_param <- tunable.step_filter_missing(rec$steps[[1]])
expect_equal(rec_param$name, c("threshold"))
expect_true(all(rec_param$source == "recipe"))
expect_true(is.list(rec_param$call_info))
expect_equal(nrow(rec_param), 1)
expect_equal(
names(rec_param),
c("name", "call_info", "source", "component", "component_id")
)
})
test_that("case weights", {
dat_cw <- dat %>%
mutate(wts = frequency_weights(rep(c(1, 0), c(20, 80))))
rec <- recipe(~., data = dat_cw)
filtering <- rec %>%
step_filter_missing(all_predictors(), threshold = .2)
filtering_trained <- prep(filtering)
removed <- c("dbl2", "dbl3", "dbl4", "dbl5", "chr1", "chr2")
expect_equal(filtering_trained$steps[[1]]$removals, removed)
expect_snapshot(filtering_trained)
# ----------------------------------------------------------------------------
dat_cw <- dat %>%
mutate(wts = importance_weights(rep(c(1, 0), c(20, 80))))
rec <- recipe(~., data = dat_cw)
filtering <- rec %>%
step_filter_missing(all_predictors(), threshold = .2)
filtering_trained <- prep(filtering)
removed <- c("dbl2", "dbl3", "dbl4", "dbl5", "chr2")
expect_equal(filtering_trained$steps[[1]]$removals, removed)
expect_snapshot(filtering_trained)
})
# Infrastructure ---------------------------------------------------------------
test_that("bake method errors when needed non-standard role columns are missing", {
# Here for completeness
# step_filter_missing() removes variables and thus does not care if they are
# not there.
expect_true(TRUE)
})
test_that("empty printing", {
rec <- recipe(mpg ~ ., mtcars)
rec <- step_filter_missing(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_filter_missing(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_filter_missing(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("printing", {
rec <- recipe(~., data = dat) %>%
step_filter_missing(all_predictors())
expect_snapshot(print(rec))
expect_snapshot(prep(rec))
})
test_that("tunable is setup to work with extract_parameter_set_dials", {
skip_if_not_installed("dials")
rec <- recipe(~., data = mtcars) %>%
step_filter_missing(
all_predictors(),
threshold = hardhat::tune()
)
params <- extract_parameter_set_dials(rec)
expect_s3_class(params, "parameters")
expect_identical(nrow(params), 1L)
})
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