library(testthat)
library(recipes)
skip_if_not_installed("modeldata")
data(biomass, package = "modeldata")
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr
)
# Note: some tests convert to data frame prior to testing
# https://github.com/tidyverse/dplyr/issues/2751
test_that("correct PCA values", {
pca_extract <- rec %>%
step_center(carbon, hydrogen, oxygen, nitrogen, sulfur) %>%
step_scale(carbon, hydrogen, oxygen, nitrogen, sulfur) %>%
step_pca(carbon, hydrogen, oxygen, nitrogen, sulfur,
options = list(retx = TRUE), id = ""
)
pca_extract_trained <- prep(pca_extract, training = biomass_tr, verbose = FALSE)
pca_pred <- bake(pca_extract_trained, new_data = biomass_te, all_predictors())
pca_pred <- as.matrix(pca_pred)
pca_exp <- prcomp(biomass_tr[, 3:7], center = TRUE, scale. = TRUE, retx = TRUE)
pca_pred_exp <- predict(pca_exp, biomass_te[, 3:7])[, 1:pca_extract$steps[[3]]$num_comp]
rownames(pca_pred) <- NULL
rownames(pca_pred_exp) <- NULL
expect_equal(pca_pred, pca_pred_exp)
tidy_exp_un <- tibble(
terms = c("carbon", "hydrogen", "oxygen", "nitrogen", "sulfur"),
value = rep(NA_real_, 5),
component = rep(NA_character_, 5),
id = ""
)
expect_equal(tidy_exp_un, tidy(pca_extract, number = 3))
pca_obj <- prcomp(
x = biomass_tr[, c("carbon", "hydrogen", "oxygen", "nitrogen", "sulfur")],
scale. = TRUE
)
variances <- pca_obj$sdev^2
pca_obj <- pca_obj$rotation
pca_obj <- as.data.frame(pca_obj)
pca_obj <- utils::stack(pca_obj)
tidy_exp_tr <- tibble(
terms = rep(tidy_exp_un$terms, pca_extract_trained$steps[[3]]$num_comp),
value = pca_obj$values,
component = as.character(pca_obj$ind),
id = ""
)
expect_equal(
as.data.frame(tidy_exp_tr),
as.data.frame(tidy(pca_extract_trained, number = 3))
)
var_obj <- tidy(pca_extract_trained, number = 3, type = "variance")
expect_equal(
var_obj$value[var_obj$terms == "variance"],
variances
)
expect_equal(
var_obj$value[var_obj$terms == "cumulative variance"],
cumsum(variances)
)
expect_equal(
var_obj$value[var_obj$terms == "percent variance"],
variances / sum(variances) * 100
)
expect_equal(
var_obj$value[var_obj$terms == "cumulative percent variance"],
cumsum(variances) / sum(variances) * 100
)
expect_snapshot(
error = TRUE,
tidy(pca_extract_trained, number = 3, type = "variances")
)
})
test_that("correct PCA values with threshold", {
pca_extract <- rec %>%
step_center(carbon, hydrogen, oxygen, nitrogen, sulfur) %>%
step_scale(carbon, hydrogen, oxygen, nitrogen, sulfur) %>%
step_pca(carbon, hydrogen, oxygen, nitrogen, sulfur, threshold = .5)
pca_extract_trained <- prep(pca_extract, training = biomass_tr, verbose = FALSE)
pca_exp <- prcomp(biomass_tr[, 3:7], center = TRUE, scale. = TRUE, retx = TRUE)
# cumsum(pca_exp$sdev^2)/sum(pca_exp$sdev^2)
expect_equal(pca_extract_trained$steps[[3]]$num_comp, 2)
})
test_that("Reduced rotation size", {
pca_extract <- rec %>%
step_center(carbon, hydrogen, oxygen, nitrogen, sulfur) %>%
step_scale(carbon, hydrogen, oxygen, nitrogen, sulfur) %>%
step_pca(carbon, hydrogen, oxygen, nitrogen, sulfur, num_comp = 3)
pca_extract_trained <- prep(pca_extract, training = biomass_tr, verbose = FALSE)
pca_pred <- bake(pca_extract_trained, new_data = biomass_te, all_predictors())
pca_pred <- as.matrix(pca_pred)
pca_exp <- prcomp(biomass_tr[, 3:7], center = TRUE, scale. = TRUE, retx = TRUE)
pca_pred_exp <- predict(pca_exp, biomass_te[, 3:7])[, 1:3]
rownames(pca_pred_exp) <- NULL
rownames(pca_pred) <- NULL
rownames(pca_pred_exp) <- NULL
expect_equal(pca_pred, pca_pred_exp)
})
test_that("No PCA comps", {
pca_extract <- rec %>%
step_pca(carbon, hydrogen, oxygen, nitrogen, sulfur, num_comp = 0)
pca_extract_trained <- prep(pca_extract, training = biomass_tr)
expect_equal(
names(bake(pca_extract_trained, new_data = NULL)),
names(biomass_tr)[-(1:2)]
)
expect_true(all(is.na(pca_extract_trained$steps[[1]]$res$rotation)))
expect_snapshot(print(pca_extract_trained))
expect_true(all(is.na(tidy(pca_extract_trained, 1)$value)))
})
test_that("backwards compatible with 0.1.17", {
pca_extract <- rec %>%
step_center(carbon, hydrogen, oxygen, nitrogen, sulfur) %>%
step_scale(carbon, hydrogen, oxygen, nitrogen, sulfur) %>%
step_pca(carbon, hydrogen, oxygen, nitrogen, sulfur,
options = list(retx = TRUE), id = ""
) %>%
prep()
exp_res <- bake(pca_extract, biomass_tr)
# Simulate what would have happened in 0.1.17
pca_extract$steps[[3]]$columns <- NULL
new_res <- bake(pca_extract, biomass_tr)
expect_equal(
exp_res,
new_res
)
expect_snapshot(pca_extract)
})
test_that("check_name() is used", {
dat <- mtcars
dat$PC1 <- dat$mpg
rec <- recipe(~ ., data = dat) %>%
step_pca(mpg, disp, vs)
expect_snapshot(
error = TRUE,
prep(rec, training = dat)
)
})
test_that("tunable", {
rec <-
recipe(~., data = iris) %>%
step_pca(all_predictors())
rec_param <- tunable.step_pca(rec$steps[[1]])
expect_equal(rec_param$name, c("num_comp", "threshold"))
expect_true(all(rec_param$source == "recipe"))
expect_true(is.list(rec_param$call_info))
expect_equal(nrow(rec_param), 2)
expect_equal(
names(rec_param),
c("name", "call_info", "source", "component", "component_id")
)
})
test_that("case weights", {
biomass_tr_cw <- biomass_tr %>%
mutate(nitrogen = frequency_weights(round(nitrogen))) %>%
select(HHV, carbon, hydrogen, oxygen, nitrogen, sulfur)
pca_extract <- recipe(HHV ~ .,
data = biomass_tr_cw) %>%
step_pca(all_numeric_predictors())
pca_extract_trained <- prep(pca_extract)
pca_pred <- bake(pca_extract_trained, new_data = biomass_te, all_predictors())
pca_pred <- as.matrix(pca_pred)
pca_exp <- pca_wts(biomass_tr[, c(3, 4, 5, 7)],
wts = as.numeric(biomass_tr_cw$nitrogen))
pca_pred_exp <- as.matrix(biomass_te[, c(3, 4, 5, 7)]) %*% pca_exp$rotation
rownames(pca_pred) <- NULL
rownames(pca_pred_exp) <- NULL
colnames(pca_pred) <- NULL
colnames(pca_pred_exp) <- NULL
expect_equal(pca_pred, pca_pred_exp)
expect_snapshot(pca_extract_trained)
# ----------------------------------------------------------------------------
biomass_tr_cw <- biomass_tr %>%
mutate(nitrogen = importance_weights(nitrogen)) %>%
select(HHV, carbon, hydrogen, oxygen, nitrogen, sulfur)
pca_extract <- recipe(HHV ~ .,
data = biomass_tr_cw) %>%
step_pca(all_numeric_predictors())
pca_extract_trained <- prep(pca_extract)
pca_pred <- bake(pca_extract_trained, new_data = biomass_te, all_predictors())
pca_pred <- as.matrix(pca_pred)
pca_exp <- prcomp(biomass_tr[, c(3, 4, 5, 7)], center = FALSE, scale. = FALSE, retx = FALSE)
pca_pred_exp <- predict(pca_exp, biomass_te[, c(3, 4, 5, 7)])[, 1:4]
rownames(pca_pred) <- NULL
rownames(pca_pred_exp) <- NULL
colnames(pca_pred) <- NULL
colnames(pca_pred_exp) <- NULL
expect_equal(pca_pred, pca_pred_exp)
expect_snapshot(pca_extract_trained)
})
test_that("Do nothing for num_comps = 0 and keep_original_cols = FALSE (#1152)", {
rec <- recipe(~ ., data = mtcars) %>%
step_pca(all_predictors(), num_comp = 0, keep_original_cols = FALSE) %>%
prep()
res <- bake(rec, new_data = NULL)
expect_identical(res, tibble::as_tibble(mtcars))
})
# Infrastructure ---------------------------------------------------------------
test_that("bake method errors when needed non-standard role columns are missing", {
pca_extract <- rec %>%
step_pca(carbon, hydrogen, oxygen, nitrogen, sulfur,
options = list(retx = TRUE), id = ""
) %>%
update_role(carbon, hydrogen, oxygen, nitrogen, sulfur, new_role = "potato") %>%
update_role_requirements(role = "potato", bake = FALSE)
pca_extract_trained <- prep(pca_extract, training = biomass_tr, verbose = FALSE)
expect_snapshot(
error = TRUE,
bake(pca_extract_trained, new_data = biomass_te[, c(-3)])
)
})
test_that("empty printing", {
rec <- recipe(mpg ~ ., mtcars)
rec <- step_pca(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_pca(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_pca(rec)
expect <- tibble(
terms = character(),
value = double(),
component = 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", {
new_names <- c("PC1")
rec <- recipe(~ mpg, mtcars) %>%
step_pca(all_predictors(), keep_original_cols = FALSE)
rec <- prep(rec)
res <- bake(rec, new_data = NULL)
expect_equal(
colnames(res),
new_names
)
rec <- recipe(~ mpg, mtcars) %>%
step_pca(all_predictors(), keep_original_cols = TRUE)
rec <- prep(rec)
res <- bake(rec, new_data = NULL)
expect_equal(
colnames(res),
c("mpg", new_names)
)
})
test_that("keep_original_cols - can prep recipes with it missing", {
rec <- recipe(~ mpg, mtcars) %>%
step_pca(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", {
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr) %>%
step_pca(carbon, hydrogen, oxygen, nitrogen, sulfur)
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_pca(
all_predictors(),
num_comp = hardhat::tune(), threshold = hardhat::tune()
)
params <- extract_parameter_set_dials(rec)
expect_s3_class(params, "parameters")
expect_identical(nrow(params), 2L)
})
test_that("bad args", {
expect_snapshot(
recipe(~ ., data = mtcars) %>%
step_pca(all_numeric_predictors(), num_comp = -1) %>%
prep(),
error = TRUE
)
expect_snapshot(
recipe(~ ., data = mtcars) %>%
step_pca(all_numeric_predictors(), prefix = 1) %>%
prep(),
error = TRUE
)
expect_snapshot(
recipe(~ ., data = mtcars) %>%
step_pca(all_numeric_predictors(), threshold = -1) %>%
prep(),
error = TRUE
)
})
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