# Train objects are large so create on the fly
# test_that("train + butcher_example() works", {
# example_files <- butcher_example()
# expect_true("train.recipe.rda" %in% example_files)
# expect_true(file.exists(butcher_example("train.recipe.rda")))
# })
test_that("train + predict() works", {
skip_on_cran()
skip_if_not_installed("caret")
skip_if_not_installed("recipes")
# Load
suppressPackageStartupMessages(library(recipes))
suppressPackageStartupMessages(library(caret))
suppressPackageStartupMessages(library(modeldata))
# Data
data(biomass)
biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]
recipe <- biomass %>%
recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors()) %>%
step_spatialsign(all_predictors())
# Model
train.recipe_fit <- train(recipe, biomass_tr, method = "svmRadial", metric = "RMSE")
# Butcher
x <- butcher(train.recipe_fit)
# Testing data
expect_equal(x$call, rlang::expr(dummy_call()))
expect_equal(x$dots, list(NULL))
expect_equal(x$control, list(NULL))
expect_equal(x$trainingData, data.frame(NA))
expect_identical(attr(x$recipe$steps[[1]]$terms[[1]], ".Environment"), rlang::base_env())
expect_equal(x$pred, list(NULL))
expected_output <- predict(train.recipe_fit, newdata = biomass_te)
expect_equal(predict(x, biomass_te), expected_output)
})
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