source(testthat::test_path("make_example_data.R")) source(testthat::test_path("test-helpers.R")) opts <- list(seed = 34677, chains = 2, iter = 500) omit_warning <- function(pattern) { function(x) { if (any(grepl(pattern, x))) { return(NULL) } x } } test_that("factor encoded predictor", { skip_on_cran() skip_if_not_installed("rstanarm") skip_if_not_installed("Matrix", "1.6-2") expect_snapshot( transform = omit_warning("^(The largest R-hat is|Bulk Effective|Tail Effective)"), { class_test <- recipe(x2 ~ ., data = ex_dat) %>% step_lencode_bayes(x3, outcome = vars(x2), verbose = FALSE, options = opts ) %>% prep(training = ex_dat, retain = TRUE) } ) tr_values <- bake(class_test, new_data = NULL)$x3 new_values <- bake(class_test, new_data = new_dat) expect_snapshot( new_values_ch <- bake(class_test, new_data = new_dat_ch) ) key <- class_test$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$x3) ) expect_true(sum(key$x3$..level == "..new") == 1) expect_true(is.numeric(tr_values)) expect_equal( new_values$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_ch$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_ch$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values_ch$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj$value, key$x3$..value ) }) test_that("character encoded predictor", { skip_on_cran() skip_if_not_installed("rstanarm") skip_if_not_installed("Matrix", "1.6-2") expect_snapshot( transform = omit_warning("^(The largest R-hat is|Bulk Effective|Tail Effective)"), class_test <- recipe(x2 ~ ., data = ex_dat_ch) %>% step_lencode_bayes(x3, outcome = vars(x2), verbose = FALSE, options = opts, id = "id" ) %>% prep( training = ex_dat_ch, retain = TRUE, options = opts ) ) tr_values <- bake(class_test, new_data = NULL)$x3 new_values <- bake(class_test, new_data = new_dat_ch) new_values_fc <- bake(class_test, new_data = new_dat) 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_true(sum(key$x3$..level == "..new") == 1) expect_true(is.numeric(tr_values)) expect_equal( new_values$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_fc$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_fc$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values_fc$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj$value, key$x3$..value ) }) test_that("factor encoded predictor", { skip_on_cran() skip_if_not_installed("rstanarm") skip_if_not_installed("Matrix", "1.6-2") expect_snapshot( transform = omit_warning("^(The largest R-hat is|Bulk Effective|Tail Effective)"), { set.seed(8283) reg_test <- recipe(x1 ~ ., data = ex_dat) %>% step_lencode_bayes(x3, outcome = vars(x1), verbose = FALSE, options = opts ) %>% prep(training = ex_dat, retain = TRUE) } ) tr_values <- bake(reg_test, new_data = NULL)$x3 new_values <- bake(reg_test, new_data = new_dat) expect_snapshot( new_values_ch <- bake(reg_test, new_data = new_dat_ch) ) td_obj <- tidy(reg_test, number = 1) key <- reg_test$steps[[1]]$mapping expect_equal("x3", names(key)) expect_equal( length(unique(ex_dat$x3)) + 1, nrow(key$x3) ) expect_true(sum(key$x3$..level == "..new") == 1) expect_true(is.numeric(tr_values)) expect_equal( new_values$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_ch$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_ch$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values_ch$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj$value, key$x3$..value ) }) test_that("character encoded predictor", { skip_on_cran() skip_if_not_installed("rstanarm") skip_if_not_installed("Matrix", "1.6-2") expect_snapshot( transform = omit_warning("^(The largest R-hat is|Bulk Effective|Tail Effective)"), { set.seed(8283) reg_test <- recipe(x1 ~ ., data = ex_dat_ch) %>% step_lencode_bayes(x3, outcome = vars(x1), verbose = FALSE, options = opts ) %>% prep(training = ex_dat_ch, retain = TRUE) } ) tr_values <- bake(reg_test, new_data = NULL)$x3 new_values <- bake(reg_test, new_data = new_dat_ch) new_values_fc <- bake(reg_test, new_data = new_dat) key <- reg_test$steps[[1]]$mapping td_obj <- tidy(reg_test, number = 1) expect_equal("x3", names(key)) expect_equal( length(unique(ex_dat$x3)) + 1, nrow(key$x3) ) expect_true(sum(key$x3$..level == "..new") == 1) expect_true(is.numeric(tr_values)) expect_equal( new_values$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_fc$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_fc$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values_fc$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj$value, key$x3$..value ) }) test_that("Works with passing family ", { skip_on_cran() skip_if_not_installed("rstanarm") skip_if_not_installed("Matrix", "1.6-2") ex_dat_poisson <- ex_dat %>% mutate(outcome = rpois(n(), 5)) expect_snapshot( transform = omit_warning("^(Bulk Effective|Tail Effective)"), { class_test <- recipe(outcome ~ ., data = ex_dat_poisson) %>% step_lencode_bayes(x3, outcome = vars(outcome), verbose = FALSE, options = c(opts, family = stats::poisson) ) %>% prep(training = ex_dat_poisson, retain = TRUE) } ) tr_values <- bake(class_test, new_data = NULL)$x3 new_values <- bake(class_test, new_data = new_dat) expect_snapshot( new_values_ch <- bake(class_test, new_data = new_dat_ch) ) key <- class_test$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$x3) ) expect_true(sum(key$x3$..level == "..new") == 1) expect_true(is.numeric(tr_values)) expect_equal( new_values$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_ch$x3[1], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( new_values_ch$x3[2], key$x3$..value[key$x3$..level == levels(ex_dat$x3)[1]] ) expect_equal( new_values_ch$x3[3], key$x3$..value[key$x3$..level == "..new"] ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj$value, key$x3$..value ) }) test_that("case weights", { skip_on_cran() skip_if_not_installed("rstanarm") skip_if_not_installed("Matrix", "1.6-2") wts_int <- rep(c(0, 1), times = c(100, 400)) ex_dat_cw <- ex_dat %>% mutate(wts = importance_weights(wts_int)) expect_snapshot( transform = omit_warning("^^(Bulk Effective|Tail Effective|The largest)"), { class_test <- recipe(x2 ~ ., data = ex_dat_cw) %>% step_lencode_bayes(x3, outcome = vars(x2), verbose = FALSE, options = opts ) %>% prep(training = ex_dat_cw, retain = TRUE) junk <- capture.output( ref_mod <- rstanarm::stan_glmer( formula = x2 ~ (1 | value), data = ex_dat_cw %>% transmute(value = x3, x2), family = binomial(), na.action = na.omit, seed = 34677, chains = 2, iter = 500, weights = wts_int, ) ) } ) expect_equal( -coef(ref_mod)$value[[1]], slice_head(class_test$steps[[1]]$mapping$x3, n = -1)$..value ) expect_snapshot(class_test) }) # Infrastructure --------------------------------------------------------------- test_that("bake method errors when needed non-standard role columns are missing", { skip_if_not_installed("rstanarm") skip_if_not_installed("Matrix", "1.6-2") rec <- recipe(x2 ~ ., data = ex_dat) %>% step_lencode_bayes(x3, outcome = vars(x2)) %>% update_role(x3, new_role = "potato") %>% update_role_requirements(role = "potato", bake = FALSE) rec_trained <- prep(rec, training = ex_dat, verbose = FALSE) expect_error( bake(rec_trained, new_data = ex_dat[, -3]), class = "new_data_missing_column" ) }) test_that("empty printing", { rec <- recipe(mpg ~ ., mtcars) rec <- step_lencode_bayes(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_lencode_bayes(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_lencode_bayes(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("printing", { skip_if_not_installed("rstanarm") skip_if_not_installed("Matrix", "1.6-2") rec <- recipe(x2 ~ ., data = ex_dat) %>% step_lencode_bayes(x3, outcome = vars(x2), verbose = FALSE, options = opts ) expect_snapshot(print(rec)) expect_snapshot( prep(rec), transform = omit_warning("^(Bulk Effective|Tail Effective|The largest)") ) })