source(testthat::test_path("make_example_data.R")) source(testthat::test_path("test-helpers.R")) # Uncomment to make stuff run on M1 # tensorflow::tf$config$get_visible_devices("CPU") %>% # tensorflow::tf$config$set_visible_devices() # Stops noisy tensorflow messages withr::local_envvar(TF_CPP_MIN_LOG_LEVEL = "2") test_that("factor encoded predictor", { skip_on_cran() skip_if(!is_tf_available()) class_test <- recipe(x2 ~ ., data = ex_dat) %>% step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0), id = "id") %>% prep(training = ex_dat, retain = TRUE) tr_values <- bake(class_test, new_data = NULL, contains("embed")) new_values <- bake(class_test, new_data = new_dat, contains("embed")) expect_snapshot( new_values_ch <- bake(class_test, new_data = new_dat_ch, contains("embed")) ) 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_equal(3, ncol(key$x3)) expect_true(sum(key$x3$..level == "..new") == 1) expect_true(all(vapply(tr_values, is.numeric, logical(1)))) expect_equal( new_values[1, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values[2, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values[3, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_ch[1, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_ch[2, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_ch[3, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj %>% select(contains("emb")) %>% setNames(letters[1:2]), key$x3 %>% select(contains("emb")) %>% setNames(letters[1:2]) ) }) test_that("character encoded predictor", { skip_on_cran() skip_if(!is_tf_available()) class_test <- recipe(x2 ~ ., data = ex_dat_ch) %>% step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0)) %>% prep(training = ex_dat_ch, retain = TRUE) tr_values <- bake(class_test, new_data = NULL, contains("embed")) new_values <- bake(class_test, new_data = new_dat, contains("embed")) new_values_fc <- bake(class_test, new_data = new_dat, contains("embed")) key <- class_test$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$x3) ) expect_equal(3, ncol(key$x3)) expect_true(sum(key$x3$..level == "..new") == 1) expect_true(all(vapply(tr_values, is.numeric, logical(1)))) expect_equal( new_values[1, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values[2, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values[3, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_fc[1, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_fc[2, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_fc[3, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj %>% select(contains("emb")) %>% setNames(letters[1:2]), key$x3 %>% select(contains("emb")) %>% setNames(letters[1:2]) ) }) test_that("factor encoded predictor", { skip_on_cran() skip_if(!is_tf_available()) class_test <- recipe(x1 ~ ., data = ex_dat) %>% step_embed(x3, outcome = vars(x1), options = embed_control(verbose = 0)) %>% prep(training = ex_dat, retain = TRUE) tr_values <- bake(class_test, new_data = NULL, contains("embed")) new_values <- bake(class_test, new_data = new_dat, contains("embed")) expect_snapshot( new_values_ch <- bake(class_test, new_data = new_dat_ch, contains("embed")) ) 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_equal(3, ncol(key$x3)) expect_true(sum(key$x3$..level == "..new") == 1) expect_true(all(vapply(tr_values, is.numeric, logical(1)))) expect_equal( new_values[1, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values[2, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values[3, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_ch[1, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_ch[2, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == levels(ex_dat$x3)[1], -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( new_values_ch[3, ] %>% setNames(letters[1:2]), key$x3[key$x3$..level == "..new", -3] %>% setNames(letters[1:2]), ignore_attr = TRUE ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj %>% select(contains("emb")) %>% setNames(letters[1:2]), key$x3 %>% select(contains("emb")) %>% setNames(letters[1:2]) ) }) test_that("character encoded predictor", { skip_on_cran() skip_if(!is_tf_available()) class_test <- recipe(x1 ~ ., data = ex_dat_ch) %>% step_embed(x3, outcome = vars(x1), num_terms = 5, options = embed_control(verbose = 0)) %>% prep(training = ex_dat_ch, retain = TRUE) tr_values <- bake(class_test, new_data = NULL, contains("embed")) new_values <- bake(class_test, new_data = new_dat, contains("embed")) new_values_fc <- bake(class_test, new_data = new_dat, contains("embed")) key <- class_test$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$x3) ) expect_equal(6, ncol(key$x3)) expect_true(sum(key$x3$..level == "..new") == 1) expect_true(all(vapply(tr_values, is.numeric, logical(1)))) expect_equal( new_values[1, ] %>% setNames(letters[1:5]), key$x3[key$x3$..level == "..new", -6] %>% setNames(letters[1:5]), ignore_attr = TRUE ) expect_equal( new_values[2, ] %>% setNames(letters[1:5]), key$x3[key$x3$..level == levels(ex_dat$x3)[1], -6] %>% setNames(letters[1:5]), ignore_attr = TRUE ) expect_equal( new_values[3, ] %>% setNames(letters[1:5]), key$x3[key$x3$..level == "..new", -6] %>% setNames(letters[1:5]), ignore_attr = TRUE ) expect_equal( new_values_fc[1, ] %>% setNames(letters[1:5]), key$x3[key$x3$..level == "..new", -6] %>% setNames(letters[1:5]), ignore_attr = TRUE ) expect_equal( new_values_fc[2, ] %>% setNames(letters[1:5]), key$x3[key$x3$..level == levels(ex_dat$x3)[1], -6] %>% setNames(letters[1:5]), ignore_attr = TRUE ) expect_equal( new_values_fc[3, ] %>% setNames(letters[1:5]), key$x3[key$x3$..level == "..new", -6] %>% setNames(letters[1:5]), ignore_attr = TRUE ) expect_equal( td_obj$level, key$x3$..level ) expect_equal( td_obj %>% select(contains("emb")) %>% setNames(letters[1:5]), key$x3 %>% select(contains("emb")) %>% setNames(letters[1:5]) ) }) test_that("bad args", { skip_on_cran() skip_if(!is_tf_available()) three_class <- iris three_class$fac <- rep(letters[1:3], 50) three_class$logical <- rep(c(TRUE, FALSE), 75) expect_snapshot( error = TRUE, recipe(Species ~ ., data = three_class) %>% step_embed(Sepal.Length, outcome = vars(Species)) %>% prep(training = three_class, retain = TRUE) ) }) test_that("check_name() is used", { skip_on_cran() skip_if(!is_tf_available()) dat <- ex_dat dat$x3_embed_1 <- dat$x3 rec <- recipe(~., data = dat) %>% step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0)) expect_snapshot( error = TRUE, prep(rec, training = dat) ) }) test_that("tunable", { rec <- recipe(~., data = mtcars) %>% step_embed(all_predictors(), outcome = "mpg") rec_param <- tunable.step_embed(rec$steps[[1]]) expect_equal(rec_param$name, c("num_terms", "hidden_units")) 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") ) }) # Infrastructure --------------------------------------------------------------- test_that("bake method errors when needed non-standard role columns are missing", { skip_on_cran() skip_if(!is_tf_available()) rec <- recipe(x2 ~ ., data = ex_dat) %>% step_embed( x3, outcome = vars(x2), options = embed_control(verbose = 0), id = "id" ) %>% 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", { skip_on_cran() skip_if(!is_tf_available()) rec <- recipe(mpg ~ ., mtcars) rec <- step_embed(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_embed(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_embed(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("keep_original_cols works", { skip_on_cran() skip_if(!is_tf_available()) new_names <- c("x2", "x3_embed_1", "x3_embed_2") rec <- recipe(x2 ~ x3, data = ex_dat) %>% step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0), keep_original_cols = FALSE) rec <- prep(rec) res <- bake(rec, new_data = NULL) expect_equal( colnames(res), new_names ) rec <- recipe(x2 ~ x3, data = ex_dat) %>% step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0), keep_original_cols = TRUE) rec <- prep(rec) res <- bake(rec, new_data = NULL) expect_equal( colnames(res), c("x3", new_names) ) }) test_that("keep_original_cols - can prep recipes with it missing", { skip_on_cran() skip_if(!is_tf_available()) rec <- recipe(x2 ~ x3, data = ex_dat) %>% step_embed(x3, outcome = vars(x2), options = embed_control(verbose = 0)) rec$steps[[1]]$keep_original_cols <- NULL expect_snapshot( rec <- prep(rec) ) expect_error( bake(rec, new_data = ex_dat), NA ) }) test_that("printing", { skip_on_cran() skip_if(!is_tf_available()) rec <- recipe(x2 ~ ., data = ex_dat_ch) %>% step_embed(x3, outcome = vars(x2)) 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_embed( all_predictors(), outcome = "mpg", num_terms = hardhat::tune(), hidden_units = hardhat::tune() ) params <- extract_parameter_set_dials(rec) expect_s3_class(params, "parameters") expect_identical(nrow(params), 2L) })