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test compute_grid_info() (#948)
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‎tests/testthat/test-grid_helpers.R

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test_that("compute_grid_info - recipe only", {
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library(workflows)
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library(recipes)
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library(parsnip)
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library(dials)
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rec <- recipe(mpg ~ ., mtcars) %>% step_spline_natural(deg_free = tune())
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wflow <- workflow()
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wflow <- add_model(wflow, boost_tree(mode = "regression"))
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wflow <- add_recipe(wflow, rec)
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grid <- grid_space_filling(extract_parameter_set_dials(wflow))
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res <- compute_grid_info(wflow, grid)
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expect_equal(res$.iter_preprocessor, 1:5)
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expect_equal(res$.msg_preprocessor, paste0("preprocessor ", 1:5, "/5"))
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expect_equal(res$deg_free, grid$deg_free)
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expect_equal(res$.iter_model, rep(1, 5))
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expect_equal(res$.iter_config, as.list(paste0("Preprocessor", 1:5, "_Model1")))
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expect_equal(res$.msg_model, paste0("preprocessor ", 1:5, "/5, model 1/1"))
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expect_equal(res$.submodels, list(list(), list(), list(), list(), list()))
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expect_named(
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res,
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c(".iter_preprocessor", ".msg_preprocessor", "deg_free", ".iter_model",
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".iter_config", ".msg_model", ".submodels"),
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ignore.order = TRUE
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)
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expect_equal(nrow(res), 5)
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})
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test_that("compute_grid_info - model only (no submodels)", {
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library(workflows)
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library(parsnip)
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library(dials)
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spec <- boost_tree(mode = "regression", learn_rate = tune())
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wflow <- workflow()
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wflow <- add_model(wflow, spec)
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wflow <- add_formula(wflow, mpg ~ .)
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grid <- grid_space_filling(extract_parameter_set_dials(wflow))
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res <- compute_grid_info(wflow, grid)
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expect_equal(res$.iter_preprocessor, rep(1, 5))
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expect_equal(res$.msg_preprocessor, rep("preprocessor 1/1", 5))
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expect_equal(res$learn_rate, grid$learn_rate)
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expect_equal(res$.iter_model, 1:5)
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expect_equal(res$.iter_config, as.list(paste0("Preprocessor1_Model", 1:5)))
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expect_equal(res$.msg_model, paste0("preprocessor 1/1, model ", 1:5, "/5"))
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expect_equal(res$.submodels, list(list(), list(), list(), list(), list()))
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expect_named(
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res,
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c(".iter_preprocessor", ".msg_preprocessor", "learn_rate", ".iter_model",
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".iter_config", ".msg_model", ".submodels"),
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ignore.order = TRUE
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)
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expect_equal(nrow(res), 5)
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})
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test_that("compute_grid_info - model only (with submodels)", {
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library(workflows)
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library(parsnip)
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library(dials)
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spec <- boost_tree(mode = "regression", trees = tune())
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wflow <- workflow()
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wflow <- add_model(wflow, spec)
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wflow <- add_formula(wflow, mpg ~ .)
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grid <- grid_space_filling(extract_parameter_set_dials(wflow))
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res <- compute_grid_info(wflow, grid)
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expect_equal(res$.iter_preprocessor, 1)
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expect_equal(res$.msg_preprocessor, "preprocessor 1/1")
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expect_equal(res$trees, max(grid$trees))
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expect_equal(res$.iter_model, 1)
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expect_equal(res$.iter_config, list(paste0("Preprocessor1_Model", 1:5)))
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expect_equal(res$.msg_model, "preprocessor 1/1, model 1/1")
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expect_equal(res$.submodels, list(list(trees = grid$trees[-which.max(grid$trees)])))
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expect_named(
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res,
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c(".iter_preprocessor", ".msg_preprocessor", "trees", ".iter_model",
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".iter_config", ".msg_model", ".submodels"),
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ignore.order = TRUE
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)
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expect_equal(nrow(res), 1)
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})
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test_that("compute_grid_info - recipe and model (no submodels)", {
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library(workflows)
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library(parsnip)
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library(recipes)
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library(dials)
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rec <- recipe(mpg ~ ., mtcars) %>% step_spline_natural(deg_free = tune())
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spec <- boost_tree(mode = "regression", learn_rate = tune())
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wflow <- workflow()
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wflow <- add_model(wflow, spec)
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wflow <- add_recipe(wflow, rec)
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grid <- grid_space_filling(extract_parameter_set_dials(wflow))
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res <- compute_grid_info(wflow, grid)
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expect_equal(res$.iter_preprocessor, 1:5)
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expect_equal(res$.msg_preprocessor, paste0("preprocessor ", 1:5, "/5"))
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expect_equal(res$learn_rate, grid$learn_rate)
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expect_equal(res$deg_free, grid$deg_free)
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expect_equal(res$.iter_model, rep(1, 5))
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expect_equal(res$.iter_config, as.list(paste0("Preprocessor", 1:5, "_Model1")))
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expect_equal(res$.msg_model, paste0("preprocessor ", 1:5, "/5, model 1/1"))
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expect_equal(res$.submodels, list(list(), list(), list(), list(), list()))
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expect_named(
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res,
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c(".iter_preprocessor", ".msg_preprocessor", "deg_free", "learn_rate",
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".iter_model", ".iter_config", ".msg_model", ".submodels"),
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ignore.order = TRUE
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)
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expect_equal(nrow(res), 5)
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})
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test_that("compute_grid_info - recipe and model (with submodels)", {
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library(workflows)
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library(parsnip)
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library(recipes)
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library(dials)
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rec <- recipe(mpg ~ ., mtcars) %>% step_spline_natural(deg_free = tune())
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spec <- boost_tree(mode = "regression", trees = tune())
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wflow <- workflow()
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wflow <- add_model(wflow, spec)
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wflow <- add_recipe(wflow, rec)
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# use grid_regular to trigger submodel trick
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set.seed(1)
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grid <- grid_regular(extract_parameter_set_dials(wflow))
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res <- compute_grid_info(wflow, grid)
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expect_equal(res$.iter_preprocessor, 1:3)
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expect_equal(res$.msg_preprocessor, paste0("preprocessor ", 1:3, "/3"))
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expect_equal(res$trees, rep(max(grid$trees), 3))
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expect_equal(res$.iter_model, rep(1, 3))
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expect_equal(
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res$.iter_config,
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list(
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c("Preprocessor1_Model1", "Preprocessor1_Model2", "Preprocessor1_Model3"),
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c("Preprocessor2_Model1", "Preprocessor2_Model2", "Preprocessor2_Model3"),
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c("Preprocessor3_Model1", "Preprocessor3_Model2", "Preprocessor3_Model3")
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)
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)
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expect_equal(res$.msg_model, paste0("preprocessor ", 1:3, "/3, model 1/1"))
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expect_equal(
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res$.submodels,
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list(
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list(trees = c(1L, 1000L)),
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list(trees = c(1L, 1000L)),
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list(trees = c(1L, 1000L))
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)
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)
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expect_named(
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res,
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c(".iter_preprocessor", ".msg_preprocessor", "deg_free", "trees",
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".iter_model", ".iter_config", ".msg_model", ".submodels"),
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ignore.order = TRUE
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)
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expect_equal(nrow(res), 3)
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})

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