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Re-add all tests
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‎tests/testthat/test-lightgbm.R

Lines changed: 102 additions & 102 deletions
Original file line numberDiff line numberDiff line change
@@ -15,105 +15,105 @@ test_that("simple test", {
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expect_s3_class(model, "lgb.CVBooster")
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})
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# test_that("lightgbm", {
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#
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# model <- parsnip::boost_tree(mtry = 1, trees = 50, tree_depth = 15, min_n = 1)
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#
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# expect_all_modes_works(model, 'lightgbm')
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# })
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#
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#
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# test_that('lightgbm alternate objective', {
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# skip_if_not_installed("lightgbm")
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#
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# spec <- boost_tree(mtry = 1, trees = 50, tree_depth = 15, min_n = 1) %>%
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# set_engine("lightgbm", objective = "huber") %>%
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# set_mode("regression")
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#
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# lgb_fit <- spec %>% fit(mpg ~ ., data = mtcars)
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#
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# info <- jsonlite::fromJSON(lightgbm::lgb.dump(lgb_fit$fit))
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#
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# expect_equal(info$objective, "huber")
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# })
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#
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# test_that("lightgbm with tune", {
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#
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# model <- parsnip::boost_tree(
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# mtry = 5,
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# learn_rate = tune(),
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# loss_reduction = tune(),
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# sample_size = tune(),
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# trees = tune(),
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# min_n = tune(),
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# tree_depth = tune()
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# )
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# model <- parsnip::set_engine(model, "lightgbm")
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#
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# expect_can_tune_boost_tree(model)
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#
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# })
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#
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#
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# test_that("lightgbm mtry", {
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#
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# hyperparameters <- data.frame(mtry = c(1, 2, 6))
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# for(i in 1:nrow(hyperparameters)) {
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# model <- parsnip::boost_tree(mtry = hyperparameters$mtry[i], min_n = 1)
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# expect_all_modes_works(model, 'lightgbm')
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# }
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#
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# })
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#
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# test_that("lightgbm trees", {
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#
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# hyperparameters <- data.frame(trees = c(1, 20, 300))
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# for(i in 1:nrow(hyperparameters)) {
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# model <- parsnip::boost_tree(trees = hyperparameters$trees[i], min_n = 1)
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# expect_all_modes_works(model, 'lightgbm')
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# }
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#
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# })
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#
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#
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# test_that("lightgbm min_n hyperparameter", {
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#
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# hyperparameters <- data.frame(min_n = c(1, 10))
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# for(i in 1:nrow(hyperparameters)) {
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# model <- parsnip::boost_tree(min_n = hyperparameters$min_n[i])
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# expect_all_modes_works(model, 'lightgbm')
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# }
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#
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# })
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#
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# test_that("lightgbm tree_depth", {
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# hyperparameters <- data.frame(tree_depth = c(1, 16))
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# for(i in 1:nrow(hyperparameters)) {
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# model <- parsnip::boost_tree(tree_depth = hyperparameters$tree_depth[i], min_n = 1)
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# expect_all_modes_works(model, 'lightgbm')
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# }
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# })
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#
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# test_that("lightgbm loss_reduction", {
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# hyperparameters <- data.frame(loss_reduction = c(0, 0.2, 2))
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# for(i in 1:nrow(hyperparameters)) {
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# model <- parsnip::boost_tree(loss_reduction = hyperparameters$loss_reduction[i], min_n = 1)
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# expect_all_modes_works(model, 'lightgbm')
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# }
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# })
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#
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# test_that("lightgbm tree_depth", {
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# hyperparameters <- data.frame(loss_reduction = c(0, 0.2, 2))
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# for(i in 1:nrow(hyperparameters)) {
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# model <- parsnip::boost_tree(loss_reduction = hyperparameters$loss_reduction[i], min_n = 1)
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# expect_all_modes_works(model, 'lightgbm')
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# }
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# })
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#
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# test_that("lightgbm multi_predict", {
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# model <- parsnip::boost_tree(mtry = 5, trees = 5, mode = "regression", min_n = 1)
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# model <- parsnip::set_engine(model, "lightgbm")
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#
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# expect_multi_predict_works(model)
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# })
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#
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test_that("lightgbm", {
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model <- parsnip::boost_tree(mtry = 1, trees = 50, tree_depth = 15, min_n = 1)
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expect_all_modes_works(model, 'lightgbm')
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})
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test_that('lightgbm alternate objective', {
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skip_if_not_installed("lightgbm")
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spec <- boost_tree(mtry = 1, trees = 50, tree_depth = 15, min_n = 1) %>%
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set_engine("lightgbm", objective = "huber") %>%
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set_mode("regression")
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lgb_fit <- spec %>% fit(mpg ~ ., data = mtcars)
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info <- jsonlite::fromJSON(lightgbm::lgb.dump(lgb_fit$fit))
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expect_equal(info$objective, "huber")
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})
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test_that("lightgbm with tune", {
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model <- parsnip::boost_tree(
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mtry = 5,
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learn_rate = tune(),
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loss_reduction = tune(),
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sample_size = tune(),
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trees = tune(),
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min_n = tune(),
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tree_depth = tune()
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)
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model <- parsnip::set_engine(model, "lightgbm")
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expect_can_tune_boost_tree(model)
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})
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test_that("lightgbm mtry", {
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hyperparameters <- data.frame(mtry = c(1, 2, 6))
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for(i in 1:nrow(hyperparameters)) {
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model <- parsnip::boost_tree(mtry = hyperparameters$mtry[i], min_n = 1)
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expect_all_modes_works(model, 'lightgbm')
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}
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})
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test_that("lightgbm trees", {
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hyperparameters <- data.frame(trees = c(1, 20, 300))
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for(i in 1:nrow(hyperparameters)) {
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model <- parsnip::boost_tree(trees = hyperparameters$trees[i], min_n = 1)
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expect_all_modes_works(model, 'lightgbm')
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}
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})
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test_that("lightgbm min_n hyperparameter", {
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hyperparameters <- data.frame(min_n = c(1, 10))
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for(i in 1:nrow(hyperparameters)) {
83+
model <- parsnip::boost_tree(min_n = hyperparameters$min_n[i])
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expect_all_modes_works(model, 'lightgbm')
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}
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})
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test_that("lightgbm tree_depth", {
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hyperparameters <- data.frame(tree_depth = c(1, 16))
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for(i in 1:nrow(hyperparameters)) {
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model <- parsnip::boost_tree(tree_depth = hyperparameters$tree_depth[i], min_n = 1)
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expect_all_modes_works(model, 'lightgbm')
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}
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})
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test_that("lightgbm loss_reduction", {
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hyperparameters <- data.frame(loss_reduction = c(0, 0.2, 2))
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for(i in 1:nrow(hyperparameters)) {
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model <- parsnip::boost_tree(loss_reduction = hyperparameters$loss_reduction[i], min_n = 1)
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expect_all_modes_works(model, 'lightgbm')
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}
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})
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test_that("lightgbm tree_depth", {
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hyperparameters <- data.frame(loss_reduction = c(0, 0.2, 2))
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for(i in 1:nrow(hyperparameters)) {
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model <- parsnip::boost_tree(loss_reduction = hyperparameters$loss_reduction[i], min_n = 1)
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expect_all_modes_works(model, 'lightgbm')
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}
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})
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test_that("lightgbm multi_predict", {
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model <- parsnip::boost_tree(mtry = 5, trees = 5, mode = "regression", min_n = 1)
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model <- parsnip::set_engine(model, "lightgbm")
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expect_multi_predict_works(model)
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})
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