rename objects in test xgboost

tidymodels · Dec 17, 2024 · df167a6 · df167a6
1 parent 1a7f1e6
commit df167a6
Showing 1 changed file with 26 additions and 26 deletions.
diff --git a/tests/testthat/test-model-xgboost.R b/tests/testthat/test-model-xgboost.R
@@ -5,14 +5,14 @@ test_that("boost_tree(), objective = binary:logistic, works with type = class",
 
 	mtcars$vs <- factor(mtcars$vs)
 
-	lr_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
+	bt_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
 
-	lr_fit <- parsnip::fit(lr_spec, vs ~ disp + mpg + hp, mtcars)
+	bt_fit <- parsnip::fit(bt_spec, vs ~ disp + mpg + hp, mtcars)
 
-	orb_obj <- orbital(lr_fit, type = "class")
+	orb_obj <- orbital(bt_fit, type = "class")
 
 	preds <- predict(orb_obj, mtcars)
-	exps <- predict(lr_fit, mtcars)
+	exps <- predict(bt_fit, mtcars)
 
 	expect_named(preds, ".pred_class")
 	expect_type(preds$.pred_class, "character")
@@ -28,14 +28,14 @@ test_that("boost_tree(), objective = binary:logistic, works with type = class",
 	skip_if_not_installed("tidypredict")
 	skip_if_not_installed("xgboost")
 
-	lr_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
+	bt_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
 
-	lr_fit <- parsnip::fit(lr_spec, Species ~ ., iris)
+	bt_fit <- parsnip::fit(bt_spec, Species ~ ., iris)
 
-	orb_obj <- orbital(lr_fit, type = "class")
+	orb_obj <- orbital(bt_fit, type = "class")
 
 	preds <- predict(orb_obj, iris)
-	exps <- predict(lr_fit, iris)
+	exps <- predict(bt_fit, iris)
 
 	expect_named(preds, ".pred_class")
 	expect_type(preds$.pred_class, "character")
@@ -53,14 +53,14 @@ test_that("boost_tree(), objective = binary:logistic, works with type = prob", {
 
 	mtcars$vs <- factor(mtcars$vs)
 
-	lr_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
+	bt_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
 
-	lr_fit <- parsnip::fit(lr_spec, vs ~ disp + mpg + hp, mtcars)
+	bt_fit <- parsnip::fit(bt_spec, vs ~ disp + mpg + hp, mtcars)
 
-	orb_obj <- orbital(lr_fit, type = "prob")
+	orb_obj <- orbital(bt_fit, type = "prob")
 
 	preds <- predict(orb_obj, mtcars)
-	exps <- predict(lr_fit, mtcars, type = "prob")
+	exps <- predict(bt_fit, mtcars, type = "prob")
 
 	expect_named(preds, c(".pred_0", ".pred_1"))
 	expect_type(preds$.pred_0, "double")
@@ -83,14 +83,14 @@ test_that("boost_tree(), objective = binary:logistic, works with type = prob", {
 	skip_if_not_installed("tidypredict")
 	skip_if_not_installed("xgboost")
 
-	lr_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
+	bt_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
 
-	lr_fit <- parsnip::fit(lr_spec, Species ~ ., iris)
+	bt_fit <- parsnip::fit(bt_spec, Species ~ ., iris)
 
-	orb_obj <- orbital(lr_fit, type = "prob")
+	orb_obj <- orbital(bt_fit, type = "prob")
 
 	preds <- predict(orb_obj, iris)
-	exps <- predict(lr_fit, iris, type = "prob")
+	exps <- predict(bt_fit, iris, type = "prob")
 
 	expect_named(preds, paste0(".pred_", levels(iris$Species)))
 	expect_type(preds$.pred_setosa, "double")
@@ -116,16 +116,16 @@ test_that("boost_tree(), objective = binary:logistic, works with type = c(class,
 
 	mtcars$vs <- factor(mtcars$vs)
 
-	lr_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
+	bt_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
 
-	lr_fit <- parsnip::fit(lr_spec, vs ~ disp + mpg + hp, mtcars)
+	bt_fit <- parsnip::fit(bt_spec, vs ~ disp + mpg + hp, mtcars)
 
-	orb_obj <- orbital(lr_fit, type = c("class", "prob"))
+	orb_obj <- orbital(bt_fit, type = c("class", "prob"))
 
 	preds <- predict(orb_obj, mtcars)
 	exps <- dplyr::bind_cols(
-		predict(lr_fit, mtcars, type = c("class")),
-		predict(lr_fit, mtcars, type = c("prob"))
+		predict(bt_fit, mtcars, type = c("class")),
+		predict(bt_fit, mtcars, type = c("prob"))
 	)
 
 	expect_named(preds, c(".pred_class", ".pred_0", ".pred_1"))
@@ -151,16 +151,16 @@ test_that("boost_tree(), objective = binary:logistic, works with type = c(class,
 	skip_if_not_installed("tidypredict")
 	skip_if_not_installed("xgboost")
 
-	lr_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
+	bt_spec <- parsnip::boost_tree(mode = "classification", engine = "xgboost")
 
-	lr_fit <- parsnip::fit(lr_spec, Species ~ ., iris)
+	bt_fit <- parsnip::fit(bt_spec, Species ~ ., iris)
 
-	orb_obj <- orbital(lr_fit, type = c("class", "prob"))
+	orb_obj <- orbital(bt_fit, type = c("class", "prob"))
 
 	preds <- predict(orb_obj, iris)
 	exps <- dplyr::bind_cols(
-		predict(lr_fit, iris, type = c("class")),
-		predict(lr_fit, iris, type = c("prob"))
+		predict(bt_fit, iris, type = c("class")),
+		predict(bt_fit, iris, type = c("prob"))
 	)
 
 	expect_named(preds, c(".pred_class", paste0(".pred_", levels(iris$Species))))