add skip on CRAN for discretize_xgboost

tests/testthat/test-discretize_xgb.R

@@ -108,6 +108,8 @@ sim_tr_reg <- sim_data_reg(1000)
 sim_te_reg <- sim_data_reg(100)
 
 test_that("run_xgboost for classification", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   xgboost <- embed:::run_xgboost(
     xgb_credit_train,
     xgb_credit_test,
@@ -127,6 +129,8 @@ test_that("run_xgboost for classification", {
 })
 
 test_that("run_xgboost for multi-classification", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   xgboost <- embed:::run_xgboost(
     xgb_attrition_train,
     xgb_attrition_test,
@@ -146,6 +150,8 @@ test_that("run_xgboost for multi-classification", {
 })
 
 test_that("run_xgboost for regression", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   xgboost <- embed:::run_xgboost(
     xgb_ames_train,
     xgb_ames_test,
@@ -165,6 +171,8 @@ test_that("run_xgboost for regression", {
 })
 
 test_that("xgb_binning for classification", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   less_than_3.6 <- function() {
     utils::compareVersion("3.5.3", as.character(getRversion())) >= 0
   }
@@ -204,6 +212,8 @@ test_that("xgb_binning for classification", {
 })
 
 test_that("xgb_binning for multi-classification", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   less_than_3.6 <- function() {
     utils::compareVersion("3.5.3", as.character(getRversion())) >= 0
   }
@@ -242,6 +252,8 @@ test_that("xgb_binning for multi-classification", {
 })
 
 test_that("xgb_binning for regression", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   less_than_3.6 <- function() {
     utils::compareVersion("3.5.3", as.character(getRversion())) >= 0
   }
@@ -281,6 +293,8 @@ test_that("xgb_binning for regression", {
 })
 
 test_that("step_discretize_xgb for classification", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   set.seed(125)
   # General use
   xgb_rec <-
@@ -335,6 +349,8 @@ test_that("step_discretize_xgb for classification", {
 })
 
 test_that("step_discretize_xgb for multi-classification", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   set.seed(125)
   # General use
   xgb_rec <-
@@ -386,6 +402,8 @@ test_that("step_discretize_xgb for multi-classification", {
 })
 
 test_that("step_discretize_xgb for regression", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   # Skip on R < 3.6 since the rng is different.
   skip("Needs to determine why random numbers are different")
 
@@ -463,6 +481,8 @@ test_that("step_discretize_xgb for regression", {
 })
 
 test_that("xgb_binning() errors if only one class in outcome", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   const_outcome <- data.frame(
     outcome = factor(rep("a", 1000)),
     predictor = rep(1, 1000)
@@ -483,6 +503,8 @@ test_that("xgb_binning() errors if only one class in outcome", {
 })
 
 test_that("case weights step_discretize_xgb", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   sim_tr_cls_cw <- sim_tr_cls %>%
     mutate(weight = importance_weights(rep(1:0, each = 500)))
 
@@ -661,6 +683,8 @@ test_that("empty selection tidy method works", {
 })
 
 test_that("printing", {
+  skip_on_cran() # because data.table uses all cores by default 
+
   rec <- recipe(class ~ ., data = sim_tr_cls) %>%
     step_discretize_xgb(all_predictors(), outcome = "class")