[R-package] remove uses of testthat::context() in tests (#4915)

R-package/tests/testthat/test_Predictor.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("Predictor")
-
 test_that("Predictor$finalize() should not fail", {
     X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L)
     y <- iris[["Sepal.Length"]]

R-package/tests/testthat/test_basic.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("lightgbm()")
-
 ON_WINDOWS <- .Platform$OS.type == "windows"
 
 UTF8_LOCALE <- all(grepl(
@@ -326,9 +324,6 @@ test_that("lightgbm() does not write model to disk if save_name=NULL", {
   expect_equal(files_before, files_after)
 })
 
-
-context("training continuation")
-
 test_that("training continuation works", {
   dtrain <- lgb.Dataset(
     train$data
@@ -360,8 +355,6 @@ test_that("training continuation works", {
   expect_lt(abs(err_bst - err_bst2), 0.01)
 })
 
-context("lgb.cv()")
-
 test_that("cv works", {
   dtrain <- lgb.Dataset(train$data, label = train$label)
   params <- list(
@@ -593,8 +586,6 @@ test_that("lgb.cv() respects eval_train_metric argument", {
   )
 })
 
-context("lgb.train()")
-
 test_that("lgb.train() works as expected with multiple eval metrics", {
   metrics <- c("binary_error", "auc", "binary_logloss")
   bst <- lgb.train(
@@ -2139,8 +2130,6 @@ test_that("lgb.cv() updates params based on keyword arguments", {
 
 })
 
-context("linear learner")
-
 test_that("lgb.train() fit on linearly-relatead data improves when using linear learners", {
   set.seed(708L)
   .new_dataset <- function() {
@@ -2380,8 +2369,6 @@ test_that("lgb.train() works with linear learners when Dataset has categorical f
   expect_true(bst_lin_last_mse <  bst_last_mse)
 })
 
-context("interaction constraints")
-
 test_that("lgb.train() throws an informative error if interaction_constraints is not a list", {
   dtrain <- lgb.Dataset(train$data, label = train$label)
   params <- list(objective = "regression", interaction_constraints = "[1,2],[3]")
@@ -2482,8 +2469,6 @@ test_that(paste0("lgb.train() gives same results when using interaction_constrai
 
 })
 
-context("monotone constraints")
-
 .generate_trainset_for_monotone_constraints_tests <- function(x3_to_categorical) {
   n_samples <- 3000L
   x1_positively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0)

R-package/tests/testthat/test_custom_objective.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("Test models with custom objective")
-
 data(agaricus.train, package = "lightgbm")
 data(agaricus.test, package = "lightgbm")
 dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)

R-package/tests/testthat/test_dataset.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("testing lgb.Dataset functionality")
-
 data(agaricus.train, package = "lightgbm")
 train_data <- agaricus.train$data[seq_len(1000L), ]
 train_label <- agaricus.train$label[seq_len(1000L)]

R-package/tests/testthat/test_learning_to_rank.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("Learning to rank")
-
 # numerical tolerance to use when checking metric values
 TOLERANCE <- 1e-06
 

R-package/tests/testthat/test_lgb.Booster.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("Booster")
-
 ON_WINDOWS <- .Platform$OS.type == "windows"
 TOLERANCE <- 1e-6
 
@@ -31,8 +29,6 @@ test_that("Booster$finalize() should not fail", {
     expect_true(lgb.is.null.handle(bst$.__enclos_env__$private$handle))
 })
 
-context("lgb.get.eval.result")
-
 test_that("lgb.get.eval.result() should throw an informative error if booster is not an lgb.Booster", {
     bad_inputs <- list(
         matrix(1.0:10.0, 2L, 5L)
@@ -124,8 +120,6 @@ test_that("lgb.get.eval.result() should throw an informative error for incorrect
     }, regexp = "Only the following eval_names exist for dataset.*\\: \\[l2\\]", fixed = FALSE)
 })
 
-context("lgb.load()")
-
 test_that("lgb.load() gives the expected error messages given different incorrect inputs", {
     set.seed(708L)
     data(agaricus.train, package = "lightgbm")
@@ -394,8 +388,6 @@ test_that("If a string and a file are both passed to lgb.load() the file is used
     expect_identical(pred, pred2)
 })
 
-context("Booster")
-
 test_that("Creating a Booster from a Dataset should work", {
     set.seed(708L)
     data(agaricus.train, package = "lightgbm")
@@ -692,8 +684,6 @@ test_that("Booster$params should include dataset params, before and after Booste
     expect_identical(bst$params, expected_params)
 })
 
-context("save_model")
-
 test_that("Saving a model with different feature importance types works", {
     set.seed(708L)
     data(agaricus.train, package = "lightgbm")
@@ -1032,8 +1022,6 @@ test_that("lgb.cv() correctly handles passing through params to the model file",
 
 })
 
-context("saveRDS.lgb.Booster() and readRDS.lgb.Booster()")
-
 test_that("params (including dataset params) should be stored in .rds file for Booster", {
     data(agaricus.train, package = "lightgbm")
     dtrain <- lgb.Dataset(
@@ -1069,8 +1057,6 @@ test_that("params (including dataset params) should be stored in .rds file for B
     )
 })
 
-context("saveRDS and readRDS work on Booster")
-
 test_that("params (including dataset params) should be stored in .rds file for Booster", {
     data(agaricus.train, package = "lightgbm")
     dtrain <- lgb.Dataset(

R-package/tests/testthat/test_lgb.convert_with_rules.R

-context("lgb.convert_with_rules()")
-
 test_that("lgb.convert_with_rules() rejects inputs that are not a data.table or data.frame", {
     bad_inputs <- list(
         matrix(1.0:10.0, 2L, 5L)

R-package/tests/testthat/test_lgb.importance.R

-context("lgb.importance")
-
 test_that("lgb.importance() should reject bad inputs", {
     bad_inputs <- list(
         .Machine$integer.max

R-package/tests/testthat/test_lgb.interprete.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
     Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("lgb.interpete")
-
 .sigmoid <- function(x) {
     1.0 / (1.0 + exp(-x))
 }

R-package/tests/testthat/test_lgb.plot.importance.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
     Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("lgb.plot.importance()")
-
 test_that("lgb.plot.importance() should run without error for well-formed inputs", {
     data(agaricus.train, package = "lightgbm")
     train <- agaricus.train

R-package/tests/testthat/test_lgb.plot.interpretation.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
     Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("lgb.plot.interpretation")
-
 .sigmoid <- function(x) {
     1.0 / (1.0 + exp(-x))
 }

R-package/tests/testthat/test_lgb.unloader.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
     Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("lgb.unloader")
-
 test_that("lgb.unloader works as expected", {
     data(agaricus.train, package = "lightgbm")
     train <- agaricus.train

R-package/tests/testthat/test_metrics.R

-context(".METRICS_HIGHER_BETTER()")
-
 test_that(".METRICS_HIGHER_BETTER() should be well formed", {
     metrics <- .METRICS_HIGHER_BETTER()
     metric_names <- names(.METRICS_HIGHER_BETTER())

R-package/tests/testthat/test_parameters.R

-
-context("feature penalties")
-
 data(agaricus.train, package = "lightgbm")
 data(agaricus.test, package = "lightgbm")
 train <- agaricus.train
@@ -47,8 +44,6 @@ test_that("Feature penalties work properly", {
   expect_length(var_gain[[length(var_gain)]], 0L)
 })
 
-context("parameter aliases")
-
 test_that(".PARAMETER_ALIASES() returns a named list of character vectors, where names are unique", {
   param_aliases <- .PARAMETER_ALIASES()
   expect_identical(class(param_aliases), "list")

R-package/tests/testthat/test_utils.R

-context("lgb.params2str")
-
 test_that("lgb.params2str() works as expected for empty lists", {
     out_str <- lgb.params2str(
         params = list()
@@ -37,8 +35,6 @@ test_that("lgb.params2str() passes through duplicated params", {
     expect_equal(out_str, "objective=regression bagging_fraction=0.8 bagging_fraction=0.5")
 })
 
-context("lgb.check.eval")
-
 test_that("lgb.check.eval works as expected with no metric", {
     params <- lgb.check.eval(
         params = list(device = "cpu")
@@ -84,8 +80,6 @@ test_that("lgb.check.eval drops duplicate metrics and preserves order", {
     expect_identical(params[["metric"]], list("l1", "l2", "rmse"))
 })
 
-context("lgb.check.wrapper_param")
-
 test_that("lgb.check.wrapper_param() uses passed-in keyword arg if no alias found in params", {
     kwarg_val <- sample(seq_len(100L), size = 1L)
     params <- lgb.check.wrapper_param(

R-package/tests/testthat/test_weighted_loss.R

@@ -2,8 +2,6 @@ VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
-context("Case weights are respected")
-
 test_that("Gamma regression reacts on 'weight'", {
   n <- 100L
   set.seed(87L)