[R-package] Added tests on use of force_col_wise and force_row_wise i…

…n training (#2719)

R-package/tests/testthat/test_basic.R

@@ -252,3 +252,46 @@ test_that("lgb.train() throws an informative error if 'valids' contains lgb.Data
     )
   }, regexp = "each element of valids must have a name")
 })
+
+test_that("lgb.train() works with force_col_wise and force_row_wise", {
+  set.seed(1234L)
+  nrounds <- 10L
+  dtrain <- lgb.Dataset(
+    train$data
+    , label = train$label
+  )
+  params <- list(
+    objective = "binary"
+    , metric = "binary_error"
+    , force_col_wise = TRUE
+  )
+  bst_colwise <- lgb.train(
+    params = params
+    , data = dtrain
+    , nrounds = nrounds
+  )
+
+  params <- list(
+    objective = "binary"
+    , metric = "binary_error"
+    , force_row_wise = TRUE
+  )
+  bst_row_wise <- lgb.train(
+    params = params
+    , data = dtrain
+    , nrounds = nrounds
+  )
+
+  expected_error <- 0.003070782
+  expect_equal(bst_colwise$eval_train()[[1L]][["value"]], expected_error)
+  expect_equal(bst_row_wise$eval_train()[[1L]][["value"]], expected_error)
+
+  # check some basic details of the boosters just to be sure force_col_wise
+  # and force_row_wise are not causing any weird side effects
+  for (bst in list(bst_row_wise, bst_colwise)){
+    expect_equal(bst$current_iter(), nrounds)
+    parsed_model <- jsonlite::fromJSON(bst$dump_model())
+    expect_equal(parsed_model$objective, "binary sigmoid:1")
+    expect_false(parsed_model$average_output)
+  }
+})

R-package/tests/testthat/test_learning_to_rank.R

@@ -47,8 +47,8 @@ test_that("learning-to-rank with lgb.train() works as expected", {
     }
     expect_identical(sapply(eval_results, function(x) {x$name}), eval_names)
     expect_equal(eval_results[[1L]][["value"]], 0.825)
-    expect_true(abs(eval_results[[2L]][["value"]] - 0.795986) < TOLERANCE)
-    expect_true(abs(eval_results[[3L]][["value"]] - 0.7734639) < TOLERANCE)
+    expect_true(abs(eval_results[[2L]][["value"]] - 0.7766434) < TOLERANCE)
+    expect_true(abs(eval_results[[3L]][["value"]] - 0.7527939) < TOLERANCE)
 })
 
 test_that("learning-to-rank with lgb.cv() works as expected", {