remove checks that were added to mlr3 (#271)

This was added in mlr3: mlr-org/mlr3@738b7bd

R/LearnerTorch.R

@@ -452,15 +452,6 @@ LearnerTorch = R6Class("LearnerTorch",
       # Ideally we could rely on state$train_task, but there is this complication
       # https://github.com/mlr-org/mlr3/issues/947
       param_vals$device = auto_device(param_vals$device)
-      if (!test_equal_col_info(ci_train, ci_predict)) { # nolint
-        stopf(paste0(
-          "Predict task's column info does not match the train task's column info.\n",
-          "This migth be handled more gracefully in the future.\n",
-          "Training column info:\n'%s'\n",
-          "Prediction column info:\n'%s'"),
-          paste0(capture.output(ci_train), collapse = "\n"),
-          paste0(capture.output(ci_predict), collapse = "\n"))
-      }
       private$.verify_predict_task(task, param_vals)
 
       with_torch_settings(seed = self$model$seed, num_threads = param_vals$num_threads, {

tests/testthat/test_LearnerTorch.R

@@ -457,24 +457,6 @@ test_that("Input verification works during `$train()` (train-predict shapes work
   expect_true(nrow(rr2$errors) == 0L)
 })
 
-test_that("Input verification works during `$predict()` (same column info, problematic fct -> int conversion)", {
-  task1 = as_task_classif(data.table(
-    y = factor(c("A", "B"))
-  ), target = "y", id = "test1")
-
-  task2 = as_task_classif(data.table(
-    y = factor(c("A", "B"), labels = c("B", "A"), levels = c("B", "A"))
-  ), target = "y", id = "test2")
-
-  learner = lrn("classif.torch_featureless", batch_size = 1L, epochs = 0L)
-
-  learner$train(task1)
-  expect_error(
-    learner$predict(task2),
-    "does not match"
-  )
-})
-
 test_that("col_info is propertly subset when comparing task validity during predict", {
   task = tsk("iris")$select("Sepal.Length")
   learner = classif_mlp2()

tests/testthat/test_learner_torch_methods.R

@@ -169,14 +169,3 @@ test_that("learner_torch_dataloader_predict works", {
   expect_false(dl$drop_last)
   expect_class(dl$batch_sampler$sampler, "utils_sampler_sequential")
 })
-
-test_that("wrong column info stops learner from prediction.", {
-  d1 = data.table(x = 1:10, y = 1:10)
-  d2 = data.table(x = runif(10), y = 1:10)
-  t1 = as_task_regr(d1, target = "y")
-  t2 = as_task_regr(d2, target = "y")
-
-  learner = lrn("regr.torch_featureless", epochs = 1, batch_size = 50)
-  learner$train(t1)
-  expect_error(learner$predict(t2), "more gracefully")
-})