Error: logical error. i is not a data.table, but 'on' argument is provided.

iter = to_tune(p_int(1, 4, tags = "budget"))
test_that("TunerBatchHyperband works with minimum budget > 1", {
iter = to_tune(p_int(2, 8, tags = "budget"))
iter = to_tune(p_int(1, 7, tags = "budget"))
test_that("TunerBatchHyperband works with eta = 2.5", {
iter = to_tune(p_int(1, 8, tags = "budget"))
test_that("TunerBatchHyperband works with xgboost", {
nrounds   = to_tune(p_int(1, 16, tags = "budget")),
eta       = to_tune(1e-4, 1, logscale = TRUE),
test_that("TunerBatchHyperband works with subsampling", {
graph_learner = as_learner(po("subsample") %>>% lrn("classif.debug"))
graph_learner$param_set$values$subsample.frac = to_tune(p_dbl(lower = 1/9, upper = 1, tags = "budget"))
test_that("TunerBatchHyperband works works with multi-crit", {
test_tuner_hyperband(eta = 2, learner, measures = msrs(c("classif.ce", "classif.acc")))
test_that("TunerBatchHyperband works with custom sampler", {
sampler = Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]] %??% learner$param_set$search_space()$subset("x"), function(n) rbeta(n, 2, 5))
test_tuner_hyperband(eta = 2, learner, sampler = sampler)
test_that("TunerBatchHyperband errors if not enough parameters are sampled", {
test_that("TunerBatchHyperband errors if budget parameter is sampled", {
Sampler1DRfun$new(learner$param_set$search_space()$params[["x"]] %??% learner$param_set$search_space()$subset("x"), function(n) rbeta(n, 2, 5)),
test_that("TunerBatchHyperband errors if budget parameter is not numeric", {
predict_missing_type = to_tune(p_fct(levels = c("na", "omit"), tags = "budget"))
test_that("TunerBatchHyperband errors if multiple budget parameters are set", {
x  = to_tune(p_dbl(0, 1, tags = "budget")),
iter = to_tune(p_int(1, 16, tags = "budget"))
instance = test_tuner_hyperband(eta = 2, learner, measures = msr("dummy", parameter_id = "x", minimize = TRUE))
expect_equal(min(instance$archive$data[bracket == 4 & stage == 0, dummy]),
instance$archive$data[bracket == 4 & stage == 4, dummy])
instance = test_tuner_hyperband(eta = 2, learner, measures = msr("dummy", parameter_id = "x", minimize = FALSE))
expect_equal(max(instance$archive$data[bracket == 4 & stage == 0, dummy]),
test_that("TunerBatchHyperband works with single budget value", {
iter = to_tune(p_int(1, 1, tags = "budget"))
test_that("TunerBatchHyperband works with repetitions", {
test_that("TunerBatchHyperband works with infinite repetitions", {