Error in `initialize(...)`: Can't connect to Redis. Check the configuration.

terminator = trm("evals", n_evals = 20),
learner = lrn("classif.debug", validate = 0.2, early_stopping = TRUE, x = to_tune(0.2, 0.3),
iter = to_tune(upper = 1000, internal = TRUE, aggr = function(x) 99))
replicate(list(list(iter = 99)), n = length(instance$archive$data$internal_tuned_values))
iter = to_tune(upper = 1000L, internal = TRUE, aggr = function(x) as.integer(ceiling(mean(unlist(x))) + 2000L)),
# this ensures we get a pareto front that contains all values
m2 = msr("classif.acc", id = "classif.acc2")
expect_true(all(map_lgl(ti$result_learner_param_vals, function(x) x$iter >= 2000L)))
test_that("internal tuning: error is thrown on incorrect configuration", {
learner = lrn("classif.debug", iter = to_tune(upper = 1000, internal = TRUE)),
test_that("internal tuning: error message when primary search space is empty", {
learner = lrn("classif.debug", iter = to_tune(upper = 1000, internal = TRUE), early_stopping = TRUE, validate = 0.2),

"internal tuning: error is thrown on incorrect configuration"
"internal tuning: error message when primary search space is empty"

internal tuning: error message when primary search space is empty

Error in `redis_connect_tcp(config$host, config$port, config$timeout)`: Failed to create context: Unknown error

at = auto_tuner(tuner = tnr("random_search", batch_size = 10), learner = learner, resampling = rsmp ("holdout"),
measure = msr("classif.ce"), term_evals = 50)
measure = msr("classif.ce"), term_time = 50)
measure = msr("classif.ce"), term_evals = 10, term_time = 50)
learner = lrn("classif.rpart", cp = to_tune(0.01, 0.1)),
test_that("async auto tuner works with rush controller", {
rush = rsh(network_id = "tuning_network")

"async auto tuner works with rush controller"

async auto tuner works with rush controller

Error in `rush_plan(n_workers = 2)`: Can't connect to Redis. Check the configuration.

tuner =  tnr("random_search", batch_size = 2),
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
callbacks = clbk("mlr3tuning.backup", path = file)
test_that("backup callback works with standalone tuner", {
tuner =  tnr("grid_search", batch_size = 2),
test_that("async measures callback works", {
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1), predict_sets = "test"),
callbacks = clbk("mlr3tuning.async_measures", measures = list(msr("classif.ce", predict_sets = "test", id = "classif.ce_holdout"))))
# test_that("rush mlflow callback works", {
#     resampling = rsmp("cv", folds = 3),
#     terminator = trm("evals", n_evals = 20),
#     callbacks = clbk("mlr3tuning.async_mlflow", tracking_uri = "http://localhost:8080")
test_that("default configuration callback works", {
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1)),
test_that("default configuration callback works with logscale", {
test_that("default configuration callback errors with trafo", {
learner = lrn("classif.rpart", cp = to_tune(p_dbl(-10, 0, trafo = function(x) 10^x))),
expect_error(tuner$optimize(instance), "Cannot evaluate default hyperparameter values")
test_that("default configuration callback works without transformation and with logscale", {
test_that("default configuration callback errors without transformation and with logscale and trafo", {
minsplit = to_tune(p_int(0, 3, trafo = function(x) 2^x)))
test_that("default configuration callback errors with extra trafo", {
test_that("default configuration callback errors with old parameter set api", {
cp = p_dbl(lower = -10, upper = 0, trafo = function(x) 10^x)
test_that("batch default configuration callback  works", {
learner = lrn("classif.rpart", cp = to_tune(1e-3, 1))
test_that("batch default configuration callback  works with logscale", {
learner = lrn("classif.rpart", cp = to_tune(1e-3, 1, logscale = TRUE))
test_that("batch default configuration callback  errors with trafo", {
learner = lrn("classif.rpart", cp = to_tune(p_dbl(-10, 0, trafo = function(x) 10^x)))
), "Cannot evaluate default hyperparameter values")
test_that("batch default configuration callback  works without transformation and with logscale", {
test_that("batch default configuration callback  errors without transformation and with logscale and trafo", {
test_that("batch default configuration callback  errors with extra trafo", {
test_that("batch default configuration callback  errors with old parameter set api", {
test_that("async save logs callback works", {
learner = lrn("classif.debug", message_train = 1, x = to_tune()),