Last updated on 2026-01-12 19:50:26 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.0.7 | 5.79 | 189.09 | 194.88 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.0.7 | 3.92 | 146.73 | 150.65 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.0.7 | 10.00 | 328.40 | 338.40 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 0.0.7 | 9.00 | 324.21 | 333.21 | ERROR | |
| r-devel-windows-x86_64 | 0.0.7 | 9.00 | 273.00 | 282.00 | OK | |
| r-patched-linux-x86_64 | 0.0.7 | 5.42 | 188.20 | 193.62 | OK | |
| r-release-linux-x86_64 | 0.0.7 | 4.77 | 195.06 | 199.83 | OK | |
| r-release-macos-arm64 | 0.0.7 | 1.00 | 56.00 | 57.00 | OK | |
| r-release-macos-x86_64 | 0.0.7 | 4.00 | 259.00 | 263.00 | OK | |
| r-release-windows-x86_64 | 0.0.7 | 7.00 | 280.00 | 287.00 | OK | |
| r-oldrel-macos-arm64 | 0.0.7 | 1.00 | 64.00 | 65.00 | OK | |
| r-oldrel-macos-x86_64 | 0.0.7 | 4.00 | 273.00 | 277.00 | OK | |
| r-oldrel-windows-x86_64 | 0.0.7 | 9.00 | 387.00 | 396.00 | OK |
Version: 0.0.7
Check: tests
Result: ERROR
Running ‘testthat.R’ [112s/117s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
> # https://github.com/Rdatatable/data.table/issues/5658
> Sys.setenv("OMP_THREAD_LIMIT" = 2)
> Sys.setenv("Ncpu" = 2)
>
> library(testthat)
> library(mllrnrs)
>
> test_check("mllrnrs")
CV fold: Fold1
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Saving _problems/test-binary-225.R
CV fold: Fold1
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold2
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold3
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
CV fold: Fold1
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold2
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold3
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
CV fold: Fold1
Saving _problems/test-regression-107.R
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold2
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold3
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Saving _problems/test-regression-309.R
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
[ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ]
══ Skipped tests (3) ═══════════════════════════════════════════════════════════
• On CRAN (3): 'test-binary.R:57:5', 'test-lints.R:10:5',
'test-multiclass.R:57:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-binary.R:225:5'): test nested cv, bayesian, binary - lightgbm ──
Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
Backtrace:
▆
1. └─lightgbm_optimizer$execute() at test-binary.R:225:5
2. └─mlexperiments:::.run_cv(self = self, private = private)
3. └─mlexperiments:::.fold_looper(self, private)
4. ├─base::do.call(private$cv_run_model, run_args)
5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
6. ├─base::do.call(.cv_run_nested_model, args)
7. └─mlexperiments (local) `<fn>`(...)
8. └─hparam_tuner$execute(k = self$k_tuning)
9. └─private$select_optimizer(self, private)
10. └─BayesianOptimizer$new(...)
11. └─mlexperiments (local) initialize(...)
── Error ('test-regression.R:107:5'): test nested cv, bayesian, regression - glmnet ──
Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
Backtrace:
▆
1. └─glmnet_optimizer$execute() at test-regression.R:107:5
2. └─mlexperiments:::.run_cv(self = self, private = private)
3. └─mlexperiments:::.fold_looper(self, private)
4. ├─base::do.call(private$cv_run_model, run_args)
5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
6. ├─base::do.call(.cv_run_nested_model, args)
7. └─mlexperiments (local) `<fn>`(...)
8. └─hparam_tuner$execute(k = self$k_tuning)
9. └─private$select_optimizer(self, private)
10. └─BayesianOptimizer$new(...)
11. └─mlexperiments (local) initialize(...)
── Error ('test-regression.R:309:5'): test nested cv, bayesian, reg:squarederror - xgboost ──
Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
Backtrace:
▆
1. └─xgboost_optimizer$execute() at test-regression.R:309:5
2. └─mlexperiments:::.run_cv(self = self, private = private)
3. └─mlexperiments:::.fold_looper(self, private)
4. ├─base::do.call(private$cv_run_model, run_args)
5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
6. ├─base::do.call(.cv_run_nested_model, args)
7. └─mlexperiments (local) `<fn>`(...)
8. └─hparam_tuner$execute(k = self$k_tuning)
9. └─private$select_optimizer(self, private)
10. └─BayesianOptimizer$new(...)
11. └─mlexperiments (local) initialize(...)
[ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc