CRAN Package Check Results for Package mlexperiments

Last updated on 2026-01-19 11:49:04 CET.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	0.0.8	10.92	207.23	218.15	ERROR
r-devel-linux-x86_64-debian-gcc	1.0.0	7.67	335.51	343.18	OK
r-devel-linux-x86_64-fedora-clang	1.0.0	21.00	860.61	881.61	OK
r-devel-windows-x86_64	0.0.8	12.00	415.00	427.00	OK
r-patched-linux-x86_64	0.0.8	13.05	203.99	217.04	ERROR
r-release-linux-x86_64	1.0.0	11.31	557.08	568.39	OK
r-release-macos-arm64	1.0.0	2.00	166.00	168.00	OK
r-release-macos-x86_64	1.0.0	8.00	444.00	452.00	OK
r-release-windows-x86_64	1.0.0	13.00	431.00	444.00	OK
r-oldrel-macos-arm64	1.0.0	2.00	159.00	161.00	OK
r-oldrel-macos-x86_64	1.0.0	8.00	821.00	829.00	OK
r-oldrel-windows-x86_64	1.0.0	15.00	492.00	507.00	OK

Check Details

Version: 0.0.8
Check: Rd cross-references
Result: NOTE Unknown package ‘ParBayesianOptimization’ in Rd xrefs Flavors: r-devel-linux-x86_64-debian-clang, r-patched-linux-x86_64

Version: 0.0.8
Check: tests
Result: ERROR Running ‘testthat.R’ [116s/118s] 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 > > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(mlexperiments) > > test_check("mlexperiments") CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold4 CV fold: Fold5 Testing for identical folds in 2 and 1. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Saving _problems/test-knn-115.R Saving _problems/test-knn-182.R CV fold: Fold1 Saving _problems/test-knn-257.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-rpart_classification-125.R Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification 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-rpart_classification-205.R CV fold: Fold1 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold2 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold3 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-rpart_regression-125.R 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-rpart_regression-203.R 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. [ FAIL 7 | WARN 0 | SKIP 1 | PASS 58 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-lints.R:10:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-knn.R:115:5'): test bayesian tuner, initGrid - knn ───────────── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute(k = 3) at test-knn.R:115:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-knn.R:182:5'): test bayesian tuner, initPoints - LearnerKnn ──── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute(k = 3) at test-knn.R:182:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-knn.R:257:5'): test nested cv, bayesian - knn ────────────────── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute() at test-knn.R:257:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<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-rpart_classification.R:125:5'): test bayesian tuner, initGrid, classification - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute(k = 3) at test-rpart_classification.R:125:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_classification.R:205:5'): test nested cv, bayesian, classification - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute() at test-rpart_classification.R:205:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_regression.R:125:5'): test bayesian tuner, initGrid, regression - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute(k = 3) at test-rpart_regression.R:125:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_regression.R:203:5'): test nested cv, bayesian, regression - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute() at test-rpart_regression.R:203:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<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 7 | WARN 0 | SKIP 1 | PASS 58 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.0.8
Check: tests
Result: ERROR Running ‘testthat.R’ [120s/146s] 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 > > Sys.setenv("OMP_THREAD_LIMIT" = 2) > Sys.setenv("Ncpu" = 2) > > library(testthat) > library(mlexperiments) > > test_check("mlexperiments") CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold4 CV fold: Fold5 Testing for identical folds in 2 and 1. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerGlm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold4 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold5 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Saving _problems/test-knn-115.R Saving _problems/test-knn-182.R CV fold: Fold1 Saving _problems/test-knn-257.R CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold2 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold3 Parameter 'ncores' is ignored for learner 'LearnerLm'. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-rpart_classification-125.R Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification 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-rpart_classification-205.R CV fold: Fold1 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold2 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold3 Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. Classification: using 'mean misclassification error' as optimization metric. CV fold: Fold1 CV fold: Fold2 CV fold: Fold3 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. Saving _problems/test-rpart_regression-125.R 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-rpart_regression-203.R 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. [ FAIL 7 | WARN 0 | SKIP 1 | PASS 58 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-lints.R:10:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-knn.R:115:5'): test bayesian tuner, initGrid - knn ───────────── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute(k = 3) at test-knn.R:115:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-knn.R:182:5'): test bayesian tuner, initPoints - LearnerKnn ──── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute(k = 3) at test-knn.R:182:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-knn.R:257:5'): test nested cv, bayesian - knn ────────────────── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─knn_optimization$execute() at test-knn.R:257:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<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-rpart_classification.R:125:5'): test bayesian tuner, initGrid, classification - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute(k = 3) at test-rpart_classification.R:125:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_classification.R:205:5'): test nested cv, bayesian, classification - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute() at test-rpart_classification.R:205:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<list>`) 6. ├─base::do.call(.cv_run_nested_model, args) 7. └─mlexperiments (local) `<fn>`(...) 8. └─hparam_tuner$execute(k = self$k_tuning) 9. └─private$select_optimizer(self, private) 10. └─BayesianOptimizer$new(...) 11. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_regression.R:125:5'): test bayesian tuner, initGrid, regression - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute(k = 3) at test-rpart_regression.R:125:5 2. └─private$select_optimizer(self, private) 3. └─BayesianOptimizer$new(...) 4. └─mlexperiments (local) initialize(...) ── Error ('test-rpart_regression.R:203:5'): test nested cv, bayesian, regression - rpart ── Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'. Backtrace: ▆ 1. └─rpart_optimization$execute() at test-rpart_regression.R:203:5 2. └─mlexperiments:::.run_cv(self = self, private = private) 3. └─mlexperiments:::.fold_looper(self, private) 4. ├─base::do.call(private$cv_run_model, run_args) 5. └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<list>`, fold_test = `<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 7 | WARN 0 | SKIP 1 | PASS 58 ] Error: ! Test failures. Execution halted Flavor: r-patched-linux-x86_64