Last updated on 2026-03-19 00:50:37 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.0.8 | 6.12 | 336.18 | 342.30 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.0.8 | 3.32 | 235.25 | 238.57 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.0.8 | 10.00 | 568.58 | 578.58 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 0.0.8 | 9.00 | 557.60 | 566.60 | OK | |
| r-devel-macos-arm64 | 0.0.8 | 1.00 | 89.00 | 90.00 | OK | |
| r-devel-windows-x86_64 | 0.0.8 | 7.00 | 328.00 | 335.00 | OK | |
| r-patched-linux-x86_64 | 0.0.8 | 5.58 | 336.99 | 342.57 | OK | |
| r-release-linux-x86_64 | 0.0.8 | 4.92 | 326.24 | 331.16 | OK | |
| r-release-macos-arm64 | 0.0.8 | 1.00 | 91.00 | 92.00 | OK | |
| r-release-macos-x86_64 | 0.0.8 | 4.00 | 283.00 | 287.00 | OK | |
| r-release-windows-x86_64 | 0.0.8 | 7.00 | 318.00 | 325.00 | OK | |
| r-oldrel-macos-arm64 | 0.0.8 | 1.00 | 91.00 | 92.00 | OK | |
| r-oldrel-macos-x86_64 | 0.0.8 | 4.00 | 393.00 | 397.00 | OK | |
| r-oldrel-windows-x86_64 | 0.0.8 | 9.00 | 429.00 | 438.00 | OK |
Version: 0.0.8
Check: tests
Result: ERROR
Running ‘testthat.R’ [144s/208s]
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 4 rows.
elapsed = 0.407 Round = 1 bagging_fraction = 0.6000 feature_fraction = 0.6000 min_data_in_leaf = 4.0000 learning_rate = 0.2000 num_leaves = 18.0000 Value = -0.4745914
elapsed = 0.196 Round = 2 bagging_fraction = 0.8000 feature_fraction = 1.0000 min_data_in_leaf = 10.0000 learning_rate = 0.2000 num_leaves = 6.0000 Value = -0.431546
elapsed = 0.052 Round = 3 bagging_fraction = 0.8000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 2.0000 Value = -0.4727251
elapsed = 0.148 Round = 4 bagging_fraction = 1.0000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 10.0000 Value = -0.4571104
elapsed = 0.091 Round = 5 bagging_fraction = 0.2466196 feature_fraction = 0.8656377 min_data_in_leaf = 7.0000 learning_rate = 0.1760741 num_leaves = 15.0000 Value = -0.4444332
elapsed = 0.258 Round = 6 bagging_fraction = 0.5481146 feature_fraction = 0.2547513 min_data_in_leaf = 9.0000 learning_rate = 0.1137251 num_leaves = 7.0000 Value = -0.5070443
Best Parameters Found:
Round = 2 bagging_fraction = 0.8000 feature_fraction = 1.0000 min_data_in_leaf = 10.0000 learning_rate = 0.2000 num_leaves = 6.0000 Value = -0.431546
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 0.852 Round = 1 bagging_fraction = 0.6000 feature_fraction = 0.6000 min_data_in_leaf = 4.0000 learning_rate = 0.2000 num_leaves = 18.0000 Value = -0.5316282
elapsed = 0.205 Round = 2 bagging_fraction = 0.8000 feature_fraction = 1.0000 min_data_in_leaf = 10.0000 learning_rate = 0.2000 num_leaves = 6.0000 Value = -0.4985911
elapsed = 0.104 Round = 3 bagging_fraction = 0.8000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 2.0000 Value = -0.505255
elapsed = 0.075 Round = 4 bagging_fraction = 1.0000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 10.0000 Value = -0.535165
elapsed = 0.106 Round = 5 bagging_fraction = 0.7832345 feature_fraction = 0.2154829 min_data_in_leaf = 7.0000 learning_rate = 0.1873565 num_leaves = 16.0000 Value = -0.5855704
elapsed = 0.10 Round = 6 bagging_fraction = 0.7998778 feature_fraction = 0.6535149 min_data_in_leaf = 5.0000 learning_rate = 0.1301516 num_leaves = 3.0000 Value = -0.4888063
Best Parameters Found:
Round = 6 bagging_fraction = 0.7998778 feature_fraction = 0.6535149 min_data_in_leaf = 5.0000 learning_rate = 0.1301516 num_leaves = 3.0000 Value = -0.4888063
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 0.123 Round = 1 bagging_fraction = 0.6000 feature_fraction = 0.6000 min_data_in_leaf = 4.0000 learning_rate = 0.2000 num_leaves = 18.0000 Value = -0.4832754
elapsed = 0.076 Round = 2 bagging_fraction = 0.8000 feature_fraction = 1.0000 min_data_in_leaf = 10.0000 learning_rate = 0.2000 num_leaves = 6.0000 Value = -0.4296056
elapsed = 0.151 Round = 3 bagging_fraction = 0.8000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 2.0000 Value = -0.4746145
elapsed = 0.129 Round = 4 bagging_fraction = 1.0000 feature_fraction = 0.8000 min_data_in_leaf = 4.0000 learning_rate = 0.1000 num_leaves = 10.0000 Value = -0.4447323
elapsed = 0.126 Round = 5 bagging_fraction = 0.5157127 feature_fraction = 0.9291875 min_data_in_leaf = 9.0000 learning_rate = 0.1998166 num_leaves = 6.0000 Value = -0.4283087
elapsed = 0.063 Round = 6 bagging_fraction = 0.7445644 feature_fraction = 0.2223738 min_data_in_leaf = 9.0000 learning_rate = 0.1282084 num_leaves = 6.0000 Value = -0.4834545
Best Parameters Found:
Round = 5 bagging_fraction = 0.5157127 feature_fraction = 0.9291875 min_data_in_leaf = 9.0000 learning_rate = 0.1998166 num_leaves = 6.0000 Value = -0.4283087
CV fold: Fold1
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold1
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold1
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold1
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean classification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean classification error' as optimization metric.
CV fold: Fold1
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 2.971 Round = 1 alpha = 0.0500 Value = -0.03838112
elapsed = 2.65 Round = 2 alpha = 0.2000 Value = -0.03852748
elapsed = 2.831 Round = 3 alpha = 0.1500 Value = -0.03849621
elapsed = 2.903 Round = 4 alpha = 0.1000 Value = -0.03844983
elapsed = 3.382 Round = 5 alpha = 0.9927179 Value = -0.03865969
elapsed = 2.806 Round = 6 alpha = 0.6273975 Value = -0.03863518
Best Parameters Found:
Round = 1 alpha = 0.0500 Value = -0.03838112
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 2.664 Round = 1 alpha = 0.0500 Value = -0.03859583
elapsed = 2.976 Round = 2 alpha = 0.2000 Value = -0.03864684
elapsed = 2.281 Round = 3 alpha = 0.1500 Value = -0.03863035
elapsed = 3.048 Round = 4 alpha = 0.1000 Value = -0.03861402
Timing stopped at: 0.031 0 0.031
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
elapsed = 0.092 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.179965
elapsed = 0.157 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1661427
elapsed = 0.211 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1687842
elapsed = 0.214 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1946351
elapsed = 0.328 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1735466
elapsed = 0.16 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1672985
elapsed = 0.125 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2445919
elapsed = 0.173 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1870047
elapsed = 0.071 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2413571
elapsed = 0.333 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1741428
elapsed = 0.093 Round = 11 subsample = 0.6936318 colsample_bytree = 0.6752604 min_child_weight = 7.0000 learning_rate = 0.2000 max_depth = 4.0000 Value = -0.1686736
elapsed = 0.233 Round = 12 subsample = 0.2499231 colsample_bytree = 0.9681048 min_child_weight = 7.0000 learning_rate = 0.2000 max_depth = 9.0000 Value = -0.1937092
Best Parameters Found:
Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1661427
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
elapsed = 0.204 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1780216
elapsed = 1.179 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1681675
elapsed = 0.367 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1634727
elapsed = 0.103 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.186193
elapsed = 0.04 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1720624
elapsed = 0.143 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1653245
elapsed = 0.167 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2425153
elapsed = 0.219 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1784738
elapsed = 0.129 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2385224
elapsed = 0.143 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1655399
elapsed = 0.045 Round = 11 subsample = 1.0000 colsample_bytree = 0.6357025 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 4.0000 Value = -0.1591217
elapsed = 0.289 Round = 12 subsample = 1.0000 colsample_bytree = 0.6333378 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 9.0000 Value = -0.1640883
Best Parameters Found:
Round = 11 subsample = 1.0000 colsample_bytree = 0.6357025 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 4.0000 Value = -0.1591217
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
elapsed = 0.109 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1913157
elapsed = 0.136 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1782915
elapsed = 0.198 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.176422
elapsed = 0.155 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1991184
elapsed = 0.217 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1763187
elapsed = 0.251 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1754786
elapsed = 0.097 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2462612
elapsed = 0.126 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1919792
elapsed = 0.075 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2433922
elapsed = 0.174 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1831283
elapsed = 0.093 Round = 11 subsample = 0.8661782 colsample_bytree = 0.5152599 min_child_weight = 9.0000 learning_rate = 0.1902978 max_depth = 6.0000 Value = -0.1671077
elapsed = 0.206 Round = 12 subsample = 0.2000 colsample_bytree = 1.0000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 10.0000 Value = -0.194026
Best Parameters Found:
Round = 11 subsample = 0.8661782 colsample_bytree = 0.5152599 min_child_weight = 9.0000 learning_rate = 0.1902978 max_depth = 6.0000 Value = -0.1671077
CV fold: Fold1
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
[ FAIL 1 | WARN 2 | SKIP 3 | PASS 31 ]
══ Skipped tests (3) ═══════════════════════════════════════════════════════════
• On CRAN (3): 'test-binary.R:54:3', 'test-lints.R:10:5',
'test-multiclass.R:54:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-regression.R:108:3'): test nested cv, bayesian, regression - glmnet ──
Error in `serverSocket(port = port)`: creation of server socket failed: port 11354 cannot be opened
Backtrace:
▆
1. └─glmnet_optimizer$execute() at test-regression.R:108:3
2. └─mlexperiments:::.run_cv(self = self, private = private)
3. └─mlexperiments:::.fold_looper(self, private)
4. ├─base::do.call(private$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. └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
10. └─mlexperiments:::.run_optimizer(...)
11. └─optimizer$execute(x = private$x, y = private$y, method_helper = private$method_helper)
12. ├─base::do.call(...)
13. └─mlexperiments (local) `<fn>`(...)
14. ├─base::do.call(...)
15. └─rBayesianOptimization (local) `<fn>`(...)
16. ├─utils::capture.output(...)
17. │ └─base::withVisible(...elt(i))
18. ├─base::system.time(...)
19. ├─base::do.call(what = FUN, args = as.list(Next_Par))
20. └─mlexperiments (local) `<fn>`(alpha = 0.992718181897844)
21. ├─base::do.call(FUN, args)
22. └─mlexperiments (local) `<fn>`(...)
23. ├─base::do.call(glmnet_bsF, kwargs)
24. └─mlexperiments (local) `<fn>`(...)
25. └─mlexperiments (local) glmnet_optimization(...)
26. └─kdry::pch_register_parallel(ncores)
27. └─parallel::makePSOCKcluster(names = ncores)
28. └─base::serverSocket(port = port)
[ FAIL 1 | WARN 2 | SKIP 3 | PASS 31 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc