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