Running ‘testthat.R’ [149s/220s]
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")
  Saving _problems/test-binary-5.R
  
  CV fold: Fold1
  
  Parameter settings [=============================>---------------] 2/3 ( 67%)
  Parameter settings [=============================================] 3/3 (100%)
                                                                                
  OMP: Warning #96: Cannot form a team with 3 threads, using 2 instead.
  OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set).
  
  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%)
                                                                                
  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 [=============================>---------------] 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 = 3.478	Round = 1	alpha = 0.0500	Value = -0.03838112 
  elapsed = 3.074	Round = 2	alpha = 0.2000	Value = -0.03852748 
  elapsed = 3.298	Round = 3	alpha = 0.1500	Value = -0.03849621 
  elapsed = 3.278	Round = 4	alpha = 0.1000	Value = -0.03844983 
  elapsed = 3.448	Round = 5	alpha = 0.9927179	Value = -0.03865969 
  elapsed = 3.367	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 = 3.292	Round = 1	alpha = 0.0500	Value = -0.03859583 
  elapsed = 3.801	Round = 2	alpha = 0.2000	Value = -0.03864684 
  elapsed = 3.547	Round = 3	alpha = 0.1500	Value = -0.03863035 
  elapsed = 3.669	Round = 4	alpha = 0.1000	Value = -0.03861402 
  elapsed = 2.553	Round = 5	alpha = 0.9927182	Value = -0.03871602 
  elapsed = 3.025	Round = 6	alpha = 0.6550449	Value = -0.03870422 
  
   Best Parameters Found: 
  Round = 1	alpha = 0.0500	Value = -0.03859583 
  
  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 = 3.464	Round = 1	alpha = 0.0500	Value = -0.04148682 
  elapsed = 2.819	Round = 2	alpha = 0.2000	Value = -0.04162914 
  elapsed = 4.445	Round = 3	alpha = 0.1500	Value = -0.04159226 
  elapsed = 3.258	Round = 4	alpha = 0.1000	Value = -0.04155432 
  elapsed = 3.739	Round = 5	alpha = 0.655018	Value = -0.04172817 
  elapsed = 2.872	Round = 6	alpha = 0.9927204	Value = -0.04175126 
  
   Best Parameters Found: 
  Round = 1	alpha = 0.0500	Value = -0.04148682 
  
  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%)
  
                                                                                
  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 = 0.43	Round = 1	subsample = 0.8000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1799936 
  elapsed = 1.155	Round = 2	subsample = 0.6000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1601449 
  elapsed = 0.271	Round = 3	subsample = 0.8000	colsample_bytree = 0.8000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1697138 
  elapsed = 0.235	Round = 4	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1959483 
  elapsed = 0.164	Round = 5	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.174398 
  elapsed = 0.422	Round = 6	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1651243 
  elapsed = 0.078	Round = 7	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2448137 
  elapsed = 0.137	Round = 8	subsample = 0.4000	colsample_bytree = 0.4000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1877671 
  elapsed = 0.055	Round = 9	subsample = 0.4000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2406981 
  elapsed = 0.747	Round = 10	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1756064 
  elapsed = 0.229	Round = 11	subsample = 0.6432993	colsample_bytree = 0.9835629	min_child_weight = 6.0000	learning_rate = 0.1935054	max_depth = 5.0000	Value = -0.1698066 
  elapsed = 0.081	Round = 12	subsample = 0.6452221	colsample_bytree = 0.7403856	min_child_weight = 2.0000	learning_rate = 0.1747095	max_depth = 5.0000	Value = -0.1618912 
  
   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 = -0.1601449 
  
  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 = 0.305	Round = 1	subsample = 0.8000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1782907 
  elapsed = 1.558	Round = 2	subsample = 0.6000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1706168 
  elapsed = 0.185	Round = 3	subsample = 0.8000	colsample_bytree = 0.8000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1639677 
  elapsed = 0.225	Round = 4	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1872621 
  elapsed = 0.106	Round = 5	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1707442 
  elapsed = 0.08	Round = 6	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1688216 
  elapsed = 0.086	Round = 7	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2412666 
  elapsed = 1.048	Round = 8	subsample = 0.4000	colsample_bytree = 0.4000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1773589 
  elapsed = 0.061	Round = 9	subsample = 0.4000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2395262 
  elapsed = 0.117	Round = 10	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1666513 
  elapsed = 0.364	Round = 11	subsample = 0.9888986	colsample_bytree = 0.7844255	min_child_weight = 6.0000	learning_rate = 0.1779883	max_depth = 6.0000	Value = -0.1602653 
  elapsed = 0.426	Round = 12	subsample = 0.9697736	colsample_bytree = 0.5113479	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 10.0000	Value = -0.1638984 
  
   Best Parameters Found: 
  Round = 11	subsample = 0.9888986	colsample_bytree = 0.7844255	min_child_weight = 6.0000	learning_rate = 0.1779883	max_depth = 6.0000	Value = -0.1602653 
  
  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 = 3.141	Round = 1	subsample = 0.8000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1912842 
  elapsed = 0.161	Round = 2	subsample = 0.6000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1757159 
  elapsed = 0.223	Round = 3	subsample = 0.8000	colsample_bytree = 0.8000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1813096 
  elapsed = 0.066	Round = 4	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1983487 
  elapsed = 0.112	Round = 5	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1792433 
  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 = -0.1760175 
  elapsed = 0.175	Round = 7	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2448583 
  elapsed = 2.722	Round = 8	subsample = 0.4000	colsample_bytree = 0.4000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1912984 
  elapsed = 0.361	Round = 9	subsample = 0.4000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2440582 
  elapsed = 3.864	Round = 10	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1812046 
  elapsed = 0.51	Round = 11	subsample = 0.4862949	colsample_bytree = 0.8329869	min_child_weight = 10.0000	learning_rate = 0.2000	max_depth = 6.0000	Value = -0.173082 
  elapsed = 0.76	Round = 12	subsample = 1.0000	colsample_bytree = 0.2000	min_child_weight = 10.0000	learning_rate = 0.2000	max_depth = 10.0000	Value = -0.204729 
  
   Best Parameters Found: 
  Round = 11	subsample = 0.4862949	colsample_bytree = 0.8329869	min_child_weight = 10.0000	learning_rate = 0.2000	max_depth = 6.0000	Value = -0.173082 
  
  CV fold: Fold1
  
  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%)
                                                                                
  [ FAIL 1 | WARN 4 | SKIP 2 | PASS 24 ]
  
  ══ Skipped tests (2) ═══════════════════════════════════════════════════════════
  • On CRAN (2): 'test-lints.R:10:5', 'test-multiclass.R:54:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-binary.R:3:1'): (code run outside of `test_that()`) ────────────
  <objectNotFoundError/error/condition>
  Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
  Backtrace:
      ▆
   1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-binary.R:3:1
   2. └─data.table::as.data.table(PimaIndiansDiabetes2)
  
  [ FAIL 1 | WARN 4 | SKIP 2 | PASS 24 ]
  Error:
  ! Test failures.
  Execution halted