Running ‘testthat.R’ [236s/396s]
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 24 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 [=============================>---------------] 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
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 4 rows.
  elapsed = 4.877	Round = 1	alpha = 0.0500	Value = -0.03838112 
  elapsed = 4.679	Round = 2	alpha = 0.2000	Value = -0.03852748 
  elapsed = 5.148	Round = 3	alpha = 0.1500	Value = -0.03849621 
  elapsed = 5.111	Round = 4	alpha = 0.1000	Value = -0.03844983 
  elapsed = 5.179	Round = 5	alpha = 0.9927179	Value = -0.03865969 
  elapsed = 5.559	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 = 4.807	Round = 1	alpha = 0.0500	Value = -0.03859583 
  elapsed = 4.671	Round = 2	alpha = 0.2000	Value = -0.03864684 
  elapsed = 5.18	Round = 3	alpha = 0.1500	Value = -0.03863035 
  elapsed = 4.685	Round = 4	alpha = 0.1000	Value = -0.03861402 
  elapsed = 5.002	Round = 5	alpha = 0.9927182	Value = -0.03871602 
  elapsed = 4.749	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 = 4.953	Round = 1	alpha = 0.0500	Value = -0.04148682 
  elapsed = 4.739	Round = 2	alpha = 0.2000	Value = -0.04162914 
  elapsed = 4.604	Round = 3	alpha = 0.1500	Value = -0.04159226 
  elapsed = 5.217	Round = 4	alpha = 0.1000	Value = -0.04155432 
  elapsed = 5.32	Round = 5	alpha = 0.655018	Value = -0.04172817 
  elapsed = 4.812	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%)
  
                                                                                
  
  
  Parameter settings [=============================================] 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 = 0.665	Round = 1	subsample = 0.8000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1828011 
  elapsed = 0.931	Round = 2	subsample = 0.6000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1653724 
  elapsed = 2.019	Round = 3	subsample = 0.8000	colsample_bytree = 0.8000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1653406 
  elapsed = 1.173	Round = 4	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1987006 
  elapsed = 2.289	Round = 5	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1897082 
  elapsed = 1.329	Round = 6	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1743092 
  elapsed = 3.516	Round = 7	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2428898 
  elapsed = 0.392	Round = 8	subsample = 0.4000	colsample_bytree = 0.4000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1921259 
  elapsed = 1.986	Round = 9	subsample = 0.4000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2441279 
  elapsed = 5.955	Round = 10	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1810449 
  elapsed = 1.078	Round = 11	subsample = 0.7869579	colsample_bytree = 0.3807963	min_child_weight = 6.0000	learning_rate = 0.131633	max_depth = 5.0000	Value = -0.1820181 
  elapsed = 0.215	Round = 12	subsample = 0.8678558	colsample_bytree = 0.514649	min_child_weight = 9.0000	learning_rate = 0.1902419	max_depth = 5.0000	Value = -0.1780571 
  
   Best Parameters Found: 
  Round = 3	subsample = 0.8000	colsample_bytree = 0.8000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1653406 
  
  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 = 0.403	Round = 1	subsample = 0.8000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1839477 
  elapsed = 1.76	Round = 2	subsample = 0.6000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.171506 
  elapsed = 1.083	Round = 3	subsample = 0.8000	colsample_bytree = 0.8000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.164792 
  elapsed = 0.575	Round = 4	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.2047308 
  elapsed = 0.546	Round = 5	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1980206 
  elapsed = 0.824	Round = 6	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1807015 
  elapsed = 0.577	Round = 7	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.248052 
  elapsed = 3.738	Round = 8	subsample = 0.4000	colsample_bytree = 0.4000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1960023 
  elapsed = 1.075	Round = 9	subsample = 0.4000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2366496 
  elapsed = 0.459	Round = 10	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1922252 
  elapsed = 0.745	Round = 11	subsample = 0.7871648	colsample_bytree = 0.8134155	min_child_weight = 7.0000	learning_rate = 0.1555881	max_depth = 6.0000	Value = -0.1670881 
  elapsed = 0.564	Round = 12	subsample = 1.0000	colsample_bytree = 1.0000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 10.0000	Value = -0.1791919 
  
   Best Parameters Found: 
  Round = 3	subsample = 0.8000	colsample_bytree = 0.8000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.164792 
  
  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 = 1.641	Round = 1	subsample = 0.8000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.1953069 
  elapsed = 0.298	Round = 2	subsample = 0.6000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1786983 
  elapsed = 0.634	Round = 3	subsample = 0.8000	colsample_bytree = 0.8000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1699451 
  elapsed = 0.406	Round = 4	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 5.0000	Value = -0.2007858 
  elapsed = 0.13	Round = 5	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1922863 
  elapsed = 0.519	Round = 6	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 5.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1927776 
  elapsed = 0.864	Round = 7	subsample = 0.6000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2455989 
  elapsed = 0.358	Round = 8	subsample = 0.4000	colsample_bytree = 0.4000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1968986 
  elapsed = 0.645	Round = 9	subsample = 0.4000	colsample_bytree = 0.8000	min_child_weight = 1.0000	learning_rate = 0.1000	max_depth = 1.0000	Value = -0.2433844 
  elapsed = 3.388	Round = 10	subsample = 0.4000	colsample_bytree = 0.6000	min_child_weight = 1.0000	learning_rate = 0.2000	max_depth = 5.0000	Value = -0.1925334 
  elapsed = 0.372	Round = 11	subsample = 1.0000	colsample_bytree = 1.0000	min_child_weight = 10.0000	learning_rate = 0.2000	max_depth = 10.0000	Value = -0.1653477 
  elapsed = 2.08	Round = 12	subsample = 0.2000	colsample_bytree = 0.2000	min_child_weight = 10.0000	learning_rate = 0.2000	max_depth = 10.0000	Value = -0.2540175 
  
   Best Parameters Found: 
  Round = 11	subsample = 1.0000	colsample_bytree = 1.0000	min_child_weight = 10.0000	learning_rate = 0.2000	max_depth = 10.0000	Value = -0.1653477 
  
  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 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