Running ‘testthat.R’ [93s/142s]
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%)
                                                                                
  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
  
   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 [=============================>---------------] 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 = 3.242	Round = 1	alpha = 0.0500	Value = -0.03838112 
  elapsed = 2.828	Round = 2	alpha = 0.2000	Value = -0.03852748 
  elapsed = 2.608	Round = 3	alpha = 0.1500	Value = -0.03849621 
  elapsed = 2.294	Round = 4	alpha = 0.1000	Value = -0.03844983 
  elapsed = 2.511	Round = 5	alpha = 0.9927179	Value = -0.03865969 
  elapsed = 2.537	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 = 2.403	Round = 1	alpha = 0.0500	Value = -0.03859583 
  elapsed = 2.389	Round = 2	alpha = 0.2000	Value = -0.03864684 
  elapsed = 2.247	Round = 3	alpha = 0.1500	Value = -0.03863035 
  elapsed = 2.269	Round = 4	alpha = 0.1000	Value = -0.03861402 
  elapsed = 2.549	Round = 5	alpha = 0.9927182	Value = -0.03871602 
  elapsed = 2.60	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 = 2.437	Round = 1	alpha = 0.0500	Value = -0.04148682 
  elapsed = 2.581	Round = 2	alpha = 0.2000	Value = -0.04162914 
  elapsed = 2.327	Round = 3	alpha = 0.1500	Value = -0.04159226 
  elapsed = 2.233	Round = 4	alpha = 0.1000	Value = -0.04155432 
  elapsed = 2.211	Round = 5	alpha = 0.655018	Value = -0.04172817 
  elapsed = 2.377	Round = 6	alpha = 0.9927204	Value = -0.04175126 
  
   Best Parameters Found: 
  Round = 1	alpha = 0.0500	Value = -0.04148682 
  
  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
  
   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 = 0.071	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 = 0.207	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 = 0.107	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 = 0.102	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 = 0.095	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 = 0.129	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 = 0.063	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 = 0.113	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 = 0.064	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 = 0.127	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 = 0.079	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 = 0.068	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 = 0.079	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 = 0.094	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 = 0.081	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 = 0.045	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 = 0.115	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 = 0.074	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 = 0.06	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 = 0.071	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 = 0.088	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 = 0.113	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 = 0.072	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 = 0.298	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 = 0.138	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 = 0.085	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 = 0.078	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 = 0.075	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 = 0.096	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 = 0.125	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 = 0.052	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 = 0.084	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 = 0.052	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 = 0.33	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 = 0.12	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 = 0.09	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
  
  CV fold: Fold2
  
  CV progress [==================================>-----------------] 2/3 ( 67%)
  
  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