Running ‘testthat.R’ [6m/18m]
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
  > 
  > Sys.setenv("OMP_THREAD_LIMIT" = 2)
  > Sys.setenv("Ncpu" = 2)
  > 
  > library(testthat)
  > library(mlexperiments)
  > 
  > test_check("mlexperiments")
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold4
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold5
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  CV fold: Fold4
  
  CV fold: Fold5
  
  Testing for identical folds in 2 and 1.
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold4
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold5
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold4
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold5
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold4
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  
  CV fold: Fold5
  Parameter 'ncores' is ignored for learner 'LearnerGlm'.
  Saving _problems/test-glm_predictions-79.R
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold4
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold5
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  Saving _problems/test-glm_predictions-188.R
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 11 times in 2 thread(s)...  79.505 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.474 seconds
  
   Noise could not be added to find unique parameter set. Stopping process and returning results so far. 
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 11 times in 2 thread(s)...  101.568 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        2.043 seconds
  
   Noise could not be added to find unique parameter set. Stopping process and returning results so far. 
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 4 times in 2 thread(s)...  31.072 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.553 seconds
    3) Running FUN 2 times in 2 thread(s)...  14.023 seconds
  
  CV fold: Fold1
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 11 times in 2 thread(s)...  65.393 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        2.358 seconds
  
   Noise could not be added to find unique parameter set. Stopping process and returning results so far. 
  
  CV fold: Fold2
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 11 times in 2 thread(s)...  65.698 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.687 seconds
  
   Noise could not be added to find unique parameter set. Stopping process and returning results so far. 
  
  CV fold: Fold3
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 11 times in 2 thread(s)...  65.596 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.653 seconds
  
   Noise could not be added to find unique parameter set. Stopping process and returning results so far. 
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerLm'.
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  50.318 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.838 seconds
    3) Running FUN 2 times in 2 thread(s)...  12.42 seconds
  
   Classification: using 'mean misclassification error' as optimization metric.
  
   Classification: using 'mean misclassification error' as optimization metric.
  
   Classification: using 'mean misclassification 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.
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  28.801 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.623 seconds
    3) Running FUN 2 times in 2 thread(s)...  4.596 seconds
  
  CV fold: Fold2
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  28.638 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.623 seconds
    3) Running FUN 2 times in 2 thread(s)...  4.225 seconds
  
  CV fold: Fold3
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  32.889 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.397 seconds
    3) Running FUN 2 times in 2 thread(s)...  5.596 seconds
  
  CV fold: Fold1
  
   Classification: using 'mean misclassification error' as optimization metric.
  
   Classification: using 'mean misclassification error' as optimization metric.
  
   Classification: using 'mean misclassification error' as optimization metric.
  
  CV fold: Fold2
  
   Classification: using 'mean misclassification error' as optimization metric.
  
   Classification: using 'mean misclassification error' as optimization metric.
  
   Classification: using 'mean misclassification error' as optimization metric.
  
  CV fold: Fold3
  
   Classification: using 'mean misclassification error' as optimization metric.
  
   Classification: using 'mean misclassification error' as optimization metric.
  
   Classification: using 'mean misclassification error' as optimization metric.
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  6.279 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.184 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.663 seconds
  
   Regression: using 'mean squared error' as optimization metric.
  
   Regression: using 'mean squared error' as optimization metric.
  
   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.
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  8.577 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.691 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.919 seconds
  
  CV fold: Fold2
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  8.847 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.82 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.667 seconds
  
  CV fold: Fold3
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  7.705 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        2.016 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.949 seconds
  
  CV fold: Fold1
  
   Regression: using 'mean squared error' as optimization metric.
  
   Regression: using 'mean squared error' as optimization metric.
  
   Regression: using 'mean squared error' as optimization metric.
  
  CV fold: Fold2
  
   Regression: using 'mean squared error' as optimization metric.
  
   Regression: using 'mean squared error' as optimization metric.
  
   Regression: using 'mean squared error' as optimization metric.
  
  CV fold: Fold3
  
   Regression: using 'mean squared error' as optimization metric.
  
   Regression: using 'mean squared error' as optimization metric.
  
   Regression: using 'mean squared error' as optimization metric.
  [ FAIL 2 | WARN 0 | SKIP 1 | PASS 68 ]
  
  ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
  • On CRAN (1): 'test-lints.R:10:5'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-glm_predictions.R:73:5'): test predictions, binary - glm ───────
  Error in ``[.data.table`(res, , `:=`(mean = mean(as.numeric(.SD), na.rm = na.rm), sd = stats::sd(as.numeric(.SD), na.rm = na.rm)), .SDcols = colnames(res), by = seq_len(nrow(res)))`: attempt access index 5/5 in VECTOR_ELT
  Backtrace:
      ▆
   1. └─mlexperiments::predictions(...) at test-glm_predictions.R:73:5
   2.   ├─...[]
   3.   └─data.table:::`[.data.table`(...)
  ── Error ('test-glm_predictions.R:182:5'): test predictions, regression - lm ───
  Error in ``[.data.table`(res, , `:=`(mean = mean(as.numeric(.SD), na.rm = na.rm), sd = stats::sd(as.numeric(.SD), na.rm = na.rm)), .SDcols = colnames(res), by = seq_len(nrow(res)))`: attempt access index 5/5 in VECTOR_ELT
  Backtrace:
      ▆
   1. └─mlexperiments::predictions(...) at test-glm_predictions.R:182:5
   2.   ├─...[]
   3.   └─data.table:::`[.data.table`(...)
  
  [ FAIL 2 | WARN 0 | SKIP 1 | PASS 68 ]
  Error:
  ! Test failures.
  Execution halted