Running ‘testthat.R’ [120s/319s]
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)...  23.457 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.565 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)...  18.646 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.584 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)...  10.622 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.904 seconds
    3) Running FUN 2 times in 2 thread(s)...  3.509 seconds
  
  CV fold: Fold1
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 11 times in 2 thread(s)...  12.459 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.909 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)...  12.477 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.696 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)...  9.689 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.515 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)...  16.037 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.428 seconds
    3) Running FUN 2 times in 2 thread(s)...  3.492 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)...  9.047 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.444 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.815 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)...  10.485 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.619 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.752 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)...  9.808 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.457 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.62 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)...  3.333 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.835 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.376 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)...  3.845 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.498 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.377 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)...  3.431 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.51 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.54 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)...  4.492 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.652 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.649 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