Running ‘testthat.R’ [135s/356s]
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)...  19.442 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.583 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)...  18.284 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.673 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)...  9.394 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.789 seconds
    3) Running FUN 2 times in 2 thread(s)...  3.076 seconds
  
  CV fold: Fold1
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 11 times in 2 thread(s)...  9.601 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.594 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)...  13.782 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.568 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)...  14.576 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.632 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)...  21.019 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.707 seconds
    3) Running FUN 2 times in 2 thread(s)...  3.28 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)...  10.137 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.694 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.059 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)...  11.439 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.482 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.811 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)...  9.864 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.581 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.657 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)...  2.892 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.477 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.452 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)...  3.513 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.479 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.388 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)...  3.146 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.59 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.398 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)...  3.165 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.448 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.369 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