Running ‘testthat.R’ [182s/468s]
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)...  25.821 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.966 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)...  27.299 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.047 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)...  12.549 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.107 seconds
    3) Running FUN 2 times in 2 thread(s)...  4.992 seconds
  
  CV fold: Fold1
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 11 times in 2 thread(s)...  15.024 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.182 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)...  15.514 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.76 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)...  14.044 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.209 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)...  25.531 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.07 seconds
    3) Running FUN 2 times in 2 thread(s)...  3.963 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)...  12.95 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.055 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.257 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)...  13.459 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.165 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.238 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)...  14.172 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.91 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.592 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)...  6.354 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.301 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.677 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)...  5.334 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.997 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.352 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)...  4.992 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.189 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.472 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)...  6.47 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.051 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.559 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