Running ‘testthat.R’ [137s/432s]
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
  > 
  > 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'.
  
  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'.
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 3 times in 2 thread(s)...  17.766 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        2.293 seconds
    3) Running FUN 2 times in 2 thread(s)...  13.503 seconds
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 3 times in 2 thread(s)...  18.302 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.737 seconds
    3) Running FUN 2 times in 2 thread(s)...  8.828 seconds
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 4 times in 2 thread(s)...  20.95 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.302 seconds
    3) Running FUN 2 times in 2 thread(s)...  9.554 seconds
  
  CV fold: Fold1
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 3 times in 2 thread(s)...  8.793 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.694 seconds
    3) Running FUN 2 times in 2 thread(s)...  7.131 seconds
  
  CV fold: Fold2
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 3 times in 2 thread(s)...  8.942 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        2.42 seconds
    3) Running FUN 2 times in 2 thread(s)...  4.081 seconds
  
  CV fold: Fold3
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 3 times in 2 thread(s)...  9.841 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        2.6 seconds
    3) Running FUN 2 times in 2 thread(s)...  3.836 seconds
  
  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
  
  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)...  30.609 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.213 seconds
    3) Running FUN 2 times in 2 thread(s)...  5.932 seconds
  
   Classification: using 'classification error rate' as optimization metric.
  
   Classification: using 'classification error rate' as optimization metric.
  
   Classification: using 'classification error rate' 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)...  17.557 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.217 seconds
    3) Running FUN 2 times in 2 thread(s)...  3.199 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)...  15.754 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.176 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.689 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)...  15.792 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.134 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.839 seconds
  
  CV fold: Fold1
  
   Classification: using 'classification error rate' as optimization metric.
  
   Classification: using 'classification error rate' as optimization metric.
  
   Classification: using 'classification error rate' as optimization metric.
  
  CV fold: Fold2
  
   Classification: using 'classification error rate' as optimization metric.
  
   Classification: using 'classification error rate' as optimization metric.
  
   Classification: using 'classification error rate' as optimization metric.
  
  CV fold: Fold3
  
   Classification: using 'classification error rate' as optimization metric.
  
   Classification: using 'classification error rate' as optimization metric.
  
   Classification: using 'classification error rate' 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)...  4.544 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.208 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.603 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)...  4.658 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.175 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.542 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.
  
  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 1 | WARN 0 | SKIP 1 | PASS 73 ]
  
  ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
  • On CRAN (1): 'test-lints.R:10:5'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-rpart_regression.R:203:5'): test nested cv, bayesian, regression - rpart ──
  Error in `serverSocket(port = port)`: creation of server socket failed: port 11519 cannot be opened
  Backtrace:
       ▆
    1. └─rpart_optimization$execute() at test-rpart_regression.R:203:5
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$cv_run_model, run_args)
    5.       └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
    6.         ├─base::do.call(.cv_run_nested_model, args)
    7.         └─mlexperiments (local) `<fn>`(...)
    8.           └─hparam_tuner$execute(k = self$k_tuning)
    9.             └─mlexperiments:::.run_tuning(self = self, private = private, optimizer = optimizer)
   10.               └─mlexperiments:::.run_optimizer(...)
   11.                 └─optimizer$execute(x = private$x, y = private$y, method_helper = private$method_helper)
   12.                   ├─base::do.call(...)
   13.                   └─mlexperiments (local) `<fn>`(...)
   14.                     └─kdry::pch_register_parallel(private$ncores)
   15.                       └─parallel::makePSOCKcluster(names = ncores)
   16.                         └─base::serverSocket(port = port)
  
  [ FAIL 1 | WARN 0 | SKIP 1 | PASS 73 ]
  Error: Test failures
  Execution halted