Running ‘testthat.R’ [88s/240s]
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(mlsurvlrnrs)
  > 
  > test_check("mlsurvlrnrs")
  
  CV fold: Fold1
  Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
  
  CV fold: Fold2
  Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
  
  CV fold: Fold3
  Parameter 'ncores' is ignored for learner 'LearnerSurvCoxPHCox'.
  
  CV fold: Fold1
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 6 times in 2 thread(s)...  8.212 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.81 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.869 seconds
  
  CV fold: Fold2
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 6 times in 2 thread(s)...  5.354 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.503 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.526 seconds
  
  CV fold: Fold3
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 6 times in 2 thread(s)...  5.923 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.906 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.835 seconds
  
  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)...  7.995 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
       - Could not obtain meaningful lengthscales.
    2) Running local optimum search...
       - Convergence Not Found. Trying again with tighter parameters...
       - Convergence Not Found. Trying again with tighter parameters...        10.747 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.513 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)...  6.526 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        14.205 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.848 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)...  6.514 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
       - Could not obtain meaningful lengthscales.
    2) Running local optimum search...        1.125 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.639 seconds
  
  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)...  3.993 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.664 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.56 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)...  4.419 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.638 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.494 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.
  
  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.
  
  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)...  4.572 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        5.289 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.513 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)...  6.048 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        7.561 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.517 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)...  4.318 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
       - Could not obtain meaningful lengthscales.
    2) Running local optimum search...
       - Convergence Not Found. Trying again with tighter parameters...
       - Convergence Not Found. Trying again with tighter parameters...
       - Convergence Not Found. Trying again with tighter parameters...
       - Maximum convergence attempts exceeded - process is probably sampling random points.        72.664 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.506 seconds
  [ FAIL 2 | WARN 0 | SKIP 1 | PASS 12 ]
  
  ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
  • On CRAN (1): 'test-lints.R:10:5'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-surv_rpart_cox.R:108:5'): test nested cv, bayesian - surv_rpart_cox ──
  Error in `serverSocket(port = port)`: creation of server socket failed: port 11065 cannot be opened
  Backtrace:
       ▆
    1. └─surv_rpart_cox_optimizer$execute() at test-surv_rpart_cox.R:108:5
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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$ncores)
   15.                       └─parallel::makePSOCKcluster(names = ncores)
   16.                         └─base::serverSocket(port = port)
  ── Error ('test-surv_xgboost_aft.R:116:5'): test nested cv, bayesian - surv_xgboost_aft ──
  Error in `serverSocket(port = port)`: creation of server socket failed: port 11065 cannot be opened
  Backtrace:
       ▆
    1. └─surv_xgboost_aft_optimizer$execute() at test-surv_xgboost_aft.R:116:5
    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 2 | WARN 0 | SKIP 1 | PASS 12 ]
  Error: Test failures
  Execution halted