Running 'testthat.R' [16m]
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(mllrnrs)
  > 
  > test_check("mllrnrs")
  
  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)...  5.03 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.64 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.36 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
  
  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)...  12.46 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.75 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.61 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)...  12.65 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.68 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.34 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)...  13.23 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.75 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.74 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)...  5.32 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.67 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.36 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)...  5.08 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.64 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.39 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)...  5.04 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.66 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.38 seconds
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  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)...  6.08 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        7.36 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.5 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)...  5.62 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        13.81 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.44 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)...  5.59 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        31.31 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.5 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)...  7.5 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        14.36 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.97 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)...  6.89 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        11.57 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.39 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)...  8.1 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        14.92 seconds
    3) Running FUN 2 times in 2 thread(s)...  2 seconds
  
  CV fold: Fold1
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  6.78 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        12.65 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.55 seconds
  
  CV fold: Fold2
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  6.9 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        18.69 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.5 seconds
  
  CV fold: Fold3
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 10 times in 2 thread(s)...  7.14 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        20.61 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.71 seconds
  
  CV fold: Fold1
  [LightGBM] [Info] Start training from score 22.606222
  [LightGBM] [Info] Start training from score 22.555507
  [LightGBM] [Info] Start training from score 22.682589
  [LightGBM] [Info] Start training from score 22.606222
  [LightGBM] [Info] Start training from score 22.555507
  [LightGBM] [Info] Start training from score 22.682589
  [LightGBM] [Info] Start training from score 22.606222
  [LightGBM] [Info] Start training from score 22.555507
  [LightGBM] [Info] Start training from score 22.682589
  
  CV fold: Fold2
  [LightGBM] [Info] Start training from score 22.530089
  [LightGBM] [Info] Start training from score 22.396460
  [LightGBM] [Info] Start training from score 22.310620
  [LightGBM] [Info] Start training from score 22.530089
  [LightGBM] [Info] Start training from score 22.396460
  [LightGBM] [Info] Start training from score 22.310620
  [LightGBM] [Info] Start training from score 22.530089
  [LightGBM] [Info] Start training from score 22.396460
  [LightGBM] [Info] Start training from score 22.310620
  
  CV fold: Fold3
  [LightGBM] [Info] Start training from score 22.675556
  [LightGBM] [Info] Start training from score 22.438739
  [LightGBM] [Info] Start training from score 22.600897
  [LightGBM] [Info] Start training from score 22.675556
  [LightGBM] [Info] Start training from score 22.438739
  [LightGBM] [Info] Start training from score 22.600897
  [LightGBM] [Info] Start training from score 22.675556
  [LightGBM] [Info] Start training from score 22.438739
  [LightGBM] [Info] Start training from score 22.600897
  
  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)...  7.09 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        4.32 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.66 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)...  7.1 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        12.85 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.42 seconds
  
   Errors encountered in FUN 
  
  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)...  6.9 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        6.7 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.81 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)...  11.96 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        11.67 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.73 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)...  12.11 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        3.67 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.89 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)...  12.2 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        3.22 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.25 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)...  6.25 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.26 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.47 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)...  6.11 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.28 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.45 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)...  6.45 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.67 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.56 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.
  
  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)...  5.08 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        11.12 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.47 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)...  5.1 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        7.58 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.35 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)...  4.94 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        12.91 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.4 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)...  10.49 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        3 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.28 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)...  10.25 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        15.38 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.64 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)...  11.3 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        14.92 seconds
    3) Running FUN 2 times in 2 thread(s)...  2.5 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)...  6.33 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        28.23 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.8 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)...  5.9 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        3.59 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.56 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)...  6.06 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        14.16 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.64 seconds
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  [ FAIL 1 | WARN 0 | SKIP 1 | PASS 46 ]
  
  ══ Skipped tests (1) ═══════════════════════════════════════════════════════════
  • On CRAN (1): 'test-lints.R:10:5'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-glmnet_binary.R:96:5'): test nested cv, bayesian, binary - glmnet ──
  Error in `parallel::makePSOCKcluster(names = ncores)`: Cluster setup failed. 2 of 2 workers failed to connect.
  Backtrace:
       ▆
    1. └─glmnet_optimizer$execute() at test-glmnet_binary.R:96:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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)
  
  [ FAIL 1 | WARN 0 | SKIP 1 | PASS 46 ]
  Error: Test failures
  Execution halted