Running ‘testthat.R’ [31s/77s]
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.
  
  CV fold: Fold1
  
  CV fold: Fold1
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  CV fold: Fold1
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  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.
  
  CV fold: Fold1
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  CV fold: Fold1
  
  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.
  
  CV fold: Fold1
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  CV fold: Fold1
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  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.
  
  CV fold: Fold1
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  CV fold: Fold1
  
  Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
  ... reducing initialization grid to 10 rows.
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  [ FAIL 12 | WARN 0 | SKIP 1 | PASS 13 ]
  
  ══ 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: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-glmnet_multiclass.R:95:5'): test nested cv, bayesian, multiclass - glmnet ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─glmnet_optimizer$execute() at test-glmnet_multiclass.R:95:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-glmnet_regression.R:95:5'): test nested cv, bayesian, regression - glmnet ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─glmnet_optimizer$execute() at test-glmnet_regression.R:95:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-lightgbm_binary.R:106:5'): test nested cv, bayesian, binary - lightgbm ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─lightgbm_optimizer$execute() at test-lightgbm_binary.R:106:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-lightgbm_multiclass.R:107:5'): test nested cv, bayesian, multiclass - lightgbm ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─lightgbm_optimizer$execute() at test-lightgbm_multiclass.R:107:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-lightgbm_regression.R:106:5'): test nested cv, bayesian, regression - lightgbm ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─lightgbm_optimization$execute() at test-lightgbm_regression.R:106:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-ranger_binary.R:99:5'): test nested cv, bayesian, binary - ranger ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─ranger_optimizer$execute() at test-ranger_binary.R:99:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-ranger_multiclass.R:97:5'): test nested cv, bayesian, regression - ranger ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─ranger_optimizer$execute() at test-ranger_multiclass.R:97:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-ranger_regression.R:96:5'): test nested cv, bayesian, regression - ranger ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─ranger_optimizer$execute() at test-ranger_regression.R:96:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-xgboost_binary.R:104:5'): test nested cv, bayesian, binary:logistic - xgboost ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─xgboost_optimizer$execute() at test-xgboost_binary.R:104:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-xgboost_multiclass.R:105:5'): test nested cv, bayesian, multi:softprob - xgboost ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─xgboost_optimizer$execute() at test-xgboost_multiclass.R:105:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  ── Error ('test-xgboost_regression.R:103:5'): test nested cv, bayesian, reg:squarederror - xgboost ──
  Error: Package "ParBayesianOptimization" must be installed to use 'strategy = "bayesian"'.
  Backtrace:
       ▆
    1. └─xgboost_optimizer$execute() at test-xgboost_regression.R:103:4
    2.   └─mlexperiments:::.run_cv(self = self, private = private)
    3.     └─mlexperiments:::.fold_looper(self, private)
    4.       ├─base::do.call(private$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.             └─private$select_optimizer(self, private)
   10.               └─BayesianOptimizer$new(...)
   11.                 └─mlexperiments (local) initialize(...)
  
  [ FAIL 12 | WARN 0 | SKIP 1 | PASS 13 ]
  Error: Test failures
  Execution halted