Running ‘testthat.R’ [51s/164s]
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
  > # https://github.com/Rdatatable/data.table/issues/5658
  > Sys.setenv("OMP_THREAD_LIMIT" = 2)
  > Sys.setenv("Ncpu" = 2)
  > 
  > library(testthat)
  > library(mllrnrs)
  > 
  > test_check("mllrnrs")
  
  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)...  5.368 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        6.117 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.573 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)...  4.949 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        4.889 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.512 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)...  4.947 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        8.056 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.56 seconds
  
  CV fold: Fold1
  
   Classification: using 'mean classification error' as optimization metric.
  Saving _problems/test-binary-287.R
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  CV fold: Fold1
  Saving _problems/test-multiclass-162.R
  
  CV fold: Fold1
  
   Classification: using 'mean classification error' as optimization metric.
  
   Classification: using 'mean classification error' as optimization metric.
  
   Classification: using 'mean classification error' as optimization metric.
  
  CV fold: Fold2
  
   Classification: using 'mean classification error' as optimization metric.
  
   Classification: using 'mean classification error' as optimization metric.
  
   Classification: using 'mean classification error' as optimization metric.
  
  CV fold: Fold3
  
   Classification: using 'mean classification error' as optimization metric.
  
   Classification: using 'mean classification error' as optimization metric.
  
   Classification: using 'mean classification error' as optimization metric.
  
  CV fold: Fold1
  Saving _problems/test-multiclass-294.R
  
  CV fold: Fold1
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 5 times in 2 thread(s)...  3.782 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        0.693 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.463 seconds
  
  CV fold: Fold2
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 5 times in 2 thread(s)...  4.265 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.099 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.546 seconds
  
  CV fold: Fold3
  
  Registering parallel backend using 2 cores.
  
  Running initial scoring function 5 times in 2 thread(s)...  4.314 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        1.443 seconds
    3) Running FUN 2 times in 2 thread(s)...  1.494 seconds
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  
  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)...  7.904 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        10.589 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.596 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)...  5.377 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        2.315 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.612 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)...  5.5 seconds
  
  Starting Epoch 1 
    1) Fitting Gaussian Process...
    2) Running local optimum search...        16.422 seconds
    3) Running FUN 2 times in 2 thread(s)...  0.861 seconds
  
  CV fold: Fold1
  
  CV fold: Fold2
  
  CV fold: Fold3
  [ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ]
  
  ══ Skipped tests (3) ═══════════════════════════════════════════════════════════
  • On CRAN (3): 'test-binary.R:57:5', 'test-lints.R:10:5',
    'test-multiclass.R:57:5'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-binary.R:287:5'): test nested cv, grid, binary - ranger ────────
  Error in `xtfrm.data.frame(structure(list(`0` = 0.379858310721837, `1` = 0.620141689278164), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x556d81434070>, .data.table.locked = TRUE))`: cannot xtfrm data frames
  Backtrace:
       ▆
    1. ├─ranger_optimizer$execute() at test-binary.R:287:5
    2. │ └─mlexperiments:::.run_cv(self = self, private = private)
    3. │   └─mlexperiments:::.fold_looper(self, private)
    4. │     ├─base::do.call(private$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. │                   └─base::lapply(...)
   15. │                     └─mlexperiments (local) FUN(X[[i]], ...)
   16. │                       ├─base::do.call(FUN, fun_parameters)
   17. │                       └─mlexperiments (local) `<fn>`(...)
   18. │                         ├─base::do.call(private$fun_optim_cv, kwargs)
   19. │                         └─mllrnrs (local) `<fn>`(...)
   20. │                           ├─base::do.call(ranger_predict, pred_args)
   21. │                           └─mllrnrs (local) `<fn>`(...)
   22. │                             └─kdry::mlh_reshape(preds)
   23. │                               ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))]
   24. │                               └─data.table:::`[.data.table`(...)
   25. └─base::which.max(.SD)
   26.   ├─base::xtfrm(`<dt[,2]>`)
   27.   └─base::xtfrm.data.frame(`<dt[,2]>`)
  ── Error ('test-multiclass.R:162:5'): test nested cv, grid, multiclass - lightgbm ──
  Error in `xtfrm.data.frame(structure(list(`0` = 0.20774260202068, `1` = 0.136781829323219, `2` = 0.655475568656101), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x556d81434070>, .data.table.locked = TRUE))`: cannot xtfrm data frames
  Backtrace:
       ▆
    1. ├─lightgbm_optimizer$execute() at test-multiclass.R:162:5
    2. │ └─mlexperiments:::.run_cv(self = self, private = private)
    3. │   └─mlexperiments:::.fold_looper(self, private)
    4. │     ├─base::do.call(private$cv_run_model, run_args)
    5. │     └─mlexperiments (local) `<fn>`(train_index = `<int>`, fold_train = `<named list>`, fold_test = `<named list>`)
    6. │       ├─base::do.call(.cv_run_nested_model, args)
    7. │       └─mlexperiments (local) `<fn>`(...)
    8. │         └─mlexperiments:::.cv_fit_model(...)
    9. │           ├─base::do.call(self$learner$predict, pred_args)
   10. │           └─mlexperiments (local) `<fn>`(...)
   11. │             ├─base::do.call(private$fun_predict, kwargs)
   12. │             └─mllrnrs (local) `<fn>`(...)
   13. │               └─kdry::mlh_reshape(preds)
   14. │                 ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))]
   15. │                 └─data.table:::`[.data.table`(...)
   16. └─base::which.max(.SD)
   17.   ├─base::xtfrm(`<dt[,3]>`)
   18.   └─base::xtfrm.data.frame(`<dt[,3]>`)
  ── Error ('test-multiclass.R:294:5'): test nested cv, grid, multi:softprob - xgboost, with weights ──
  Error in `xtfrm.data.frame(structure(list(`0` = 0.250160574913025, `1` = 0.124035485088825, `2` = 0.62580394744873), row.names = c(NA, -1L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x556d81434070>, .data.table.locked = TRUE))`: cannot xtfrm data frames
  Backtrace:
       ▆
    1. ├─xgboost_optimizer$execute() at test-multiclass.R:294:5
    2. │ └─mlexperiments:::.run_cv(self = self, private = private)
    3. │   └─mlexperiments:::.fold_looper(self, private)
    4. │     ├─base::do.call(private$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. │         └─mlexperiments:::.cv_fit_model(...)
    9. │           ├─base::do.call(self$learner$predict, pred_args)
   10. │           └─mlexperiments (local) `<fn>`(...)
   11. │             ├─base::do.call(private$fun_predict, kwargs)
   12. │             └─mllrnrs (local) `<fn>`(...)
   13. │               └─kdry::mlh_reshape(preds)
   14. │                 ├─data.table::as.data.table(object)[, cn[which.max(.SD)], by = seq_len(nrow(object))]
   15. │                 └─data.table:::`[.data.table`(...)
   16. └─base::which.max(.SD)
   17.   ├─base::xtfrm(`<dt[,3]>`)
   18.   └─base::xtfrm.data.frame(`<dt[,3]>`)
  
  [ FAIL 3 | WARN 0 | SKIP 3 | PASS 25 ]
  Error:
  ! Test failures.
  Execution halted