Running ‘testthat.R’ [144s/174s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(xgboost)
  > 
  > test_check("xgboost", reporter = ProgressReporter)
  ✔ | F W S  OK | Context
  
  ⠏ |         0 | basic                                                           
  ⠏ |         0 | basic functions                                                 
  ⠋ |         1 | basic functions                                                 
  ⠸ |         4 | basic functions                                                 [03:43:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  
  ⠙ |        12 | basic functions                                                 [1]	train-rmse:1.678965 
  [2]	train-rmse:1.678965 
  [3]	train-rmse:1.524624 
  [4]	train-rmse:1.441523 
  [5]	train-rmse:1.437758 
  [6]	train-rmse:1.377895 
  [7]	train-rmse:1.305894 
  [8]	train-rmse:1.336120 
  [9]	train-rmse:1.316072 
  [10]	train-rmse:1.316031 
  [11]	train-rmse:1.319404 
  [12]	train-rmse:1.235097 
  [13]	train-rmse:1.225430 
  [14]	train-rmse:1.221079 
  [15]	train-rmse:1.235220 
  [16]	train-rmse:1.219153 
  [17]	train-rmse:1.226439 
  [18]	train-rmse:1.235026 
  [19]	train-rmse:1.244651 
  [20]	train-rmse:1.253197 
  [21]	train-rmse:1.262494 
  [22]	train-rmse:1.272865 
  [23]	train-rmse:1.276073 
  [24]	train-rmse:1.277040 
  [25]	train-rmse:1.287974 
  [26]	train-rmse:1.289816 
  [27]	train-rmse:1.292365 
  [28]	train-rmse:1.282785 
  [29]	train-rmse:1.283805 
  [30]	train-rmse:1.290661 
  [31]	train-rmse:1.289439 
  [32]	train-rmse:1.281995 
  [03:43:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  
  ⠸ |        14 | basic functions                                                 [03:43:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  
  ⠴ |        16 | basic functions                                                 [03:43:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  
  ⠧ |        28 | basic functions                                                 [1]	train-error:0.028405 
  
  ⠦ |        37 | basic functions                                                 [03:43:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  
  ⠙ |        42 | basic functions                                                 [03:43:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  
  ⠦ |        47 | basic functions                                                 
  ⠋ |        51 | basic functions                                                 [1]	train-logloss:0.439409 
  [2]	train-logloss:0.299260 
  [3]	train-logloss:0.209937 
  [4]	train-logloss:0.150151 
  
  ⠇ |        59 | basic functions                                                 
  ⠏ |        60 | basic functions                                                 [1]	train-logloss:0.233470+0.001565	test-logloss:0.233607+0.004930 
  [2]	train-logloss:0.136851+0.001767	test-logloss:0.137010+0.006645 
  [1]	train-logloss:0.233482+0.001761	test-logloss:0.233527+0.005591 
  [2]	train-logloss:0.136860+0.002158	test-logloss:0.136933+0.007299 
  
  ⠋ |        71 | basic functions                                                 
  ⠙ |        72 | basic functions                                                 
  ⠹ |        73 | basic functions                                                 
  ⠦ |        77 | basic functions                                                 
  ⠏ |        80 | basic functions                                                 [1]	train-logloss:0.380598 
  [2]	train-logloss:0.247331 
  [3]	train-logloss:0.175047 
  [4]	train-logloss:0.122301 
  [5]	train-logloss:0.089889 
  [1]	train-logloss:0.497338 
  [2]	train-logloss:0.357306 
  [3]	train-logloss:0.257215 
  [4]	train-logloss:0.184518 
  [5]	train-logloss:0.132113 
  
  ⠙ |        82 | basic functions                                                 
  ⠸ |        84 | basic functions                                                 [1]	train-error:0.046522	train-auc:0.958228	train-logloss:0.233376 
  [2]	train-error:0.022263	train-auc:0.981413	train-logloss:0.136658 
  [1]	train-merror:0.040000 
  [2]	train-merror:0.026667 
  
  ⠴ |        86 | basic functions                                                 [1]	train-error:0.046522	train-auc:0.958228	train-logloss:0.482541 
  [2]	train-error:0.046522	train-auc:0.987161	train-logloss:0.359536 
  
  ⠸ |        94 | basic functions                                                 
  ⠇ |        99 | basic functions                                                 
  ✔ |       100 | basic functions [21.2s]
  
  ⠏ |         0 | callbacks                                                       
  ⠏ |         0 | callbacks                                                       
  ⠋ |         1 | callbacks                                                       [1]	train-auc:0.900000	test-auc:0.800000 
  
  ⠴ |        26 | callbacks                                                       
  ⠏ |        30 | callbacks                                                       
  ⠙ |        32 | callbacks                                                       
  ⠸ |        34 | callbacks                                                       
  ⠴ |        36 | callbacks                                                       
  ⠧ |        38 | callbacks                                                       
  ⠇ |        39 | callbacks                                                       
  ⠏ |        40 | callbacks                                                       
  ⠹ |        43 | callbacks                                                       
  ⠇ |   1    48 | callbacks                                                       
  ⠙ |   1    51 | callbacks                                                       
  ⠧ |   1    57 | callbacks                                                       
  ⠹ |   1    62 | callbacks                                                       [03:43:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [1]	train-auc:0.829749 
  Will train until train_auc hasn't improved in 3 rounds.
  
  [2]	train-auc:0.829749 
  [3]	train-auc:0.829749 
  [4]	train-auc:0.829749 
  Stopping. Best iteration:
  [1]	train-auc:0.829749
  
  
  ⠇ |   1    68 | callbacks                                                       
  ⠏ |   1    69 | callbacks                                                       
  ⠼ |   1    74 | callbacks                                                       
  ⠇ |   1    78 | callbacks                                                       
  ⠙ |   1    81 | callbacks                                                       
  ⠼ |   1    84 | callbacks                                                       
  ⠹ |   1    92 | callbacks                                                       
  ✔ |   1    95 | callbacks [11.6s]
  ────────────────────────────────────────────────────────────────────────────────
  Warning ('test_callbacks.R:196:3'): cb.save.model works as expected
  one argument not used by format 'xgboost.json'
  Backtrace:
   1. xgboost::xgb.train(...)
        at test_callbacks.R:196:2
   2. xgboost (local) f()
   4. base::sprintf(save_name, env$iteration)
  ────────────────────────────────────────────────────────────────────────────────
  
  ⠏ |         0 | config                                                          
  ⠏ |         0 | Test global configuration                                       
  ✔ |         8 | Test global configuration
  
  ⠏ |         0 | custom_objective                                                
  ⠏ |         0 | Test models with custom objective                               [1]	eval-error:0.042831	train-error:0.046522 
  [2]	eval-error:0.021726	train-error:0.022263 
  
  ⠋ |         1 | Test models with custom objective                               
  ⠼ |         5 | Test models with custom objective                               [1]	eval-error:0.042831	train-error:0.046522 
  [2]	eval-error:0.021726	train-error:0.022263 
  [3]	eval-error:0.018001	train-error:0.015200 
  [4]	eval-error:0.018001	train-error:0.015200 
  [5]	eval-error:0.006207	train-error:0.007063 
  [6]	eval-error:0.000000	train-error:0.001228 
  [7]	eval-error:0.000000	train-error:0.001228 
  [8]	eval-error:0.000000	train-error:0.001228 
  [9]	eval-error:0.000000	train-error:0.001228 
  [10]	eval-error:0.000000	train-error:0.000000 
  
  ⠧ |         8 | Test models with custom objective                               [1]	eval-error:0.042831	train-error:0.046522 
  [2]	eval-error:0.021726	train-error:0.022263 
  
  ⠏ |        10 | Test models with custom objective                               
  ✔ |        12 | Test models with custom objective [2.1s]
  
  ⠏ |         0 | dmatrix                                                         
  ⠏ |         0 | testing xgb.DMatrix functionality                               
  ⠧ |         8 | testing xgb.DMatrix functionality                               [03:43:38] 6513x126 matrix with 143286 entries loaded from /home/hornik/tmp/scratch/RtmpFuss2t/xgb.DMatrix_2b300322b70a3a
  
  ⠹ |        13 | testing xgb.DMatrix functionality                               
  ⠸ |        24 | testing xgb.DMatrix functionality                               
  ⠦ |   1    26 | testing xgb.DMatrix functionality                               
  ⠹ |   1    32 | testing xgb.DMatrix functionality                               
  ⠼ |   1    34 | testing xgb.DMatrix functionality                               
  ✔ |   1    40 | testing xgb.DMatrix functionality [0.8s]
  ────────────────────────────────────────────────────────────────────────────────
  Warning ('test_dmatrix.R:82:3'): xgb.DMatrix: getinfo & setinfo
  NAs introduced by coercion
  Backtrace:
   1. testthat::expect_error(setinfo(dtest, "weight", rep("a", nrow(test_data))))
        at test_dmatrix.R:82:2
   7. xgboost:::setinfo.xgb.DMatrix(dtest, "weight", rep("a", nrow(test_data)))
  ────────────────────────────────────────────────────────────────────────────────
  
  ⠏ |         0 | feature_weights                                                 
  ⠏ |         0 | feature weights                                                 
  ⠋ |         1 | feature weights                                                 
  ⠹ |         3 | feature weights                                                 
  ⠼ |         5 | feature weights                                                 
  ✔ |         6 | feature weights [0.6s]
  
  ⠏ |         0 | gc_safety                                                       
  ⠏ |         0 | Garbage Collection Safety Check                                 [1]	train-logloss:0.233376 
  [2]	train-logloss:0.136658 
  
  ⠋ |         1 | Garbage Collection Safety Check                                 
  ✔ |         1 | Garbage Collection Safety Check [107.0s]
  
  ⠏ |         0 | glm                                                             
  ⠏ |         0 | Test generalized linear models                                  
  ⠋ |         1 | Test generalized linear models                                  
  ⠴ |         6 | Test generalized linear models                                  
  ⠦ |         7 | Test generalized linear models                                  
  ⠧ |         8 | Test generalized linear models                                  [1]	eval-error:0.002483	train-error:0.004760 
  Multiple eval metrics are present. Will use train_error for early stopping.
  Will train until train_error hasn't improved in 1 rounds.
  
  [2]	eval-error:0.000621	train-error:0.002610 
  [3]	eval-error:0.000000	train-error:0.001842 
  [4]	eval-error:0.000000	train-error:0.001228 
  [5]	eval-error:0.000000	train-error:0.000614 
  [6]	eval-error:0.000000	train-error:0.000614 
  Stopping. Best iteration:
  [5]	eval-error:0.000000	train-error:0.000614
  
  
  ⠙ |        12 | Test generalized linear models                                  [1]	eval-error:0.002483	train-error:0.004760 
  Multiple eval metrics are present. Will use train_error for early stopping.
  Will train until train_error hasn't improved in 1 rounds.
  
  [2]	eval-error:0.000621	train-error:0.002610 
  [3]	eval-error:0.000000	train-error:0.001842 
  [4]	eval-error:0.000000	train-error:0.001228 
  [5]	eval-error:0.000000	train-error:0.000614 
  [6]	eval-error:0.000000	train-error:0.000614 
  Stopping. Best iteration:
  [5]	eval-error:0.000000	train-error:0.000614
  
  
  ✔ |        13 | Test generalized linear models [0.8s]
  
  ⠏ |         0 | helpers                                                         
  ⠏ |         0 | Test helper functions                                           
  ⠋ |         1 | Test helper functions                                           [1]	train-logloss:0.636592 
  
  ⠼ |        15 | Test helper functions                                           
  ⠧ |        38 | Test helper functions                                           [1]	train-rmse:1.940066 
  [2]	train-rmse:1.560864 
  [3]	train-rmse:1.277414 
  [4]	train-rmse:1.068562 
  [5]	train-rmse:0.903897 
  [6]	train-rmse:0.763228 
  [7]	train-rmse:0.664924 
  [8]	train-rmse:0.570358 
  [9]	train-rmse:0.507416 
  [10]	train-rmse:0.458599 
  [11]	train-rmse:0.394349 
  [12]	train-rmse:0.355794 
  [13]	train-rmse:0.305484 
  [14]	train-rmse:0.271176 
  [15]	train-rmse:0.259943 
  [16]	train-rmse:0.238429 
  [17]	train-rmse:0.228003 
  [18]	train-rmse:0.212117 
  [19]	train-rmse:0.187830 
  [20]	train-rmse:0.173381 
  [21]	train-rmse:0.164001 
  [22]	train-rmse:0.156113 
  [23]	train-rmse:0.143242 
  [24]	train-rmse:0.130215 
  [25]	train-rmse:0.120160 
  [26]	train-rmse:0.112119 
  [27]	train-rmse:0.104753 
  [28]	train-rmse:0.096786 
  [29]	train-rmse:0.089361 
  [30]	train-rmse:0.083706 
  
  ⠧ |        48 | Test helper functions                                           
  ⠋ |       121 | Test helper functions                                           
  ⠇ |       189 | Test helper functions                                           [1]	train-rmse:1.940066 
  [2]	train-rmse:1.675038 
  [3]	train-rmse:1.462383 
  [4]	train-rmse:1.283198 
  [5]	train-rmse:1.155542 
  [6]	train-rmse:1.049559 
  [7]	train-rmse:0.942910 
  [8]	train-rmse:0.859371 
  [9]	train-rmse:0.774970 
  [10]	train-rmse:0.725452 
  [11]	train-rmse:0.679127 
  [12]	train-rmse:0.628614 
  [13]	train-rmse:0.594549 
  [14]	train-rmse:0.535545 
  [15]	train-rmse:0.485623 
  [16]	train-rmse:0.460187 
  [17]	train-rmse:0.413632 
  [18]	train-rmse:0.403692 
  [19]	train-rmse:0.385314 
  [20]	train-rmse:0.366653 
  [21]	train-rmse:0.354532 
  [22]	train-rmse:0.326526 
  [23]	train-rmse:0.316875 
  [24]	train-rmse:0.301910 
  [25]	train-rmse:0.285458 
  [26]	train-rmse:0.275437 
  [27]	train-rmse:0.267556 
  [28]	train-rmse:0.263727 
  [29]	train-rmse:0.253460 
  [30]	train-rmse:0.235433 
  
  ⠏ |       250 | Test helper functions                                           
  ⠼ |       285 | Test helper functions                                           
  ⠙ |       322 | Test helper functions                                           
  ⠧ |       368 | Test helper functions                                           
  ⠋ |       411 | Test helper functions                                           
  ⠧ |       458 | Test helper functions                                           
  ⠏ |       480 | Test helper functions                                           
  ⠧ |       498 | Test helper functions                                           
  ⠴ |       516 | Test helper functions                                           
  ⠼ |       535 | Test helper functions                                           
  ⠹ |       553 | Test helper functions                                           
  ⠦ |       577 | Test helper functions                                           
  ⠹ |       603 | Test helper functions                                           
  ⠙ |       622 | Test helper functions                                           
  ⠙ |       642 | Test helper functions                                           
  ⠋ |       661 | Test helper functions                                           
  ⠇ |       669 | Test helper functions                                           
  ⠸ |       674 | Test helper functions                                           
  ⠸ |       684 | Test helper functions                                           
  ⠴ |       686 | Test helper functions                                           
  ⠦ |       687 | Test helper functions                                           [1]	train-rmse:0.943398 
  
  ⠧ |       688 | Test helper functions                                           
  ⠦ |       697 | Test helper functions                                           
  ⠏ | 1     699 | Test helper functions                                           
  ⠋ | 2     699 | Test helper functions                                           
  ⠙ | 2     700 | Test helper functions                                           
  ⠹ | 2     701 | Test helper functions                                           
  ⠋ | 2     709 | Test helper functions                                           
  ⠦ | 2     715 | Test helper functions                                           
  ⠏ | 2     728 | Test helper functions                                           
  ✖ | 2     747 | Test helper functions [8.1s]
  ────────────────────────────────────────────────────────────────────────────────
  Error ('test_helpers.R:382:3'): xgb.plot.tree works with and without feature names
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
    1. testthat::expect_silent(...)
         at test_helpers.R:382:2
   15. dplyr:::full_join.data.frame(...)
   16. dplyr:::join_mutate(...)
   18. tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,2]>`)
   19. tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   20. tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   22. tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  
  Error ('test_helpers.R:387:3'): xgb.plot.multi.trees works with and without feature names
  Error in `vectbl_assign(x[[j]], i, recycled_value[[j]])`: DLL requires the use of native symbols
  Backtrace:
    1. xgboost::xgb.plot.multi.trees(...)
         at test_helpers.R:387:2
    7. dplyr:::full_join.data.frame(...)
    8. dplyr:::join_mutate(...)
   10. tibble:::`[<-.tbl_df`(`*tmp*`, new_rows, merge, value = `<tibble[,2]>`)
   11. tibble:::tbl_subassign(x, i, j, value, i_arg, j_arg, substitute(value))
   12. tibble:::tbl_subassign_row(xj, i, value, i_arg, value_arg, call)
   14. tibble:::vectbl_assign(x[[j]], i, recycled_value[[j]])
  ────────────────────────────────────────────────────────────────────────────────
  
  ⠏ |         0 | interaction_constraints                                         
  ⠏ |         0 | interaction constraints                                         
  ⠋ |         1 | interaction constraints                                         
  ⠙ |         2 | interaction constraints                                         
  ✔ |         2 | interaction constraints [9.3s]
  
  ⠏ |         0 | interactions                                                    
  ⠏ |         0 | Test prediction of feature interactions                         
  ⠋ |         1 | Test prediction of feature interactions                         
  ⠙ |         2 | Test prediction of feature interactions                         
  ⠸ |         4 | Test prediction of feature interactions                         
  ⠹ |        13 | Test prediction of feature interactions                         [1]	train-logloss:0.482541 
  [2]	train-logloss:0.359536 
  [3]	train-logloss:0.279935 
  [4]	train-logloss:0.218599 
  
  ⠧ |        18 | Test prediction of feature interactions                         
  ⠇ |        19 | Test prediction of feature interactions                         
  ✔ |        19 | Test prediction of feature interactions [3.2s]
  
  ⠏ |         0 | io                                                              
  ⠏ |         0 | Test model IO.                                                  [1]	train-logloss:0.439409 
  [2]	train-logloss:0.299260 
  [3]	train-logloss:0.209937 
  [4]	train-logloss:0.150151 
  [5]	train-logloss:0.108673 
  [6]	train-logloss:0.079348 
  [7]	train-logloss:0.058385 
  [8]	train-logloss:0.043147 
  
  ⠋ |         1 | Test model IO.                                                  
  ✔ |         2 | Test model IO. [0.2s]
  
  ⠏ |         0 | model_compatibility                                             
  ⠏ |         0 | Models from previous versions of XGBoost can be loaded          
  ⠋ |         1 | Models from previous versions of XGBoost can be loaded          
  ⠧ |         8 | Models from previous versions of XGBoost can be loaded          
  ⠹ |        23 | Models from previous versions of XGBoost can be loaded          
  ⠇ |        39 | Models from previous versions of XGBoost can be loaded          
  ⠼ |        55 | Models from previous versions of XGBoost can be loaded          
  ⠧ |        78 | Models from previous versions of XGBoost can be loaded          
  ⠏ |       100 | Models from previous versions of XGBoost can be loaded          
  ⠋ |       121 | Models from previous versions of XGBoost can be loaded          
  ⠸ |       134 | Models from previous versions of XGBoost can be loaded          
  ⠏ |       150 | Models from previous versions of XGBoost can be loaded          
  ⠹ |       163 | Models from previous versions of XGBoost can be loaded          
  ⠼ |       175 | Models from previous versions of XGBoost can be loaded          
  ⠴ |       186 | Models from previous versions of XGBoost can be loaded          
  ⠴ |       196 | Models from previous versions of XGBoost can be loaded          
  ⠏ |       210 | Models from previous versions of XGBoost can be loaded          
  ⠦ |       227 | Models from previous versions of XGBoost can be loaded          
  ✔ |       233 | Models from previous versions of XGBoost can be loaded [3.0s]
  
  ⠏ |         0 | monotone                                                        
  ⠏ |         0 | monotone constraints                                            
  ⠋ |         1 | monotone constraints                                            
  ✔ |         1 | monotone constraints [0.1s]
  
  ⠏ |         0 | parameter_exposure                                              
  ⠏ |         0 | Test model params and call are exposed to R                     
  ⠋ |         1 | Test model params and call are exposed to R                     
  ✔ |         6 | Test model params and call are exposed to R [0.1s]
  
  ⠏ |         0 | poisson_regression                                              
  ⠏ |         0 | Test Poisson regression model                                   
  ⠋ |         1 | Test Poisson regression model                                   
  ✔ |         3 | Test Poisson regression model [0.1s]
  
  ⠏ |         0 | ranking                                                         
  ⠏ |         0 | Learning to rank                                                [1]	train-auc:0.575000	train-aucpr:0.550000 
  [2]	train-auc:0.650000	train-aucpr:0.700000 
  [3]	train-auc:0.725000	train-aucpr:0.850000 
  [4]	train-auc:0.800000	train-aucpr:1.000000 
  [5]	train-auc:0.800000	train-aucpr:1.000000 
  [6]	train-auc:0.800000	train-aucpr:1.000000 
  [7]	train-auc:0.800000	train-aucpr:1.000000 
  [8]	train-auc:0.800000	train-aucpr:1.000000 
  [9]	train-auc:0.800000	train-aucpr:1.000000 
  [10]	train-auc:0.800000	train-aucpr:1.000000 
  [1]	train-auc:0.575000	train-aucpr:0.550000 
  [2]	train-auc:0.650000	train-aucpr:0.700000 
  [3]	train-auc:0.725000	train-aucpr:0.850000 
  [4]	train-auc:0.725000	train-aucpr:0.850000 
  [5]	train-auc:0.725000	train-aucpr:0.850000 
  [6]	train-auc:0.725000	train-aucpr:0.850000 
  [7]	train-auc:0.725000	train-aucpr:0.850000 
  [8]	train-auc:0.800000	train-aucpr:1.000000 
  [9]	train-auc:0.800000	train-aucpr:1.000000 
  [10]	train-auc:0.800000	train-aucpr:1.000000 
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  [03:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
  
  ✔ |        14 | Learning to rank
  
  ⠏ |         0 | update                                                          
  ⠏ |         0 | update trees in an existing model                               [03:45:52] WARNING: src/gbm/gbtree.cc:84: DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.
  
  ⠋ |         1 | update trees in an existing model                               [03:45:52] WARNING: src/gbm/gbtree.cc:84: DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.
  
  ⠹ |         3 | update trees in an existing model                               [03:45:52] WARNING: src/gbm/gbtree.cc:84: DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.
  [03:45:52] WARNING: src/gbm/gbtree.cc:84: DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.
  
  ⠇ |         9 | update trees in an existing model                               [03:45:52] WARNING: src/gbm/gbtree.cc:84: DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.
  
  ⠼ |        15 | update trees in an existing model                               [03:45:52] WARNING: src/gbm/gbtree.cc:84: DANGER AHEAD: You have manually specified `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.
  
  ✔ |        22 | update trees in an existing model [0.7s]
  
  ══ Results ═════════════════════════════════════════════════════════════════════
  Duration: 169.6 s
  
  [ FAIL 2 | WARN 2 | SKIP 0 | PASS 1324 ]
  Error: Test failures
  Execution halted