R Under development (unstable) (2024-07-23 r86915 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(testthat) > library(xgboost) > > test_check("xgboost", reporter = ProgressReporter) ✔ | F W S OK | Context ⠏ | 0 | basic ⠏ | 0 | basic functions ⠸ | 4 | basic functions [13:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [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 [13:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. ⠧ | 18 | basic functions [13:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [1] train-error:0.028405 ⠦ | 37 | basic functions [13:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. ⠧ | 48 | 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 [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 ⠹ | 73 | basic functions ⠇ | 79 | 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 [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 ⠼ | 85 | basic functions [1] train-merror:0.040000 [2] train-merror:0.026667 [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 ✔ | 100 | basic functions [4.5s] ⠏ | 0 | callbacks ⠏ | 0 | callbacks [1] train-auc:0.900000 test-auc:0.800000 ⠴ | 26 | callbacks ⠇ | 39 | callbacks ⠙ | 1 51 | callbacks ⠹ | 1 62 | callbacks [13:47:47] 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 69 | callbacks ⠼ | 1 74 | callbacks ⠇ | 1 78 | callbacks ⠼ | 1 84 | callbacks ✔ | 1 95 | callbacks [1.3s] ──────────────────────────────────────────────────────────────────────────────── Warning ('test_callbacks.R:200:3'): cb.save.model works as expected one argument not used by format 'xgboost.json' Backtrace: ▆ 1. └─xgboost::xgb.train(...) at test_callbacks.R:200:3 2. └─xgboost (local) f() 3. ├─xgboost::xgb.save(env$bst, sprintf(save_name, env$iteration)) 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 ⠼ | 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 [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 ⠏ | 0 | dmatrix ⠏ | 0 | testing xgb.DMatrix functionality [13:47:48] 6513x126 matrix with 143286 entries loaded from D:\temp\RtmpcjPRCP\xgb.DMatrix_1f75c59785883 ⠹ | 23 | testing xgb.DMatrix functionality ⠇ | 1 38 | testing xgb.DMatrix functionality ✔ | 1 44 | testing xgb.DMatrix functionality ──────────────────────────────────────────────────────────────────────────────── Warning ('test_dmatrix.R:125:3'): 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:125:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. ├─xgboost::setinfo(dtest, "weight", rep("a", nrow(test_data))) 7. └─xgboost:::setinfo.xgb.DMatrix(dtest, "weight", rep("a", nrow(test_data))) ──────────────────────────────────────────────────────────────────────────────── ⠏ | 0 | feature_weights ⠏ | 0 | feature weights ⠹ | 3 | feature weights ✔ | 6 | feature weights ⠏ | 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 [46.7s] ⠏ | 0 | glm ⠏ | 0 | Test generalized linear models ⠴ | 6 | 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 | helpers ⠏ | 0 | Test helper functions ⠋ | 1 | Test helper functions [1] train-logloss:0.636592 ⠇ | 29 | 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 ⠸ | 54 | Test helper functions ⠧ | 148 | Test helper functions ⠋ | 241 | 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 ⠹ | 253 | Test helper functions ⠧ | 348 | Test helper functions ⠙ | 442 | Test helper functions ⠙ | 492 | Test helper functions ⠋ | 561 | Test helper functions ⠹ | 633 | Test helper functions ⠹ | 673 | Test helper functions ⠼ | 685 | Test helper functions ⠴ | 686 | Test helper functions [1] train-rmse:0.943398 ⠏ | 700 | Test helper functions ⠋ | 701 | Test helper functions ⠙ | 1 701 | Test helper functions ⠸ | 1 703 | Test helper functions ⠇ | 1 718 | Test helper functions ✔ | 1 749 | Test helper functions [3.3s] ⠏ | 0 | interaction_constraints ⠏ | 0 | interaction constraints ⠋ | 1 | interaction constraints ⠙ | 2 | interaction constraints ✔ | 2 | interaction constraints [3.6s] ⠏ | 0 | interactions ⠏ | 0 | Test prediction of feature interactions ⠋ | 1 | Test prediction of feature interactions ⠸ | 4 | Test prediction of feature interactions ⠋ | 11 | Test prediction of feature interactions ⠼ | 15 | 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 ✔ | 19 | Test prediction of feature interactions ⠏ | 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 | model_compatibility ⠏ | 0 | Models from previous versions of XGBoost can be loaded ⠋ | 1 | Models from previous versions of XGBoost can be loaded ⠇ | 19 | Models from previous versions of XGBoost can be loaded ⠼ | 75 | Models from previous versions of XGBoost can be loaded ⠋ | 131 | Models from previous versions of XGBoost can be loaded ⠼ | 185 | Models from previous versions of XGBoost can be loaded ✔ | 233 | Models from previous versions of XGBoost can be loaded [1.6s] ⠏ | 0 | monotone ⠏ | 0 | monotone constraints ✔ | 1 | monotone constraints ⠏ | 0 | parameter_exposure ⠏ | 0 | Test model params and call are exposed to R ⠸ | 4 | Test model params and call are exposed to R ✔ | 6 | Test model params and call are exposed to R ⠏ | 0 | poisson_regression ⠏ | 0 | Test Poisson regression model ✔ | 3 | Test Poisson regression model ⠏ | 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 [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. [13:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead. ✔ | 14 | Learning to rank ⠏ | 0 | unicode ⠏ | 0 | Test Unicode handling [1] train-error:0.046522 [2] train-error:0.022263 ✔ | 3 | Test Unicode handling ⠏ | 0 | update ⠏ | 0 | update trees in an existing model [13:48:46] 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. [13:48:46] 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. [13:48:46] 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 [13:48:46] 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. [13:48:46] 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. ⠦ | 7 | update trees in an existing model [13:48:46] 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. [13:48:46] 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 [13:48:46] 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 ══ Results ═════════════════════════════════════════════════════════════════════ Duration: 64.8 s ── Skipped tests (1) ─────────────────────────────────────────────────────────── • empty test (1): 'test_helpers.R:388:1' [ FAIL 0 | WARN 2 | SKIP 1 | PASS 1333 ] > > proc.time() user system elapsed 65.32 1.89 66.06