R Under development (unstable) (2023-09-20 r85183 ucrt) -- "Unsuffered Consequences" Copyright (C) 2023 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 [01:10:48] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [1] train-rmse:1.673884 [2] train-rmse:1.673884 [3] train-rmse:1.499310 [4] train-rmse:1.368343 [5] train-rmse:1.240554 [6] train-rmse:1.273484 [7] train-rmse:1.160811 [8] train-rmse:1.125973 [9] train-rmse:1.153294 [10] train-rmse:1.097217 [11] train-rmse:1.093373 [12] train-rmse:1.103536 [13] train-rmse:1.071194 [14] train-rmse:1.077706 [15] train-rmse:1.086782 [16] train-rmse:1.088217 [17] train-rmse:1.097977 [18] train-rmse:1.104176 [19] train-rmse:1.112740 [20] train-rmse:1.116199 [21] train-rmse:1.125133 [22] train-rmse:1.140310 [23] train-rmse:1.142462 [24] train-rmse:1.153596 [25] train-rmse:1.165751 [26] train-rmse:1.172017 [27] train-rmse:1.175089 [28] train-rmse:1.176897 [29] train-rmse:1.177296 [30] train-rmse:1.178395 [31] train-rmse:1.179324 [32] train-rmse:1.178905 [01:10:48] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. ⠸ | 14 | basic functions [01:10:48] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:10:48] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. ⠇ | 29 | basic functions [1] train-error:0.055581 [01:10:48] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. ⠙ | 42 | basic functions [01:10:48] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. ⠋ | 61 | basic functions [1] train-logloss:0.439333 [2] train-logloss:0.298250 [3] train-logloss:0.209268 [4] train-logloss:0.149690 ⠴ | 66 | basic functions [1] train-logloss:0.233238+0.001543 test-logloss:0.233373+0.004959 [2] train-logloss:0.136728+0.001808 test-logloss:0.136889+0.006601 [1] train-logloss:0.233260+0.001793 test-logloss:0.233307+0.005568 [2] train-logloss:0.136743+0.002172 test-logloss:0.136818+0.007295 ⠼ | 75 | basic functions ⠏ | 80 | basic functions [1] train-logloss:0.380058 [2] train-logloss:0.247209 [3] train-logloss:0.174862 [4] train-logloss:0.122213 [5] train-logloss:0.089849 [1] train-logloss:0.496540 [2] train-logloss:0.356014 [3] train-logloss:0.254303 [4] train-logloss:0.187174 [5] train-logloss:0.139038 ⠧ | 88 | basic functions [1] train-error:0.046522 train-auc:0.958228 train-logloss:0.233156 [2] train-error:0.022263 train-auc:0.981413 train-logloss:0.136542 [1] train-merror:0.040000 [2] train-merror:0.026667 [1] train-error:0.046522 train-auc:0.958228 train-logloss:0.481958 [2] train-error:0.046522 train-auc:0.987161 train-logloss:0.359194 ⠧ | 98 | basic functions ✔ | 104 | basic functions [3.7s] ⠏ | 0 | callbacks ⠏ | 0 | callbacks [1] train-auc:0.900000 test-auc:0.800000 ⠦ | 27 | callbacks ⠹ | 43 | callbacks [01:10:52] WARNING: src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified. ⠹ | 1 62 | callbacks [01:10:52] WARNING: src/c_api/c_api.cc:1081: `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:0.831941 [5] train-auc:0.831941 [6] train-auc:0.832835 [7] train-auc:0.832835 [8] train-auc:0.832835 [9] train-auc:0.832835 Stopping. Best iteration: [6] train-auc:0.832835 ⠏ | 1 69 | callbacks ⠇ | 1 78 | callbacks ⠼ | 1 84 | callbacks ✔ | 1 95 | callbacks ──────────────────────────────────────────────────────────────────────────────── Warning ('test_callbacks.R:193:3'): cb.save.model works as expected one argument not used by format 'xgboost.json' Backtrace: ▆ 1. └─xgboost::xgb.train(...) at test_callbacks.R:193:2 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 ✔ | 12 | Test models with custom objective ⠏ | 0 | dmatrix ⠏ | 0 | testing xgb.DMatrix functionality [1] train-rmse:0.463687 [2] train-rmse:0.441245 [3] train-rmse:0.426704 [4] train-rmse:0.417565 [5] train-rmse:0.411911 [6] train-rmse:0.408288 [7] train-rmse:0.406064 [8] train-rmse:0.404508 [1] train-rmse:0.463687 [2] train-rmse:0.441245 [3] train-rmse:0.426704 [4] train-rmse:0.417565 [5] train-rmse:0.411911 [6] train-rmse:0.408288 [7] train-rmse:0.406064 [8] train-rmse:0.404508 [1] train-rmse:0.463687 [2] train-rmse:0.441245 [3] train-rmse:0.426704 [4] train-rmse:0.417565 [5] train-rmse:0.411911 [6] train-rmse:0.408288 [7] train-rmse:0.406064 [8] train-rmse:0.404508 ⠴ | 16 | testing xgb.DMatrix functionality [01:10:53] 6513x126 matrix with 143286 entries loaded from D:\temp\Rtmp4KLuzd\xgb.DMatrix_23d433077b07 ⠇ | 1 38 | testing xgb.DMatrix functionality ⠴ | 1 45 | testing xgb.DMatrix functionality ✔ | 1 52 | testing xgb.DMatrix functionality ──────────────────────────────────────────────────────────────────────────────── Warning ('test_dmatrix.R:158:3'): 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:158:2 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:0.233156 [2] train-logloss:0.136542 ⠋ | 1 | Garbage Collection Safety Check ✔ | 1 | Garbage Collection Safety Check [33.0s] ⠏ | 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:0.636514 ⠴ | 46 | Test helper functions [1] train-rmse:1.562893 [2] train-rmse:1.287330 [3] train-rmse:1.072544 [4] train-rmse:0.907420 [5] train-rmse:0.799787 [6] train-rmse:0.688437 [7] train-rmse:0.613057 [8] train-rmse:0.553188 [9] train-rmse:0.478449 [10] train-rmse:0.431064 [11] train-rmse:0.379525 [12] train-rmse:0.348680 [13] train-rmse:0.305695 [14] train-rmse:0.282344 [15] train-rmse:0.259822 [16] train-rmse:0.240520 [17] train-rmse:0.218238 [18] train-rmse:0.204584 [19] train-rmse:0.193815 [20] train-rmse:0.180568 [21] train-rmse:0.165250 [22] train-rmse:0.155912 [23] train-rmse:0.141602 [24] train-rmse:0.126580 [25] train-rmse:0.114774 [26] train-rmse:0.110963 [27] train-rmse:0.107689 [28] train-rmse:0.098438 [29] train-rmse:0.096071 [30] train-rmse:0.085989 ⠏ | 100 | Test helper functions ⠹ | 223 | Test helper functions [1] train-rmse:1.562893 [2] train-rmse:1.369877 [3] train-rmse:1.212888 [4] train-rmse:1.075445 [5] train-rmse:0.972425 [6] train-rmse:0.882535 [7] train-rmse:0.810012 [8] train-rmse:0.754729 [9] train-rmse:0.701319 [10] train-rmse:0.641273 [11] train-rmse:0.596999 [12] train-rmse:0.537648 [13] train-rmse:0.502804 [14] train-rmse:0.478473 [15] train-rmse:0.444775 [16] train-rmse:0.426076 [17] train-rmse:0.394513 [18] train-rmse:0.361599 [19] train-rmse:0.343141 [20] train-rmse:0.327435 [21] train-rmse:0.315436 [22] train-rmse:0.297298 [23] train-rmse:0.279117 [24] train-rmse:0.267233 [25] train-rmse:0.255586 [26] train-rmse:0.249762 [27] train-rmse:0.237249 [28] train-rmse:0.227048 [29] train-rmse:0.222748 [30] train-rmse:0.201239 ⠏ | 250 | Test helper functions ⠦ | 367 | Test helper functions [01:11:28] WARNING: src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified. ⠋ | 471 | Test helper functions ⠼ | 565 | Test helper functions ⠙ | 662 | Test helper functions [01:11:28] WARNING: src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified. ⠴ | 676 | 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 ⠏ | 1 719 | Test helper functions ✔ | 1 749 | Test helper functions [3.2s] ⠏ | 0 | interaction_constraints ⠏ | 0 | interaction constraints ⠋ | 1 | interaction constraints [01:11:34] WARNING: src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified. [01:11:34] WARNING: src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified. ⠙ | 2 | interaction constraints ✔ | 2 | interaction constraints [3.7s] ⠏ | 0 | interactions ⠏ | 0 | Test prediction of feature interactions ⠋ | 1 | Test prediction of feature interactions ⠸ | 4 | Test prediction of feature interactions ⠹ | 13 | Test prediction of feature interactions [1] train-logloss:0.481958 [2] train-logloss:0.359194 [3] train-logloss:0.279534 [4] train-logloss:0.218252 ⠧ | 18 | Test prediction of feature interactions ✔ | 19 | Test prediction of feature interactions ⠏ | 0 | io ⠏ | 0 | Test model IO. [1] train-logloss:0.439333 [2] train-logloss:0.298250 [3] train-logloss:0.209268 [4] train-logloss:0.149690 [5] train-logloss:0.108428 [6] train-logloss:0.079171 [7] train-logloss:0.058255 [8] train-logloss:0.043050 [01:11:35] WARNING: src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified. [01:11:35] WARNING: src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified. [01:11:35] WARNING: src/c_api/c_api.cc:1240: Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format will default to JSON in XGBoost 2.2 if not specified. ✔ | 2 | Test model IO. ⠏ | 0 | model_compatibility ⠏ | 0 | Models from previous versions of XGBoost can be loaded [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility ⠋ | 1 | Models from previous versions of XGBoost can be loaded [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility ⠙ | 32 | Models from previous versions of XGBoost can be loaded [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:36] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:37] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:37] WARNING: src/learner.cc:1071: Loading model from XGBoost < 1.0.0, consider saving it again for improved compatibility [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. ⠹ | 83 | Models from previous versions of XGBoost can be loaded [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. ⠋ | 151 | Models from previous versions of XGBoost can be loaded [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. [01:11:37] WARNING: src/common/error_msg.h:80: If you are loading a serialized model (like pickle in Python, RDS in R) or configuration generated by an older version of XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. See: https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html for more details about differences between saving model and serializing. [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. ⠇ | 209 | Models from previous versions of XGBoost can be loaded [01:11:37] WARNING: src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3. ✔ | 228 | Models from previous versions of XGBoost can be loaded [2.0s] ⠏ | 0 | monotone ⠏ | 0 | monotone constraints ✔ | 1 | monotone constraints ⠏ | 0 | parameter_exposure ⠏ | 0 | 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:0.650000 train-aucpr:0.700000 [4] train-auc:0.650000 train-aucpr:0.700000 [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:0.725000 train-aucpr:0.850000 [9] train-auc:0.725000 train-aucpr:0.850000 [10] train-auc:0.725000 train-aucpr:0.850000 [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `ntree_limit` is deprecated, use `iteration_range` instead. [01:11:37] WARNING: src/c_api/c_api.cc:1081: `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 [01:11:37] WARNING: src/common/error_msg.cc:33: You have manually specified the `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 ⠇ | 9 | update trees in an existing model ✔ | 22 | update trees in an existing model ══ Results ═════════════════════════════════════════════════════════════════════ Duration: 50.1 s ── Skipped tests (1) ─────────────────────────────────────────────────────────── • empty test (1): 'test_helpers.R:407:1' [ FAIL 0 | WARN 2 | SKIP 1 | PASS 1340 ] > > proc.time() user system elapsed 51.21 2.34 51.56