CRAN Package Check Results for Package mllrnrs

Last updated on 2026-07-24 11:50:23 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	0.0.8	5.73	330.36	336.09	OK
r-devel-linux-x86_64-debian-gcc	0.0.8	3.94	232.63	236.57	OK
r-devel-linux-x86_64-fedora-clang	0.0.8	9.00	424.41	433.41	ERROR
r-devel-linux-x86_64-fedora-gcc	0.0.8			194.18	ERROR
r-devel-windows-x86_64	0.0.8	7.00	329.00	336.00	OK
r-patched-linux-x86_64	0.0.8	5.37	351.15	356.52	OK
r-release-linux-x86_64	0.0.8	5.26	333.44	338.70	OK
r-release-macos-arm64	0.0.8	1.00	90.00	91.00	OK
r-release-macos-x86_64	0.0.8	4.00	404.00	408.00	OK
r-release-windows-x86_64	0.0.8	7.00	328.00	335.00	OK
r-oldrel-macos-arm64	0.0.8	1.00	91.00	92.00	OK
r-oldrel-macos-x86_64	0.0.8	4.00	283.00	287.00	OK
r-oldrel-windows-x86_64	0.0.8	10.00	454.00	464.00	OK

Check Details

Version: 0.0.8
Check: examples
Result: ERROR Running examples in ‘mllrnrs-Ex.R’ failed The error most likely occurred in: > ### Name: LearnerGlmnet > ### Title: R6 Class to construct a Glmnet learner > ### Aliases: LearnerGlmnet > > ### ** Examples > > # binary classification > if (requireNamespace("glmnet", quietly = TRUE) && + requireNamespace("mlbench", quietly = TRUE) && + requireNamespace("measures", quietly = TRUE)) { + + library(mlbench) + data("PimaIndiansDiabetes2") + dataset <- PimaIndiansDiabetes2 |> + data.table::as.data.table() |> + na.omit() + + seed <- 123 + feature_cols <- colnames(dataset)[1:8] + + train_x <- model.matrix( + ~ -1 + ., + dataset[, .SD, .SDcols = feature_cols] + ) + train_y <- as.integer(dataset[, get("diabetes")]) - 1L + + fold_list <- splitTools::create_folds( + y = train_y, + k = 3, + type = "stratified", + seed = seed + ) + glmnet_cv <- mlexperiments::MLCrossValidation$new( + learner = mllrnrs::LearnerGlmnet$new( + metric_optimization_higher_better = FALSE + ), + fold_list = fold_list, + ncores = 2, + seed = 123 + ) + glmnet_cv$learner_args <- list( + alpha = 1, + lambda = 0.1, + family = "binomial", + type.measure = "class", + standardize = TRUE + ) + glmnet_cv$predict_args <- list(type = "response") + glmnet_cv$performance_metric_args <- list(positive = "1", negative = "0") + glmnet_cv$performance_metric <- mlexperiments::metric("AUC") + + # set data + glmnet_cv$set_data( + x = train_x, + y = train_y + ) + + glmnet_cv$execute() + } Warning in data("PimaIndiansDiabetes2") : data set ‘PimaIndiansDiabetes2’ not found Error: object 'PimaIndiansDiabetes2' not found Execution halted Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 0.0.8
Check: tests
Result: ERROR Running ‘testthat.R’ [236s/396s] 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") Saving _problems/test-binary-5.R CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) OMP: Warning #96: Cannot form a team with 24 threads, using 2 instead. OMP: Hint Consider unsetting KMP_DEVICE_THREAD_LIMIT (KMP_ALL_THREADS), KMP_TEAMS_THREAD_LIMIT, and OMP_THREAD_LIMIT (if any are set). CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 4.877 Round = 1 alpha = 0.0500 Value = -0.03838112 elapsed = 4.679 Round = 2 alpha = 0.2000 Value = -0.03852748 elapsed = 5.148 Round = 3 alpha = 0.1500 Value = -0.03849621 elapsed = 5.111 Round = 4 alpha = 0.1000 Value = -0.03844983 elapsed = 5.179 Round = 5 alpha = 0.9927179 Value = -0.03865969 elapsed = 5.559 Round = 6 alpha = 0.6273975 Value = -0.03863518 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.03838112 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 4.807 Round = 1 alpha = 0.0500 Value = -0.03859583 elapsed = 4.671 Round = 2 alpha = 0.2000 Value = -0.03864684 elapsed = 5.18 Round = 3 alpha = 0.1500 Value = -0.03863035 elapsed = 4.685 Round = 4 alpha = 0.1000 Value = -0.03861402 elapsed = 5.002 Round = 5 alpha = 0.9927182 Value = -0.03871602 elapsed = 4.749 Round = 6 alpha = 0.6550449 Value = -0.03870422 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.03859583 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 4.953 Round = 1 alpha = 0.0500 Value = -0.04148682 elapsed = 4.739 Round = 2 alpha = 0.2000 Value = -0.04162914 elapsed = 4.604 Round = 3 alpha = 0.1500 Value = -0.04159226 elapsed = 5.217 Round = 4 alpha = 0.1000 Value = -0.04155432 elapsed = 5.32 Round = 5 alpha = 0.655018 Value = -0.04172817 elapsed = 4.812 Round = 6 alpha = 0.9927204 Value = -0.04175126 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.04148682 CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) 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. elapsed = 0.665 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1828011 elapsed = 0.931 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1653724 elapsed = 2.019 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1653406 elapsed = 1.173 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1987006 elapsed = 2.289 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1897082 elapsed = 1.329 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1743092 elapsed = 3.516 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2428898 elapsed = 0.392 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1921259 elapsed = 1.986 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2441279 elapsed = 5.955 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1810449 elapsed = 1.078 Round = 11 subsample = 0.7869579 colsample_bytree = 0.3807963 min_child_weight = 6.0000 learning_rate = 0.131633 max_depth = 5.0000 Value = -0.1820181 elapsed = 0.215 Round = 12 subsample = 0.8678558 colsample_bytree = 0.514649 min_child_weight = 9.0000 learning_rate = 0.1902419 max_depth = 5.0000 Value = -0.1780571 Best Parameters Found: Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1653406 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. elapsed = 0.403 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1839477 elapsed = 1.76 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.171506 elapsed = 1.083 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.164792 elapsed = 0.575 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.2047308 elapsed = 0.546 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1980206 elapsed = 0.824 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1807015 elapsed = 0.577 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.248052 elapsed = 3.738 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1960023 elapsed = 1.075 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2366496 elapsed = 0.459 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1922252 elapsed = 0.745 Round = 11 subsample = 0.7871648 colsample_bytree = 0.8134155 min_child_weight = 7.0000 learning_rate = 0.1555881 max_depth = 6.0000 Value = -0.1670881 elapsed = 0.564 Round = 12 subsample = 1.0000 colsample_bytree = 1.0000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 10.0000 Value = -0.1791919 Best Parameters Found: Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.164792 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. elapsed = 1.641 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1953069 elapsed = 0.298 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1786983 elapsed = 0.634 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1699451 elapsed = 0.406 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.2007858 elapsed = 0.13 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1922863 elapsed = 0.519 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1927776 elapsed = 0.864 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2455989 elapsed = 0.358 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1968986 elapsed = 0.645 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2433844 elapsed = 3.388 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1925334 elapsed = 0.372 Round = 11 subsample = 1.0000 colsample_bytree = 1.0000 min_child_weight = 10.0000 learning_rate = 0.2000 max_depth = 10.0000 Value = -0.1653477 elapsed = 2.08 Round = 12 subsample = 0.2000 colsample_bytree = 0.2000 min_child_weight = 10.0000 learning_rate = 0.2000 max_depth = 10.0000 Value = -0.2540175 Best Parameters Found: Round = 11 subsample = 1.0000 colsample_bytree = 1.0000 min_child_weight = 10.0000 learning_rate = 0.2000 max_depth = 10.0000 Value = -0.1653477 CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) [ FAIL 1 | WARN 4 | SKIP 2 | PASS 24 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • On CRAN (2): 'test-lints.R:10:5', 'test-multiclass.R:54:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-binary.R:3:1'): (code run outside of `test_that()`) ──────────── <objectNotFoundError/error/condition> Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found Backtrace: ▆ 1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-binary.R:3:1 2. └─data.table::as.data.table(PimaIndiansDiabetes2) [ FAIL 1 | WARN 4 | SKIP 2 | PASS 24 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.0.8
Check: tests
Result: ERROR Running ‘testthat.R’ [103s/134s] 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") Saving _problems/test-binary-5.R CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Classification: using 'mean classification error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Classification: using 'mean classification error' as optimization metric. CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 1.893 Round = 1 alpha = 0.0500 Value = -0.03838112 elapsed = 1.721 Round = 2 alpha = 0.2000 Value = -0.03852748 elapsed = 1.749 Round = 3 alpha = 0.1500 Value = -0.03849621 elapsed = 1.876 Round = 4 alpha = 0.1000 Value = -0.03844983 elapsed = 1.836 Round = 5 alpha = 0.9927179 Value = -0.03865969 elapsed = 1.686 Round = 6 alpha = 0.6273975 Value = -0.03863518 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.03838112 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 1.851 Round = 1 alpha = 0.0500 Value = -0.03859583 elapsed = 1.796 Round = 2 alpha = 0.2000 Value = -0.03864684 elapsed = 1.838 Round = 3 alpha = 0.1500 Value = -0.03863035 elapsed = 1.912 Round = 4 alpha = 0.1000 Value = -0.03861402 elapsed = 1.926 Round = 5 alpha = 0.9927182 Value = -0.03871602 elapsed = 1.612 Round = 6 alpha = 0.6550449 Value = -0.03870422 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.03859583 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 4 rows. elapsed = 1.744 Round = 1 alpha = 0.0500 Value = -0.04148682 elapsed = 1.679 Round = 2 alpha = 0.2000 Value = -0.04162914 elapsed = 1.597 Round = 3 alpha = 0.1500 Value = -0.04159226 elapsed = 1.727 Round = 4 alpha = 0.1000 Value = -0.04155432 elapsed = 1.896 Round = 5 alpha = 0.655018 Value = -0.04172817 elapsed = 2.081 Round = 6 alpha = 0.9927204 Value = -0.04175126 Best Parameters Found: Round = 1 alpha = 0.0500 Value = -0.04148682 CV fold: Fold1 Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold1 Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================>---------------] 2/3 ( 67%) Regression: using 'mean squared error' as optimization metric. Parameter settings [=============================================] 3/3 (100%) 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. elapsed = 0.202 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1799936 elapsed = 0.15 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1601449 elapsed = 0.128 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1697138 elapsed = 0.525 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1959483 elapsed = 0.383 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.174398 elapsed = 0.283 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1651243 elapsed = 0.072 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2448137 elapsed = 0.12 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1877671 elapsed = 0.069 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2406981 elapsed = 0.142 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1756064 elapsed = 0.114 Round = 11 subsample = 0.6432993 colsample_bytree = 0.9835629 min_child_weight = 6.0000 learning_rate = 0.1935054 max_depth = 5.0000 Value = -0.1698066 elapsed = 0.331 Round = 12 subsample = 0.6452221 colsample_bytree = 0.7403856 min_child_weight = 2.0000 learning_rate = 0.1747095 max_depth = 5.0000 Value = -0.1618912 Best Parameters Found: Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1601449 CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. elapsed = 0.227 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1782907 elapsed = 0.315 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1706168 elapsed = 0.177 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1639677 elapsed = 0.11 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1872621 elapsed = 0.121 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1707442 elapsed = 0.085 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1688216 elapsed = 0.059 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2412666 elapsed = 0.184 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1773589 elapsed = 0.489 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2395262 elapsed = 0.231 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1666513 elapsed = 0.392 Round = 11 subsample = 0.9888986 colsample_bytree = 0.7844255 min_child_weight = 6.0000 learning_rate = 0.1779883 max_depth = 6.0000 Value = -0.1602653 elapsed = 0.936 Round = 12 subsample = 0.9697736 colsample_bytree = 0.5113479 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 10.0000 Value = -0.1638984 Best Parameters Found: Round = 11 subsample = 0.9888986 colsample_bytree = 0.7844255 min_child_weight = 6.0000 learning_rate = 0.1779883 max_depth = 6.0000 Value = -0.1602653 CV fold: Fold3 CV progress [====================================================] 3/3 (100%) Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'... ... reducing initialization grid to 10 rows. elapsed = 0.201 Round = 1 subsample = 0.8000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1912842 elapsed = 0.107 Round = 2 subsample = 0.6000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1757159 elapsed = 0.528 Round = 3 subsample = 0.8000 colsample_bytree = 0.8000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1813096 elapsed = 0.068 Round = 4 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 5.0000 Value = -0.1983487 elapsed = 0.125 Round = 5 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1792433 elapsed = 0.275 Round = 6 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 5.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1760175 elapsed = 0.182 Round = 7 subsample = 0.6000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2448583 elapsed = 0.417 Round = 8 subsample = 0.4000 colsample_bytree = 0.4000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1912984 elapsed = 0.06 Round = 9 subsample = 0.4000 colsample_bytree = 0.8000 min_child_weight = 1.0000 learning_rate = 0.1000 max_depth = 1.0000 Value = -0.2440582 elapsed = 0.096 Round = 10 subsample = 0.4000 colsample_bytree = 0.6000 min_child_weight = 1.0000 learning_rate = 0.2000 max_depth = 5.0000 Value = -0.1812046 elapsed = 0.112 Round = 11 subsample = 0.4862949 colsample_bytree = 0.8329869 min_child_weight = 10.0000 learning_rate = 0.2000 max_depth = 6.0000 Value = -0.173082 elapsed = 0.124 Round = 12 subsample = 1.0000 colsample_bytree = 0.2000 min_child_weight = 10.0000 learning_rate = 0.2000 max_depth = 10.0000 Value = -0.204729 Best Parameters Found: Round = 11 subsample = 0.4862949 colsample_bytree = 0.8329869 min_child_weight = 10.0000 learning_rate = 0.2000 max_depth = 6.0000 Value = -0.173082 CV fold: Fold1 Parameter settings [=============================>---------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold2 CV progress [==================================>-----------------] 2/3 ( 67%) Parameter settings [=============================================] 3/3 (100%) CV fold: Fold3 CV progress [====================================================] 3/3 (100%) [ FAIL 1 | WARN 4 | SKIP 2 | PASS 24 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • On CRAN (2): 'test-lints.R:10:5', 'test-multiclass.R:54:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-binary.R:3:1'): (code run outside of `test_that()`) ──────────── <objectNotFoundError/error/condition> Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found Backtrace: ▆ 1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-binary.R:3:1 2. └─data.table::as.data.table(PimaIndiansDiabetes2) [ FAIL 1 | WARN 4 | SKIP 2 | PASS 24 ] Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc