Last updated on 2026-07-26 18:49:03 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.0.8 | 6.03 | 332.67 | 338.70 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.0.8 | 3.68 | 182.63 | 186.31 | ERROR | |
| 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 | 4.86 | 275.33 | 280.19 | ERROR | |
| 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 |
Version: 0.0.8
Check: examples
Result: ERROR
Running examples in ‘mllrnrs-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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-debian-gcc, r-release-linux-x86_64
Version: 0.0.8
Check: tests
Result: ERROR
Running ‘testthat.R’ [93s/142s]
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 [=============================================] 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 [=============================================] 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 [=============================>---------------] 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 = 3.242 Round = 1 alpha = 0.0500 Value = -0.03838112
elapsed = 2.828 Round = 2 alpha = 0.2000 Value = -0.03852748
elapsed = 2.608 Round = 3 alpha = 0.1500 Value = -0.03849621
elapsed = 2.294 Round = 4 alpha = 0.1000 Value = -0.03844983
elapsed = 2.511 Round = 5 alpha = 0.9927179 Value = -0.03865969
elapsed = 2.537 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 = 2.403 Round = 1 alpha = 0.0500 Value = -0.03859583
elapsed = 2.389 Round = 2 alpha = 0.2000 Value = -0.03864684
elapsed = 2.247 Round = 3 alpha = 0.1500 Value = -0.03863035
elapsed = 2.269 Round = 4 alpha = 0.1000 Value = -0.03861402
elapsed = 2.549 Round = 5 alpha = 0.9927182 Value = -0.03871602
elapsed = 2.60 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 = 2.437 Round = 1 alpha = 0.0500 Value = -0.04148682
elapsed = 2.581 Round = 2 alpha = 0.2000 Value = -0.04162914
elapsed = 2.327 Round = 3 alpha = 0.1500 Value = -0.04159226
elapsed = 2.233 Round = 4 alpha = 0.1000 Value = -0.04155432
elapsed = 2.211 Round = 5 alpha = 0.655018 Value = -0.04172817
elapsed = 2.377 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 [=============================================] 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 = 0.071 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 = 0.207 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 = 0.107 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 = 0.102 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 = 0.095 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 = 0.129 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 = 0.063 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 = 0.113 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 = 0.064 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 = 0.127 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 = 0.079 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 = 0.068 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 = 0.079 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 = 0.094 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 = 0.081 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 = 0.045 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 = 0.115 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 = 0.074 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 = 0.06 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 = 0.071 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 = 0.088 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 = 0.113 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 = 0.072 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 = 0.298 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 = 0.138 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 = 0.085 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 = 0.078 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 = 0.075 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 = 0.096 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 = 0.125 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 = 0.052 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 = 0.084 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 = 0.052 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 = 0.33 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 = 0.12 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 = 0.09 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
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
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-debian-gcc
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
Version: 0.0.8
Check: tests
Result: ERROR
Running ‘testthat.R’ [161s/258s]
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 = 3.45 Round = 1 alpha = 0.0500 Value = -0.03838112
elapsed = 3.658 Round = 2 alpha = 0.2000 Value = -0.03852748
elapsed = 3.514 Round = 3 alpha = 0.1500 Value = -0.03849621
elapsed = 3.514 Round = 4 alpha = 0.1000 Value = -0.03844983
elapsed = 4.257 Round = 5 alpha = 0.9927179 Value = -0.03865969
elapsed = 3.751 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 = 4.063 Round = 1 alpha = 0.0500 Value = -0.03859583
elapsed = 3.595 Round = 2 alpha = 0.2000 Value = -0.03864684
elapsed = 3.74 Round = 3 alpha = 0.1500 Value = -0.03863035
elapsed = 3.636 Round = 4 alpha = 0.1000 Value = -0.03861402
elapsed = 3.089 Round = 5 alpha = 0.9927182 Value = -0.03871602
elapsed = 4.252 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 = 4.082 Round = 1 alpha = 0.0500 Value = -0.04148682
elapsed = 3.994 Round = 2 alpha = 0.2000 Value = -0.04162914
elapsed = 3.973 Round = 3 alpha = 0.1500 Value = -0.04159226
elapsed = 3.545 Round = 4 alpha = 0.1000 Value = -0.04155432
elapsed = 3.299 Round = 5 alpha = 0.655018 Value = -0.04172817
elapsed = 2.99 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 = 0.224 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 = 0.263 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 = 0.185 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 = 0.254 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 = 0.132 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 = 0.158 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 = 0.199 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 = 0.186 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 = 0.129 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 = 0.174 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 = 0.471 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 = 0.219 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 = 3.005 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 = 0.386 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 = 1.494 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 = 0.522 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 = 0.184 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 = 0.172 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 = 0.294 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 = 0.203 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 = 0.108 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 = 0.179 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 = 0.243 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 = 1.255 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 = 2.647 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 = 0.876 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 = 0.167 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 = 1.42 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 = 0.711 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 = 0.43 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 = 0.358 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 = 0.094 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 = 0.428 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 = 0.189 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 = 0.208 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 = 0.154 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%)
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()`) ────────────
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-release-linux-x86_64