Last updated on 2025-12-26 11:52:14 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.0.8 | 11.98 | 286.03 | 298.01 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.0.8 | 8.19 | 218.41 | 226.60 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.0.8 | 21.00 | 467.24 | 488.24 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 0.0.8 | 21.00 | 627.98 | 648.98 | OK | |
| r-devel-windows-x86_64 | 0.0.8 | 13.00 | 406.00 | 419.00 | ERROR | |
| r-patched-linux-x86_64 | 0.0.8 | 10.73 | 271.95 | 282.68 | OK | |
| r-release-linux-x86_64 | 0.0.8 | 11.88 | 281.95 | 293.83 | OK | |
| r-release-macos-arm64 | 0.0.8 | OK | ||||
| r-release-macos-x86_64 | 0.0.8 | 7.00 | 362.00 | 369.00 | OK | |
| r-release-windows-x86_64 | 0.0.8 | 14.00 | 407.00 | 421.00 | OK | |
| r-oldrel-macos-arm64 | 0.0.8 | OK | ||||
| r-oldrel-macos-x86_64 | 0.0.8 | 7.00 | 377.00 | 384.00 | OK | |
| r-oldrel-windows-x86_64 | 0.0.8 | 20.00 | 571.00 | 591.00 | OK |
Version: 0.0.8
Check: examples
Result: ERROR
Running examples in 'mlexperiments-Ex.R' failed
The error most likely occurred in:
> ### Name: performance
> ### Title: performance
> ### Aliases: performance
>
> ### ** Examples
>
> dataset <- do.call(
+ cbind,
+ c(sapply(paste0("col", 1:6), function(x) {
+ rnorm(n = 500)
+ },
+ USE.NAMES = TRUE,
+ simplify = FALSE
+ ),
+ list(target = sample(0:1, 500, TRUE))
+ ))
>
> fold_list <- splitTools::create_folds(
+ y = dataset[, 7],
+ k = 3,
+ type = "stratified",
+ seed = 123
+ )
>
> glm_optimization <- mlexperiments::MLCrossValidation$new(
+ learner = LearnerGlm$new(),
+ fold_list = fold_list,
+ seed = 123
+ )
>
> glm_optimization$learner_args <- list(family = binomial(link = "logit"))
> glm_optimization$predict_args <- list(type = "response")
> glm_optimization$performance_metric_args <- list(
+ positive = "1",
+ negative = "0"
+ )
> glm_optimization$performance_metric <- list(
+ auc = metric("AUC"), sensitivity = metric("TPR"),
+ specificity = metric("TNR")
+ )
> glm_optimization$return_models <- TRUE
>
> # set data
> glm_optimization$set_data(
+ x = data.matrix(dataset[, -7]),
+ y = dataset[, 7]
+ )
>
> cv_results <- glm_optimization$execute()
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
>
> # predictions
> preds <- mlexperiments::predictions(
+ object = glm_optimization,
+ newdata = data.matrix(dataset[, -7]),
+ na.rm = FALSE,
+ ncores = 2L,
+ type = "response"
+ )
Error in `[.data.table`(res, , `:=`(mean = mean(as.numeric(.SD), na.rm = na.rm), :
attempt access index 3/3 in VECTOR_ELT
Calls: <Anonymous> -> [ -> [.data.table
Execution halted
Flavor: r-devel-windows-x86_64
Version: 0.0.8
Check: tests
Result: ERROR
Running 'testthat.R' [296s]
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
>
> Sys.setenv("OMP_THREAD_LIMIT" = 2)
> Sys.setenv("Ncpu" = 2)
>
> library(testthat)
> library(mlexperiments)
>
> test_check("mlexperiments")
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold4
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold5
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
CV fold: Fold4
CV fold: Fold5
Testing for identical folds in 2 and 1.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold4
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold5
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold4
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold5
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold4
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
CV fold: Fold5
Parameter 'ncores' is ignored for learner 'LearnerGlm'.
Saving _problems/test-glm_predictions-79.R
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold4
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold5
Parameter 'ncores' is ignored for learner 'LearnerLm'.
Saving _problems/test-glm_predictions-188.R
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 22.5 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.64 seconds
Noise could not be added to find unique parameter set. Stopping process and returning results so far.
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 23.36 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.64 seconds
Noise could not be added to find unique parameter set. Stopping process and returning results so far.
Registering parallel backend using 2 cores.
Running initial scoring function 4 times in 2 thread(s)... 8.95 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.52 seconds
3) Running FUN 2 times in 2 thread(s)... 3.55 seconds
CV fold: Fold1
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 8.58 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.57 seconds
Noise could not be added to find unique parameter set. Stopping process and returning results so far.
CV fold: Fold2
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 8.45 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.56 seconds
Noise could not be added to find unique parameter set. Stopping process and returning results so far.
CV fold: Fold3
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 8.23 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.58 seconds
Noise could not be added to find unique parameter set. Stopping process and returning results so far.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 18.5 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.45 seconds
3) Running FUN 2 times in 2 thread(s)... 3.5 seconds
Classification: using 'mean misclassification error' as optimization metric.
Classification: using 'mean misclassification error' as optimization metric.
Classification: using 'mean misclassification 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.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 8.56 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.47 seconds
3) Running FUN 2 times in 2 thread(s)... 1.36 seconds
CV fold: Fold2
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 8.78 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.46 seconds
3) Running FUN 2 times in 2 thread(s)... 1.46 seconds
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 9.33 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.47 seconds
3) Running FUN 2 times in 2 thread(s)... 1.5 seconds
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Classification: using 'mean misclassification error' as optimization metric.
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
Classification: using 'mean misclassification error' as optimization metric.
Classification: using 'mean misclassification error' as optimization metric.
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
Classification: using 'mean misclassification error' as optimization metric.
Classification: using 'mean misclassification error' as optimization metric.
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 2.76 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.49 seconds
3) Running FUN 2 times in 2 thread(s)... 0.18 seconds
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
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.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 2.75 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.45 seconds
3) Running FUN 2 times in 2 thread(s)... 0.24 seconds
CV fold: Fold2
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 2.79 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.42 seconds
3) Running FUN 2 times in 2 thread(s)... 0.19 seconds
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Registering parallel backend using 2 cores.
Running initial scoring function 10 times in 2 thread(s)... 2.78 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.46 seconds
3) Running FUN 2 times in 2 thread(s)... 0.19 seconds
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold2
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold3
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 68 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glm_predictions.R:73:5'): test predictions, binary - glm ───────
Error in ``[.data.table`(res, , `:=`(mean = mean(as.numeric(.SD), na.rm = na.rm), sd = stats::sd(as.numeric(.SD), na.rm = na.rm)), .SDcols = colnames(res), by = seq_len(nrow(res)))`: attempt access index 5/5 in VECTOR_ELT
Backtrace:
▆
1. └─mlexperiments::predictions(...) at test-glm_predictions.R:73:5
2. ├─...[]
3. └─data.table:::`[.data.table`(...)
── Error ('test-glm_predictions.R:182:5'): test predictions, regression - lm ───
Error in ``[.data.table`(res, , `:=`(mean = mean(as.numeric(.SD), na.rm = na.rm), sd = stats::sd(as.numeric(.SD), na.rm = na.rm)), .SDcols = colnames(res), by = seq_len(nrow(res)))`: attempt access index 5/5 in VECTOR_ELT
Backtrace:
▆
1. └─mlexperiments::predictions(...) at test-glm_predictions.R:182:5
2. ├─...[]
3. └─data.table:::`[.data.table`(...)
[ FAIL 2 | WARN 0 | SKIP 1 | PASS 68 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-windows-x86_64