Last updated on 2025-12-24 09:50:21 CET.
| Package | ERROR | OK |
|---|---|---|
| autonewsmd | 13 | |
| BiasCorrector | 13 | |
| DQAgui | 13 | |
| DQAstats | 13 | |
| kdry | 13 | |
| mlexperiments | 4 | 9 |
| mllrnrs | 13 | |
| mlsurvlrnrs | 13 | |
| rBiasCorrection | 13 | |
| sjtable2df | 13 |
Current CRAN status: OK: 13
Current CRAN status: OK: 13
Current CRAN status: OK: 13
Current CRAN status: OK: 13
Current CRAN status: OK: 13
Current CRAN status: ERROR: 4, OK: 9
Version: 0.0.8
Check: examples
Result: ERROR
Running examples in ‘mlexperiments-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 0.0.8
Check: tests
Result: ERROR
Running ‘testthat.R’ [182s/468s]
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)... 25.821 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.966 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)... 27.299 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.047 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)... 12.549 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.107 seconds
3) Running FUN 2 times in 2 thread(s)... 4.992 seconds
CV fold: Fold1
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 15.024 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.182 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)... 15.514 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.76 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)... 14.044 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.209 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)... 25.531 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.07 seconds
3) Running FUN 2 times in 2 thread(s)... 3.963 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)... 12.95 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.055 seconds
3) Running FUN 2 times in 2 thread(s)... 2.257 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)... 13.459 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.165 seconds
3) Running FUN 2 times in 2 thread(s)... 2.238 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)... 14.172 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.91 seconds
3) Running FUN 2 times in 2 thread(s)... 2.592 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)... 6.354 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.301 seconds
3) Running FUN 2 times in 2 thread(s)... 0.677 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)... 5.334 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.997 seconds
3) Running FUN 2 times in 2 thread(s)... 0.352 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)... 4.992 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.189 seconds
3) Running FUN 2 times in 2 thread(s)... 0.472 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)... 6.47 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.051 seconds
3) Running FUN 2 times in 2 thread(s)... 0.559 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-linux-x86_64-debian-clang
Version: 0.0.8
Check: tests
Result: ERROR
Running ‘testthat.R’ [132s/384s]
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)... 26.929 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.587 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)... 29.539 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.687 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)... 13.247 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.905 seconds
3) Running FUN 2 times in 2 thread(s)... 4.051 seconds
CV fold: Fold1
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 10.801 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.858 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)... 13.759 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.526 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)... 9.464 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.566 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)... 22.365 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.625 seconds
3) Running FUN 2 times in 2 thread(s)... 2.873 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)... 12.701 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.766 seconds
3) Running FUN 2 times in 2 thread(s)... 2.5 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)... 12.272 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.907 seconds
3) Running FUN 2 times in 2 thread(s)... 3.209 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)... 11.953 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.891 seconds
3) Running FUN 2 times in 2 thread(s)... 2.999 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)... 4.45 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.573 seconds
3) Running FUN 2 times in 2 thread(s)... 0.702 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)... 3.248 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.665 seconds
3) Running FUN 2 times in 2 thread(s)... 0.598 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)... 4.349 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.996 seconds
3) Running FUN 2 times in 2 thread(s)... 0.588 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)... 4.035 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 0.744 seconds
3) Running FUN 2 times in 2 thread(s)... 0.365 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-linux-x86_64-debian-gcc
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
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’ [5m/15m]
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)... 75.409 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.954 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)... 77.037 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.66 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)... 29.606 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.937 seconds
3) Running FUN 2 times in 2 thread(s)... 10.967 seconds
CV fold: Fold1
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 34.584 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.896 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)... 23.424 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.281 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)... 35.142 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.429 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)... 40.728 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.139 seconds
3) Running FUN 2 times in 2 thread(s)... 7.196 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)... 22.844 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.087 seconds
3) Running FUN 2 times in 2 thread(s)... 4.535 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)... 24.945 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.651 seconds
3) Running FUN 2 times in 2 thread(s)... 3.887 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)... 23.217 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.89 seconds
3) Running FUN 2 times in 2 thread(s)... 3.093 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)... 7.897 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.636 seconds
3) Running FUN 2 times in 2 thread(s)... 0.767 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)... 8.308 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.338 seconds
3) Running FUN 2 times in 2 thread(s)... 0.682 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)... 10.865 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.748 seconds
3) Running FUN 2 times in 2 thread(s)... 0.75 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)... 9.606 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.309 seconds
3) Running FUN 2 times in 2 thread(s)... 1.017 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-linux-x86_64-fedora-clang
Version: 0.0.8
Check: tests
Result: ERROR
Running ‘testthat.R’ [6m/18m]
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)... 79.505 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.474 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)... 101.568 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.043 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)... 31.072 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.553 seconds
3) Running FUN 2 times in 2 thread(s)... 14.023 seconds
CV fold: Fold1
Registering parallel backend using 2 cores.
Running initial scoring function 11 times in 2 thread(s)... 65.393 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.358 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)... 65.698 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.687 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)... 65.596 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.653 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)... 50.318 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.838 seconds
3) Running FUN 2 times in 2 thread(s)... 12.42 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)... 28.801 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.623 seconds
3) Running FUN 2 times in 2 thread(s)... 4.596 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)... 28.638 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.623 seconds
3) Running FUN 2 times in 2 thread(s)... 4.225 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)... 32.889 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.397 seconds
3) Running FUN 2 times in 2 thread(s)... 5.596 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)... 6.279 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.184 seconds
3) Running FUN 2 times in 2 thread(s)... 0.663 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)... 8.577 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.691 seconds
3) Running FUN 2 times in 2 thread(s)... 0.919 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)... 8.847 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 1.82 seconds
3) Running FUN 2 times in 2 thread(s)... 0.667 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)... 7.705 seconds
Starting Epoch 1
1) Fitting Gaussian Process...
2) Running local optimum search... 2.016 seconds
3) Running FUN 2 times in 2 thread(s)... 0.949 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-linux-x86_64-fedora-gcc
Current CRAN status: OK: 13
Current CRAN status: OK: 13
Current CRAN status: OK: 13
Current CRAN status: OK: 13