Last updated on 2026-08-02 21:53:40 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 10.56 | 484.28 | 494.84 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.0.1 | 7.48 | 362.25 | 369.73 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.0.1 | 10.00 | 470.71 | 480.71 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.1 | 312.70 | OK | |||
| r-devel-windows-x86_64 | 1.0.0 | 13.00 | 452.00 | 465.00 | ERROR | |
| r-patched-linux-x86_64 | 1.0.0 | 12.60 | 481.50 | 494.10 | ERROR | |
| r-release-linux-x86_64 | 1.0.0 | 10.83 | 488.74 | 499.57 | ERROR | |
| r-release-macos-arm64 | 1.0.1 | 3.00 | 173.00 | 176.00 | OK | |
| r-release-macos-x86_64 | 1.0.1 | 8.00 | 569.00 | 577.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 13.00 | 453.00 | 466.00 | ERROR | |
| r-oldrel-macos-arm64 | 1.0.1 | 2.00 | 177.00 | 179.00 | OK | |
| r-oldrel-macos-x86_64 | 1.0.1 | 8.00 | 827.00 | 835.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 18.00 | 623.00 | 641.00 | ERROR |
Version: 1.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [399s/450s]
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")
Saving _problems/test-fold_equality-5.R
Saving _problems/test-glm-5.R
Saving _problems/test-glm_predictions-5.R
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
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 = 5.385 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 6.634 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 5.30 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 4.06 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 4.075 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 6.675 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 6.244 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.991 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 4.974 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 4.67 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 4.883 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 5.19 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 6.045 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 5.112 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 5.127 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 5.554 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 4.058 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 4.026 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
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 = 2.243 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 1.292 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.068 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 1.172 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.377 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.237 Round = 6 k = 52.0000 Value = -0.1290153
Best Parameters Found:
Round = 6 k = 52.0000 Value = -0.1290153
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 = 1.484 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.44 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 1.559 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 1.386 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 1.705 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 1.378 Round = 6 k = 51.0000 Value = -0.1243006
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1082897
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 = 1.343 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.632 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 1.692 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 1.185 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.101 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 1.958 Round = 6 k = 50.0000 Value = -0.1341896
Best Parameters Found:
Round = 2 k = 64.0000 Value = -0.1299503
CV fold: Fold1
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
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 progress [==================================>-----------------] 2/3 ( 67%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.248 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.969 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.398 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.048 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.012 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.208 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.399 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.099 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.122 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.038 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.43 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.521 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.413 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.421 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.44 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.338 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.665 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.612 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.352 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.473 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.566 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.77 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.851 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.432 Round = 12 minsplit = 78.0000 cp = 0.02853145 maxdepth = 30.0000 Value = -0.09558117
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.324 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.262 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.311 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.307 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.477 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.274 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.288 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.705 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.525 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.096 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.342 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.489 Round = 12 minsplit = 82.0000 cp = 0.07214655 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.42 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.326 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.318 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.492 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.341 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.67 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.541 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.544 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.382 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.911 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.991 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.444 Round = 12 minsplit = 78.0000 cp = 0.0395761 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.082 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.084 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.085 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.083 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.153 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.158 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.087 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.079 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.096 Round = 12 minsplit = 25.0000 cp = 0.05205146 maxdepth = 7.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.087 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.073 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.109 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.15 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.086 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.146 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.101 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.093 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.144 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 12 minsplit = 83.0000 cp = 0.08284277 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.147 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.081 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.061 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.088 Round = 12 minsplit = 12.0000 cp = 0.05804747 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.121 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.088 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.073 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.108 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.146 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.124 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.063 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.069 Round = 12 minsplit = 72.0000 cp = 0.05062197 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
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
CV progress [==================================>-----------------] 2/3 ( 67%)
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
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.
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.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-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.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-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.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-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.0.1
Check: for new files in some other directories
Result: NOTE
Found the following files/directories:
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‘~/tmp/scratch/Rtmp0XLWTH’ ‘~/tmp/scratch/Rtmp0hIci7’
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‘~/tmp/scratch/Rtmp1tbOAi’ ‘~/tmp/scratch/Rtmp1yc4IZ’
‘~/tmp/scratch/Rtmp2PYtEe’ ‘~/tmp/scratch/Rtmp2uPSom’
‘~/tmp/scratch/Rtmp3oRHIg’ ‘~/tmp/scratch/Rtmp4TVlnV’
‘~/tmp/scratch/Rtmp4y2UE2’ ‘~/tmp/scratch/Rtmp5HMzRO’
‘~/tmp/scratch/Rtmp5RrXgd’ ‘~/tmp/scratch/Rtmp5Sn99r’
‘~/tmp/scratch/Rtmp5gxsIH’ ‘~/tmp/scratch/Rtmp5x29DN’
‘~/tmp/scratch/Rtmp62pOCd’ ‘~/tmp/scratch/Rtmp6SzPSf’
‘~/tmp/scratch/Rtmp6weB3n’ ‘~/tmp/scratch/Rtmp7H9qbw’
‘~/tmp/scratch/Rtmp83wqLb’ ‘~/tmp/scratch/Rtmp86mUNu’
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‘~/tmp/scratch/RtmpA4Hl1T’ ‘~/tmp/scratch/RtmpAMGA7M’
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‘~/tmp/scratch/RtmpBNgkrb’ ‘~/tmp/scratch/RtmpCHwJ4r’
‘~/tmp/scratch/RtmpCLeFFp’ ‘~/tmp/scratch/RtmpCTcpZl’
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‘~/tmp/scratch/RtmpF8X9P4’ ‘~/tmp/scratch/RtmpFVrRqO’
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‘~/tmp/scratch/RtmpISVJEO’ ‘~/tmp/scratch/RtmpIttjnV’
‘~/tmp/scratch/RtmpJAW4BX’ ‘~/tmp/scratch/RtmpJdpPSy’
‘~/tmp/scratch/RtmpJfvlDQ’ ‘~/tmp/scratch/RtmpKioHzZ’
‘~/tmp/scratch/RtmpL1JuJq’ ‘~/tmp/scratch/RtmpL1vbEG’
‘~/tmp/scratch/RtmpMLn40N’ ‘~/tmp/scratch/RtmpMSaTnY’
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‘~/tmp/scratch/RtmpVhBkyo’ ‘~/tmp/scratch/RtmpVu6dJk’
‘~/tmp/scratch/RtmpWD2hzb’ ‘~/tmp/scratch/RtmpWEWjpX’
‘~/tmp/scratch/RtmpWQtEDV’ ‘~/tmp/scratch/RtmpWq9Igv’
‘~/tmp/scratch/RtmpWwC3r6’ ‘~/tmp/scratch/RtmpXbxpIG’
‘~/tmp/scratch/RtmpXdAnog’ ‘~/tmp/scratch/RtmpYaiUk1’
‘~/tmp/scratch/RtmpZ4Uhqk’ ‘~/tmp/scratch/RtmpZ5BUdq’
‘~/tmp/scratch/RtmpZOj8Gc’ ‘~/tmp/scratch/RtmpZZpWrW’
‘~/tmp/scratch/RtmpZtWpcM’ ‘~/tmp/scratch/RtmpaEJETS’
‘~/tmp/scratch/RtmpaNd3IK’ ‘~/tmp/scratch/RtmpagMBBb’
‘~/tmp/scratch/RtmpaiQHLX’ ‘~/tmp/scratch/Rtmpbkzoyp’
‘~/tmp/scratch/RtmpcDqDIm’ ‘~/tmp/scratch/RtmpcLeiuE’
‘~/tmp/scratch/RtmpcmYHa9’ ‘~/tmp/scratch/Rtmpd9pzq9’
‘~/tmp/scratch/RtmpdipUls’ ‘~/tmp/scratch/RtmpdoOdix’
‘~/tmp/scratch/RtmpdxYiBs’ ‘~/tmp/scratch/Rtmpf8cUjn’
‘~/tmp/scratch/RtmpfXqSUx’ ‘~/tmp/scratch/RtmpfgtN9Q’
‘~/tmp/scratch/RtmpgRvb0d’ ‘~/tmp/scratch/RtmpgdFn6P’
‘~/tmp/scratch/RtmpgfX58Z’ ‘~/tmp/scratch/Rtmpi4tL9c’
‘~/tmp/scratch/Rtmpj8HFPK’ ‘~/tmp/scratch/RtmpjOgojd’
‘~/tmp/scratch/RtmpjgFPk1’ ‘~/tmp/scratch/RtmpjyNkye’
‘~/tmp/scratch/RtmpkYC2kT’ ‘~/tmp/scratch/RtmpkYiMQ9’
‘~/tmp/scratch/RtmplQAxJj’ ‘~/tmp/scratch/Rtmpm8FCG4’
‘~/tmp/scratch/Rtmpm9BeTu’ ‘~/tmp/scratch/RtmpnHb57N’
‘~/tmp/scratch/RtmpnZScIJ’ ‘~/tmp/scratch/Rtmpnfsv2w’
‘~/tmp/scratch/Rtmpni5EaS’ ‘~/tmp/scratch/RtmpoGZtMs’
‘~/tmp/scratch/RtmpoKQLbD’ ‘~/tmp/scratch/RtmpoSBlEs’
‘~/tmp/scratch/RtmpoXLQ3e’ ‘~/tmp/scratch/Rtmpod27BE’
‘~/tmp/scratch/Rtmpon4F0U’ ‘~/tmp/scratch/Rtmpov7I5q’
‘~/tmp/scratch/RtmppKph2G’ ‘~/tmp/scratch/Rtmpq0r91I’
‘~/tmp/scratch/RtmpqHuaZJ’ ‘~/tmp/scratch/RtmpqLjVik’
‘~/tmp/scratch/RtmpqUYwqk’ ‘~/tmp/scratch/Rtmpqj7F57’
‘~/tmp/scratch/Rtmpqm6Ugd’ ‘~/tmp/scratch/RtmpqvcLCt’
‘~/tmp/scratch/RtmprEmLid’ ‘~/tmp/scratch/RtmprIhOnY’
‘~/tmp/scratch/RtmprUU5fZ’ ‘~/tmp/scratch/RtmpsOcwgv’
‘~/tmp/scratch/RtmpsaJuVO’ ‘~/tmp/scratch/Rtmpt7sP4P’
‘~/tmp/scratch/RtmptFZcaN’ ‘~/tmp/scratch/RtmpthaTgg’
‘~/tmp/scratch/RtmptnQZ8Z’ ‘~/tmp/scratch/RtmptyclXZ’
‘~/tmp/scratch/RtmpuD9Oep’ ‘~/tmp/scratch/RtmpuFZios’
‘~/tmp/scratch/RtmpvSrN5i’ ‘~/tmp/scratch/RtmpvTzttu’
‘~/tmp/scratch/RtmpvfmHr4’ ‘~/tmp/scratch/RtmpwHAy0j’
‘~/tmp/scratch/RtmpwKoF17’ ‘~/tmp/scratch/Rtmpwnu6lY’
‘~/tmp/scratch/RtmpxJWl9C’ ‘~/tmp/scratch/RtmpxNZP88’
‘~/tmp/scratch/RtmpxVGIGo’ ‘~/tmp/scratch/RtmpxWbw82’
‘~/tmp/scratch/RtmpxXYoF1’ ‘~/tmp/scratch/Rtmpy7sA3q’
‘~/tmp/scratch/RtmpyP4UBC’ ‘~/tmp/scratch/RtmpyyYxMQ’
‘~/tmp/scratch/RtmpzVZVfY’ ‘~/tmp/scratch/RtmpzY4BTV’
‘~/tmp/scratch/Rtmpzv7qz1’ ‘~/tmp/scratch/RtmpzzVGdn’
‘~/tmp/scratch/xvfb-run.0MQ84t’ ‘~/tmp/scratch/xvfb-run.0SxQs9’
‘~/tmp/scratch/xvfb-run.1rCwKX’ ‘~/tmp/scratch/xvfb-run.1ySdwB’
‘~/tmp/scratch/xvfb-run.2FhbbG’ ‘~/tmp/scratch/xvfb-run.3BMJTC’
‘~/tmp/scratch/xvfb-run.3drPv8’ ‘~/tmp/scratch/xvfb-run.4HZYjz’
‘~/tmp/scratch/xvfb-run.4i1rJi’ ‘~/tmp/scratch/xvfb-run.5JDntH’
‘~/tmp/scratch/xvfb-run.6Rc7xQ’ ‘~/tmp/scratch/xvfb-run.8B2fRv’
‘~/tmp/scratch/xvfb-run.97kDg2’ ‘~/tmp/scratch/xvfb-run.9R67eD’
‘~/tmp/scratch/xvfb-run.9tZQlY’ ‘~/tmp/scratch/xvfb-run.AYnxDO’
‘~/tmp/scratch/xvfb-run.BgSUe2’ ‘~/tmp/scratch/xvfb-run.C88ijI’
‘~/tmp/scratch/xvfb-run.EDAalK’ ‘~/tmp/scratch/xvfb-run.EJWSm0’
‘~/tmp/scratch/xvfb-run.KwqhT2’ ‘~/tmp/scratch/xvfb-run.MCZSGu’
‘~/tmp/scratch/xvfb-run.MgopEr’ ‘~/tmp/scratch/xvfb-run.Mk7aUF’
‘~/tmp/scratch/xvfb-run.NGHuaS’ ‘~/tmp/scratch/xvfb-run.NHKdc8’
‘~/tmp/scratch/xvfb-run.OHF3z8’ ‘~/tmp/scratch/xvfb-run.OJxret’
‘~/tmp/scratch/xvfb-run.PrMMcI’ ‘~/tmp/scratch/xvfb-run.QIfyfM’
‘~/tmp/scratch/xvfb-run.R0vYng’ ‘~/tmp/scratch/xvfb-run.R1QOSM’
‘~/tmp/scratch/xvfb-run.RllwRL’ ‘~/tmp/scratch/xvfb-run.SIgTux’
‘~/tmp/scratch/xvfb-run.TD9hCz’ ‘~/tmp/scratch/xvfb-run.TFRQJg’
‘~/tmp/scratch/xvfb-run.TGzffK’ ‘~/tmp/scratch/xvfb-run.VJtWHz’
‘~/tmp/scratch/xvfb-run.VyjrNA’ ‘~/tmp/scratch/xvfb-run.WTV75T’
‘~/tmp/scratch/xvfb-run.Ww4u6f’ ‘~/tmp/scratch/xvfb-run.YEGC3H’
‘~/tmp/scratch/xvfb-run.apjs1L’ ‘~/tmp/scratch/xvfb-run.blkdvK’
‘~/tmp/scratch/xvfb-run.cYipkM’ ‘~/tmp/scratch/xvfb-run.dJXvVi’
‘~/tmp/scratch/xvfb-run.eVS6v6’ ‘~/tmp/scratch/xvfb-run.eXzpAl’
‘~/tmp/scratch/xvfb-run.ed6gfN’ ‘~/tmp/scratch/xvfb-run.fjWkcx’
‘~/tmp/scratch/xvfb-run.h4J8MI’ ‘~/tmp/scratch/xvfb-run.i4Mjn6’
‘~/tmp/scratch/xvfb-run.istDsg’ ‘~/tmp/scratch/xvfb-run.jJ0Ike’
‘~/tmp/scratch/xvfb-run.jf3ocp’ ‘~/tmp/scratch/xvfb-run.jrXRK4’
‘~/tmp/scratch/xvfb-run.kBOj2e’ ‘~/tmp/scratch/xvfb-run.kqPaIQ’
‘~/tmp/scratch/xvfb-run.lWcQQ0’ ‘~/tmp/scratch/xvfb-run.m6MlZ9’
‘~/tmp/scratch/xvfb-run.mxVcal’ ‘~/tmp/scratch/xvfb-run.n7pCcf’
‘~/tmp/scratch/xvfb-run.o3l2KY’ ‘~/tmp/scratch/xvfb-run.oEPhxA’
‘~/tmp/scratch/xvfb-run.ox4fbG’ ‘~/tmp/scratch/xvfb-run.psfAIj’
‘~/tmp/scratch/xvfb-run.qeKhwN’ ‘~/tmp/scratch/xvfb-run.qnUOkw’
‘~/tmp/scratch/xvfb-run.qoY7R4’ ‘~/tmp/scratch/xvfb-run.r1J0qr’
‘~/tmp/scratch/xvfb-run.rh6QI0’ ‘~/tmp/scratch/xvfb-run.sNjQ6c’
‘~/tmp/scratch/xvfb-run.vMMYBR’ ‘~/tmp/scratch/xvfb-run.vQlLap’
‘~/tmp/scratch/xvfb-run.vWWm5L’ ‘~/tmp/scratch/xvfb-run.vuYOmw’
‘~/tmp/scratch/xvfb-run.wltCZ6’ ‘~/tmp/scratch/xvfb-run.wzFKbf’
‘~/tmp/scratch/xvfb-run.xE6fRf’ ‘~/tmp/scratch/xvfb-run.xrPZvW’
‘/dev/shm/sm_segment.gimli1.1001.45b50000.0’
‘~/.cache/pocl/uncached/tempfile_0zQPeX’
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0.0
Check: tests
Result: ERROR
Running 'testthat.R' [341s]
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")
Saving _problems/test-fold_equality-5.R
Saving _problems/test-glm-5.R
Saving _problems/test-glm_predictions-5.R
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
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 = 3.25 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.42 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.09 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.31 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.67 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.39 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 3.17 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.23 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.11 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.39 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.60 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.40 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.32 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 3.03 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 3.26 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 3.32 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 3.09 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 3.48 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
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 = 0.92 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 1.05 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 0.94 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 0.96 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.08 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.06 Round = 6 k = 52.0000 Value = -0.1290153
Best Parameters Found:
Round = 6 k = 52.0000 Value = -0.1290153
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 = 1.00 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.08 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 1.06 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 1.03 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 0.95 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 0.94 Round = 6 k = 51.0000 Value = -0.1243006
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1082897
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 = 0.98 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.07 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 0.83 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 0.88 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.00 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 1.03 Round = 6 k = 50.0000 Value = -0.1341896
Best Parameters Found:
Round = 2 k = 64.0000 Value = -0.1299503
CV fold: Fold1
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
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 progress [==================================>-----------------] 2/3 ( 67%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.25 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.36 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.07 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.88 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.23 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.39 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.29 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.96 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.12 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.27 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.17 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.08 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.27 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.31 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.16 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.26 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.24 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.17 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.31 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.34 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.29 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.75 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.19 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.47 Round = 12 minsplit = 57.0000 cp = 0.06699886 maxdepth = 30.0000 Value = -0.09558117
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.31 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.25 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.27 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.27 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.22 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.35 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.23 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.46 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.29 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.81 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.20 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.33 Round = 12 minsplit = 88.0000 cp = 0.06845091 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.36 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.39 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.14 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.20 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.34 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.21 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.29 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.35 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.45 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.69 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.28 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.23 Round = 12 minsplit = 88.0000 cp = 0.06845084 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 12 minsplit = 50.0000 cp = 0.01435421 maxdepth = 15.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.04 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 12 minsplit = 21.0000 cp = 0.01106419 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 12 minsplit = 46.0000 cp = 0.02893404 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 12 minsplit = 29.0000 cp = 0.09896765 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
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
CV progress [==================================>-----------------] 2/3 ( 67%)
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
CV progress [====================================================] 3/3 (100%)
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 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.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-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.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-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.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-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-windows-x86_64
Version: 1.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [398s/464s]
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")
Saving _problems/test-fold_equality-5.R
Saving _problems/test-glm-5.R
Saving _problems/test-glm_predictions-5.R
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
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 = 4.623 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 6.098 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 5.024 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 4.516 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 5.374 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 4.119 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 3.936 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.128 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.233 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.517 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.789 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.706 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.89 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 3.81 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 4.583 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 5.111 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 3.787 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 4.508 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
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 = 1.713 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 1.402 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.74 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 1.522 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.404 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.727 Round = 6 k = 52.0000 Value = -0.1290153
Best Parameters Found:
Round = 6 k = 52.0000 Value = -0.1290153
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 = 1.71 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.695 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 1.455 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 3.639 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 1.731 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 1.331 Round = 6 k = 51.0000 Value = -0.1243006
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1082897
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 = 1.506 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.501 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 1.451 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 1.622 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.465 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 1.403 Round = 6 k = 50.0000 Value = -0.1341896
Best Parameters Found:
Round = 2 k = 64.0000 Value = -0.1299503
CV fold: Fold1
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
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 progress [==================================>-----------------] 2/3 ( 67%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.291 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.466 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.808 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.32 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.264 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.52 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.379 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.031 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.304 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.725 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.532 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.283 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.465 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.599 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.471 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.535 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.444 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.447 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.81 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.586 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.569 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.339 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.273 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.82 Round = 12 minsplit = 78.0000 cp = 0.02853145 maxdepth = 30.0000 Value = -0.09558117
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.344 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.803 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.541 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.561 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.678 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.452 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.41 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.897 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.044 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.123 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.452 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.025 Round = 12 minsplit = 82.0000 cp = 0.07214655 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.399 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.923 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.581 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.683 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.564 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.779 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.552 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.822 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.577 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.947 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.829 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.354 Round = 12 minsplit = 78.0000 cp = 0.0395761 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.149 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.073 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.063 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.099 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 12 minsplit = 25.0000 cp = 0.05205146 maxdepth = 7.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.128 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.107 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.146 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.079 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 12 minsplit = 83.0000 cp = 0.08284277 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.139 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.084 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.069 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.199 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.098 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.086 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.111 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.268 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.096 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.145 Round = 12 minsplit = 12.0000 cp = 0.05804747 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.144 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.205 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.183 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.124 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.118 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.144 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.099 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.078 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.149 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.148 Round = 12 minsplit = 72.0000 cp = 0.05062197 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
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.
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.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.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-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.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-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.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-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-patched-linux-x86_64
Version: 1.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [406s/471s]
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")
Saving _problems/test-fold_equality-5.R
Saving _problems/test-glm-5.R
Saving _problems/test-glm_predictions-5.R
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
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 = 5.272 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.866 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 4.62 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 7.056 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 4.866 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 4.686 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 4.365 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.147 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 4.369 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 4.216 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 4.356 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 4.513 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 4.212 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 4.632 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 3.511 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 4.442 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 3.668 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 4.199 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
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 = 1.736 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 1.581 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.387 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 1.648 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.49 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.486 Round = 6 k = 52.0000 Value = -0.1290153
Best Parameters Found:
Round = 6 k = 52.0000 Value = -0.1290153
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 = 1.746 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.953 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 3.908 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 2.377 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 1.57 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 1.613 Round = 6 k = 51.0000 Value = -0.1243006
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1082897
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 = 1.421 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.507 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 1.406 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 1.477 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.584 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 1.583 Round = 6 k = 50.0000 Value = -0.1341896
Best Parameters Found:
Round = 2 k = 64.0000 Value = -0.1299503
CV fold: Fold1
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
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 progress [==================================>-----------------] 2/3 ( 67%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.466 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.054 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.762 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.284 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.758 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.609 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.289 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.617 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.379 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.222 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.79 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.956 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.636 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.564 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.703 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.604 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.552 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.769 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.688 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.792 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.505 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.978 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.493 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.353 Round = 12 minsplit = 78.0000 cp = 0.02853145 maxdepth = 30.0000 Value = -0.09558117
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.532 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.186 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.62 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.439 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.118 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.795 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.446 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.731 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.317 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.258 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.035 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.355 Round = 12 minsplit = 82.0000 cp = 0.07214655 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.95 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.966 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.862 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.464 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.615 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.371 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.766 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.495 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.552 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.043 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.557 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.94 Round = 12 minsplit = 78.0000 cp = 0.0395761 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.135 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.13 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.081 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.078 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.079 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.079 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.162 Round = 12 minsplit = 25.0000 cp = 0.05205146 maxdepth = 7.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.125 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.211 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.103 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.148 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.081 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 12 minsplit = 83.0000 cp = 0.08284277 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.094 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.065 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.123 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.088 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.097 Round = 12 minsplit = 12.0000 cp = 0.05804747 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.088 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.143 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.101 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.069 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 12 minsplit = 72.0000 cp = 0.05062197 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
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
CV progress [==================================>-----------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
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.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.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-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.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-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.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-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-linux-x86_64
Version: 1.0.0
Check: tests
Result: ERROR
Running 'testthat.R' [339s]
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")
Saving _problems/test-fold_equality-5.R
Saving _problems/test-glm-5.R
Saving _problems/test-glm_predictions-5.R
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
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 = 3.15 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.17 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.28 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.53 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.28 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.26 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 3.17 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.11 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.12 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.36 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.52 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.36 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.28 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 3.27 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 3.25 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 3.38 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 3.42 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 3.52 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
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 = 1.00 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 0.99 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.00 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 1.00 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.00 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.00 Round = 6 k = 52.0000 Value = -0.1290153
Best Parameters Found:
Round = 6 k = 52.0000 Value = -0.1290153
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 = 0.91 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.05 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 1.02 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 1.06 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 1.06 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 1.03 Round = 6 k = 51.0000 Value = -0.1243006
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1082897
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 = 0.97 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 0.85 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 0.86 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 0.94 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.06 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 0.93 Round = 6 k = 50.0000 Value = -0.1341896
Best Parameters Found:
Round = 2 k = 64.0000 Value = -0.1299503
CV fold: Fold1
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
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 progress [==================================>-----------------] 2/3 ( 67%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.12 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.33 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.10 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.16 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.22 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.14 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.02 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.95 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.39 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.29 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.89 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.81 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.33 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.39 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.38 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.34 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.31 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.31 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.33 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.25 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.33 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.72 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.15 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.32 Round = 12 minsplit = 57.0000 cp = 0.06699886 maxdepth = 30.0000 Value = -0.09558117
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.17 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.28 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.31 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.28 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.26 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.32 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.19 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.28 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.24 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.70 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.30 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.38 Round = 12 minsplit = 88.0000 cp = 0.06845091 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.36 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.41 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.22 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.36 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.14 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.33 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.20 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.31 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.35 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.81 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.26 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.39 Round = 12 minsplit = 88.0000 cp = 0.06845084 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 12 minsplit = 50.0000 cp = 0.01435421 maxdepth = 15.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 12 minsplit = 21.0000 cp = 0.01106419 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 12 minsplit = 46.0000 cp = 0.02893404 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 12 minsplit = 29.0000 cp = 0.09896765 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
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
CV progress [==================================>-----------------] 2/3 ( 67%)
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
CV progress [====================================================] 3/3 (100%)
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 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.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-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.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-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.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-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-windows-x86_64
Version: 1.0.0
Check: tests
Result: ERROR
Running 'testthat.R' [486s]
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")
Saving _problems/test-fold_equality-5.R
Saving _problems/test-glm-5.R
Saving _problems/test-glm_predictions-5.R
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
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 = 5.05 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.77 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 5.53 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 5.86 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 5.25 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 5.22 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 5.52 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.97 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 4.50 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 5.41 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 4.94 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 6.33 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 5.94 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 6.13 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 5.98 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 5.30 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 5.55 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 5.51 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
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 = 1.31 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 1.70 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.53 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 1.76 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.81 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.44 Round = 6 k = 52.0000 Value = -0.1290153
Best Parameters Found:
Round = 6 k = 52.0000 Value = -0.1290153
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 = 1.73 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.69 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 1.77 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 1.96 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 1.75 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 1.53 Round = 6 k = 51.0000 Value = -0.1243006
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1082897
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 = 1.42 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.58 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 1.35 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 1.49 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.66 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 1.63 Round = 6 k = 50.0000 Value = -0.1341896
Best Parameters Found:
Round = 2 k = 64.0000 Value = -0.1299503
CV fold: Fold1
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
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 progress [==================================>-----------------] 2/3 ( 67%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.26 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.10 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.07 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.04 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.00 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.25 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.29 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.83 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.98 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.14 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.26 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.89 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.70 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.48 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.48 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.59 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.62 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.64 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.62 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.64 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.47 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.99 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.44 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.50 Round = 12 minsplit = 12.0000 cp = 0.09946662 maxdepth = 30.0000 Value = -0.09558117
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.55 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.55 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.54 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.55 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.47 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.57 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.81 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.72 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.69 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.98 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.49 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.47 Round = 12 minsplit = 42.0000 cp = 0.07902683 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
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.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.50 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.56 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.56 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.66 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.63 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.54 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.62 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.64 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.58 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.94 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.73 Round = 11 minsplit = 28.0000 cp = 0.04674927 maxdepth = 14.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.69 Round = 12 minsplit = 57.0000 cp = 0.06694248 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.11 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.11 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.11 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.11 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 12 minsplit = 25.0000 cp = 0.05205146 maxdepth = 7.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 12 minsplit = 74.0000 cp = 0.07391919 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.11 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 12 minsplit = 19.0000 cp = 0.0304321 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
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.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 12 minsplit = 4.0000 cp = 0.07846899 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
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.
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.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.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-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.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-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.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-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-windows-x86_64