Last updated on 2026-08-01 22:49:22 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 | 8.10 | 352.77 | 360.87 | 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 | 9.91 | 531.62 | 541.53 | 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/Rtmp0fjpGF’ ‘~/tmp/scratch/Rtmp1B4nP8’
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‘~/tmp/scratch/Rtmp2syMEv’ ‘~/tmp/scratch/Rtmp30rk0g’
‘~/tmp/scratch/Rtmp3B7h0a’ ‘~/tmp/scratch/Rtmp3G61bf’
‘~/tmp/scratch/Rtmp3U35q3’ ‘~/tmp/scratch/Rtmp4PqS2f’
‘~/tmp/scratch/Rtmp4cERuK’ ‘~/tmp/scratch/Rtmp4rsMwU’
‘~/tmp/scratch/Rtmp5FxrOY’ ‘~/tmp/scratch/Rtmp5Sd4E5’
‘~/tmp/scratch/Rtmp5WYstu’ ‘~/tmp/scratch/Rtmp6tAhsq’
‘~/tmp/scratch/Rtmp83swW9’ ‘~/tmp/scratch/Rtmp8F5SwC’
‘~/tmp/scratch/Rtmp9yUNlX’ ‘~/tmp/scratch/RtmpAK6cSb’
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‘~/tmp/scratch/RtmpApgHW9’ ‘~/tmp/scratch/RtmpAzUq32’
‘~/tmp/scratch/RtmpBj2G2t’ ‘~/tmp/scratch/RtmpCOYoqE’
‘~/tmp/scratch/RtmpCQCduA’ ‘~/tmp/scratch/RtmpCXytnS’
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‘~/tmp/scratch/RtmpE3K5WY’ ‘~/tmp/scratch/RtmpEAcO2D’
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‘~/tmp/scratch/RtmpI24Gmm’ ‘~/tmp/scratch/RtmpIHSYoh’
‘~/tmp/scratch/RtmpIgXrCb’ ‘~/tmp/scratch/RtmpImHV8w’
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‘~/tmp/scratch/RtmpKDBwA2’ ‘~/tmp/scratch/RtmpKRJoaj’
‘~/tmp/scratch/RtmpKd1NFu’ ‘~/tmp/scratch/RtmpL0jrzU’
‘~/tmp/scratch/RtmpLIt7dE’ ‘~/tmp/scratch/RtmpLMMH1h’
‘~/tmp/scratch/RtmpLbHZ3U’ ‘~/tmp/scratch/RtmpLwVuJ2’
‘~/tmp/scratch/RtmpMYZvfq’ ‘~/tmp/scratch/RtmpMzrnoW’
‘~/tmp/scratch/RtmpNE9aMk’ ‘~/tmp/scratch/RtmpNOFNws’
‘~/tmp/scratch/RtmpOybicu’ ‘~/tmp/scratch/RtmpPK8Hk0’
‘~/tmp/scratch/RtmpSBZseg’ ‘~/tmp/scratch/RtmpTEb9pu’
‘~/tmp/scratch/RtmpTHxcXz’ ‘~/tmp/scratch/RtmpU8VndA’
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‘~/tmp/scratch/RtmpWcKGfY’ ‘~/tmp/scratch/RtmpWezNEI’
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‘~/tmp/scratch/RtmpYeSoyC’ ‘~/tmp/scratch/RtmpYpee4n’
‘~/tmp/scratch/RtmpZ4y8QG’ ‘~/tmp/scratch/RtmpZCKeF4’
‘~/tmp/scratch/RtmpZE8nm6’ ‘~/tmp/scratch/RtmpZK2nZW’
‘~/tmp/scratch/RtmpZhyFAF’ ‘~/tmp/scratch/Rtmpa3EHiH’
‘~/tmp/scratch/RtmpaAuW0M’ ‘~/tmp/scratch/RtmpbTZGcw’
‘~/tmp/scratch/RtmpcTsXc9’ ‘~/tmp/scratch/RtmpcUuUx1’
‘~/tmp/scratch/RtmpcXhW97’ ‘~/tmp/scratch/RtmpcaUNUh’
‘~/tmp/scratch/RtmpccXPNq’ ‘~/tmp/scratch/RtmpcmVlQB’
‘~/tmp/scratch/RtmpdPvX7z’ ‘~/tmp/scratch/RtmpdUygtW’
‘~/tmp/scratch/RtmpdihyQY’ ‘~/tmp/scratch/RtmpdnEDso’
‘~/tmp/scratch/Rtmpe5vpBR’ ‘~/tmp/scratch/RtmpeK1bgx’
‘~/tmp/scratch/RtmpePrPXX’ ‘~/tmp/scratch/RtmpeXQw8M’
‘~/tmp/scratch/Rtmpf0txA1’ ‘~/tmp/scratch/Rtmpf2NqNG’
‘~/tmp/scratch/Rtmpf2c8IP’ ‘~/tmp/scratch/RtmpfP3AST’
‘~/tmp/scratch/RtmpfSYG3a’ ‘~/tmp/scratch/RtmpgGxHT7’
‘~/tmp/scratch/Rtmpgynz1P’ ‘~/tmp/scratch/RtmphGSGxV’
‘~/tmp/scratch/RtmphVYNR6’ ‘~/tmp/scratch/Rtmphu0Vcn’
‘~/tmp/scratch/Rtmphzzd2y’ ‘~/tmp/scratch/Rtmpi8ty0d’
‘~/tmp/scratch/RtmpiHZT02’ ‘~/tmp/scratch/RtmpiID6DN’
‘~/tmp/scratch/RtmpiU0YnU’ ‘~/tmp/scratch/Rtmpj2EBTj’
‘~/tmp/scratch/RtmpjqvOJg’ ‘~/tmp/scratch/RtmpkO8qU9’
‘~/tmp/scratch/Rtmpkh9wrd’ ‘~/tmp/scratch/Rtmpkr9Ewr’
‘~/tmp/scratch/Rtmpkzs8Xl’ ‘~/tmp/scratch/RtmplGmtWQ’
‘~/tmp/scratch/RtmplNLOxX’ ‘~/tmp/scratch/RtmplOX0kg’
‘~/tmp/scratch/RtmplVoVpY’ ‘~/tmp/scratch/RtmpmCA0M8’
‘~/tmp/scratch/RtmpmDdrho’ ‘~/tmp/scratch/RtmpmMbqUS’
‘~/tmp/scratch/RtmpmZYemV’ ‘~/tmp/scratch/RtmpmdoGMS’
‘~/tmp/scratch/RtmpmzxMJQ’ ‘~/tmp/scratch/Rtmpn12Q7E’
‘~/tmp/scratch/Rtmpn2BKml’ ‘~/tmp/scratch/RtmpngxMoe’
‘~/tmp/scratch/RtmpnkgL4p’ ‘~/tmp/scratch/RtmpoXXh99’
‘~/tmp/scratch/RtmpoY3dn1’ ‘~/tmp/scratch/RtmpojTwH9’
‘~/tmp/scratch/RtmpokZriE’ ‘~/tmp/scratch/Rtmponbotf’
‘~/tmp/scratch/RtmppFYGrC’ ‘~/tmp/scratch/Rtmpq6Uz55’
‘~/tmp/scratch/RtmpqCPD6G’ ‘~/tmp/scratch/Rtmpqe39JX’
‘~/tmp/scratch/RtmpqeNa33’ ‘~/tmp/scratch/RtmpqsEhnD’
‘~/tmp/scratch/RtmprARkyt’ ‘~/tmp/scratch/RtmprGqTgi’
‘~/tmp/scratch/Rtmprag8Rh’ ‘~/tmp/scratch/RtmprnLE4s’
‘~/tmp/scratch/RtmprxczRt’ ‘~/tmp/scratch/RtmprzDnd1’
‘~/tmp/scratch/RtmpsPcP2W’ ‘~/tmp/scratch/RtmpshSZ5D’
‘~/tmp/scratch/Rtmpsn05bz’ ‘~/tmp/scratch/RtmpsoOGxl’
‘~/tmp/scratch/RtmptAhOBF’ ‘~/tmp/scratch/RtmptShXDL’
‘~/tmp/scratch/RtmpuBgrCo’ ‘~/tmp/scratch/RtmpuN5Vpr’
‘~/tmp/scratch/RtmpuqSeT3’ ‘~/tmp/scratch/RtmputeEqF’
‘~/tmp/scratch/Rtmpvb1p0u’ ‘~/tmp/scratch/Rtmpw7KpSr’
‘~/tmp/scratch/RtmpwZGhfo’ ‘~/tmp/scratch/RtmpwkUVFK’
‘~/tmp/scratch/RtmpwqXC1N’ ‘~/tmp/scratch/RtmpxDVAJr’
‘~/tmp/scratch/RtmpxL1eXy’ ‘~/tmp/scratch/Rtmpxqpi6W’
‘~/tmp/scratch/RtmpyZMPq4’ ‘~/tmp/scratch/RtmpyxLYqR’
‘~/tmp/scratch/Rtmpz0IUqz’ ‘~/tmp/scratch/Rtmpz4q5Xe’
‘~/tmp/scratch/Rtmpztv6Vv’ ‘~/tmp/scratch/xvfb-run.2IEy9y’
‘~/tmp/scratch/xvfb-run.2oUGAW’ ‘~/tmp/scratch/xvfb-run.3Owvzu’
‘~/tmp/scratch/xvfb-run.3sYdRp’ ‘~/tmp/scratch/xvfb-run.40pg11’
‘~/tmp/scratch/xvfb-run.44lXUC’ ‘~/tmp/scratch/xvfb-run.5BCFc0’
‘~/tmp/scratch/xvfb-run.76gx99’ ‘~/tmp/scratch/xvfb-run.78JZcO’
‘~/tmp/scratch/xvfb-run.8jjckd’ ‘~/tmp/scratch/xvfb-run.9q10dH’
‘~/tmp/scratch/xvfb-run.BAgO5i’ ‘~/tmp/scratch/xvfb-run.BouMAu’
‘~/tmp/scratch/xvfb-run.C4qB4m’ ‘~/tmp/scratch/xvfb-run.CJLvAy’
‘~/tmp/scratch/xvfb-run.D9AnX5’ ‘~/tmp/scratch/xvfb-run.EA40yI’
‘~/tmp/scratch/xvfb-run.EEMz2y’ ‘~/tmp/scratch/xvfb-run.FCuXRF’
‘~/tmp/scratch/xvfb-run.FHsjYJ’ ‘~/tmp/scratch/xvfb-run.Fti9ml’
‘~/tmp/scratch/xvfb-run.J21vd6’ ‘~/tmp/scratch/xvfb-run.JIBMmP’
‘~/tmp/scratch/xvfb-run.KQPSTX’ ‘~/tmp/scratch/xvfb-run.KUQxil’
‘~/tmp/scratch/xvfb-run.LD0pea’ ‘~/tmp/scratch/xvfb-run.Llwov4’
‘~/tmp/scratch/xvfb-run.NM1y57’ ‘~/tmp/scratch/xvfb-run.OSiiOJ’
‘~/tmp/scratch/xvfb-run.Onwy5n’ ‘~/tmp/scratch/xvfb-run.Q7WOYC’
‘~/tmp/scratch/xvfb-run.Qa0qXU’ ‘~/tmp/scratch/xvfb-run.Qi31zd’
‘~/tmp/scratch/xvfb-run.RGPTYq’ ‘~/tmp/scratch/xvfb-run.RVIFNC’
‘~/tmp/scratch/xvfb-run.Rpiba7’ ‘~/tmp/scratch/xvfb-run.StAdmf’
‘~/tmp/scratch/xvfb-run.StxN4m’ ‘~/tmp/scratch/xvfb-run.UZZkxG’
‘~/tmp/scratch/xvfb-run.Ucldu2’ ‘~/tmp/scratch/xvfb-run.VrtlgJ’
‘~/tmp/scratch/xvfb-run.ZAmI85’ ‘~/tmp/scratch/xvfb-run.ZGN2qU’
‘~/tmp/scratch/xvfb-run.ZJgngl’ ‘~/tmp/scratch/xvfb-run.ZlELjC’
‘~/tmp/scratch/xvfb-run.Znprmy’ ‘~/tmp/scratch/xvfb-run.bmE6CI’
‘~/tmp/scratch/xvfb-run.c7cMTl’ ‘~/tmp/scratch/xvfb-run.d4xvgM’
‘~/tmp/scratch/xvfb-run.eTpko6’ ‘~/tmp/scratch/xvfb-run.fGHlNe’
‘~/tmp/scratch/xvfb-run.gl1auE’ ‘~/tmp/scratch/xvfb-run.itzHZ8’
‘~/tmp/scratch/xvfb-run.k0pNQ6’ ‘~/tmp/scratch/xvfb-run.kVELAe’
‘~/tmp/scratch/xvfb-run.kdJibc’ ‘~/tmp/scratch/xvfb-run.l9gu8i’
‘~/tmp/scratch/xvfb-run.lYcL2l’ ‘~/tmp/scratch/xvfb-run.niwWyL’
‘~/tmp/scratch/xvfb-run.nr9ZHs’ ‘~/tmp/scratch/xvfb-run.oCjWVd’
‘~/tmp/scratch/xvfb-run.oEYwvC’ ‘~/tmp/scratch/xvfb-run.pco9pK’
‘~/tmp/scratch/xvfb-run.pigJGi’ ‘~/tmp/scratch/xvfb-run.qbt9JJ’
‘~/tmp/scratch/xvfb-run.rRQmvI’ ‘~/tmp/scratch/xvfb-run.rzoMMv’
‘~/tmp/scratch/xvfb-run.s8lcSm’ ‘~/tmp/scratch/xvfb-run.tkDUz1’
‘~/tmp/scratch/xvfb-run.uDpnCT’ ‘~/tmp/scratch/xvfb-run.uRXG03’
‘~/tmp/scratch/xvfb-run.wErvEU’ ‘~/tmp/scratch/xvfb-run.wr3BKC’
‘~/tmp/scratch/xvfb-run.wwSxDT’ ‘~/tmp/scratch/xvfb-run.x9gUlX’
‘~/tmp/scratch/xvfb-run.xRu3gb’ ‘~/tmp/scratch/xvfb-run.yiAYoB’
‘~/tmp/scratch/xvfb-run.ywz0aT’ ‘~/tmp/scratch/xvfb-run.zBxVkC’
‘/dev/shm/sm_segment.gimli1.1001.90840000.0’
‘~/.cache/pocl/uncached/tempfile_Jzwif0’
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’ [7m/11m]
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 = 5.793 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 6.292 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 5.931 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 5.683 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 5.323 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 5.266 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 = 5.421 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 5.742 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 5.592 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.705 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.868 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 5.346 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 5.893 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 5.501 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 4.741 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 5.295 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 4.852 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 5.574 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 = 3.205 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 2.827 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 3.969 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 2.867 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 2.54 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 3.138 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 = 2.843 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 2.127 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 3.708 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 3.071 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 2.938 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 2.115 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 = 1.838 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 2.045 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 2.407 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 2.192 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 3.083 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 4.625 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 = 3.036 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.208 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.405 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.141 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.363 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.743 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 5.793 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 6.255 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 6.038 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.139 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.235 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.807 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 = 4.026 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.269 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.374 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.827 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.765 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.636 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.403 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.082 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.704 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.655 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.041 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.138 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 = 3.04 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.939 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.40 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.217 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.363 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.224 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.004 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.498 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.538 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.121 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.885 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.762 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 = 3.007 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.532 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.14 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.932 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.729 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.091 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.237 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.762 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.618 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.861 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.197 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.212 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 = 0.156 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.134 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.147 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.134 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.123 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.16 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.141 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.098 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.153 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.145 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.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.129 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.143 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 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 = 0.157 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.095 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.088 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.15 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.086 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 = 0.091 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 = 0.068 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.066 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.065 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.062 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.105 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.093 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 = 0.12 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.14 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.144 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.131 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 = 0.073 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.094 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.066 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 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.
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-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