Last updated on 2026-07-26 18:49:03 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 11.34 | 481.55 | 492.89 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 8.18 | 351.98 | 360.16 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 20.00 | 749.75 | 769.75 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 353.99 | ERROR | |||
| r-devel-windows-x86_64 | 1.0.0 | 12.00 | 444.00 | 456.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 12.61 | 533.65 | 546.26 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 9.91 | 531.62 | 541.53 | ERROR | |
| r-release-macos-arm64 | 1.0.0 | 2.00 | 182.00 | 184.00 | OK | |
| r-release-macos-x86_64 | 1.0.0 | 8.00 | 1379.00 | 1387.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 12.00 | 444.00 | 456.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 2.00 | 166.00 | 168.00 | OK | |
| r-oldrel-macos-x86_64 | 1.0.0 | 8.00 | 444.00 | 452.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.0 | 17.00 | 606.00 | 623.00 | OK |
Version: 1.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [287s/362s]
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 = 3.06 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.126 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 2.675 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 2.746 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 2.714 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 2.582 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 = 2.708 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.84 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.45 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.402 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.006 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 2.401 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 2.317 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 3.988 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 3.706 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 3.392 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 3.429 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 4.232 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 = 0.816 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 1.097 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.568 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 0.882 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 0.811 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 0.81 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 = 1.241 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 0.877 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 0.802 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 0.794 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 0.794 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 0.80 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 = 0.941 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 0.94 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 1.352 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 0.926 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 0.897 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 0.816 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 = 2.597 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.599 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.542 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.763 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.497 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.629 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.876 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.584 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.637 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.067 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.311 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.265 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 = 1.617 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.353 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.115 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.779 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.385 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.16 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.684 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.743 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.376 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.263 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.648 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.599 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 = 3.262 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.498 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.626 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.206 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.20 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.504 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.449 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.315 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.652 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.854 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.557 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.146 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 = 1.601 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.153 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.43 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.246 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.616 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.392 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.238 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.27 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.43 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.091 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.577 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.479 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 = 0.064 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.053 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.059 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.054 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.056 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.062 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.091 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.114 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.096 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 = 0.102 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.063 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 = 0.124 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.051 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.099 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.055 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.046 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.102 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.058 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.089 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.041 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.055 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.122 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 = 0.059 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 = 0.048 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.063 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.053 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 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 = 0.091 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.054 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 = 0.055 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 = 0.084 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 = 0.053 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.061 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.051 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.055 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.088 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.048 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.109 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.054 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()`) ─────
<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-gcc
Version: 1.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [11m/12m]
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 = 9.045 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 10.549 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 11.709 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 11.029 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 11.259 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 13.19 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 = 15.316 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 9.759 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 9.454 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 9.461 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 10.663 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 12.087 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 10.82 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 10.825 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 12.399 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 14.242 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 8.734 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 9.662 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 = 2.978 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 3.369 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 2.94 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 3.156 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 5.401 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 3.907 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 = 3.384 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 3.229 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 2.422 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 2.745 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 4.508 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 6.493 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 = 5.608 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 3.445 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 2.885 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 3.087 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 2.919 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 3.66 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 = 5.044 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 5.149 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.844 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 5.036 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.74 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.637 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 5.226 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.81 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.715 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.045 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.012 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.07 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 = 2.158 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.065 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.009 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.028 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.063 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.963 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.06 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.098 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.071 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.13 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.906 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.907 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 = 1.972 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.982 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.549 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.875 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.836 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.587 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.954 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.545 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.716 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.809 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.886 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.568 Round = 12 minsplit = 100.0000 cp = 0.04982227 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 = 1.666 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.467 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.016 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.389 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.169 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.956 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.946 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.635 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.529 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.798 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.988 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.414 Round = 12 minsplit = 82.0000 cp = 0.07214655 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 = 0.076 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 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 = 0.076 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.094 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 = 0.071 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 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 = 0.066 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.062 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.066 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.063 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.061 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.061 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 = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.065 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 = 0.059 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.065 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 = 0.065 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 = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.061 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.061 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.054 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.065 Round = 12 minsplit = 17.0000 cp = 0.01675548 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 = 0.06 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 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 = 0.062 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.063 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 = 0.054 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.062 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.066 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()`) ─────
<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-fedora-clang
Version: 1.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [282s/287s]
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 = 3.178 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.222 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 2.471 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 2.486 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 2.443 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 2.443 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 = 2.293 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 2.372 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 2.633 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 2.479 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 2.492 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 2.547 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 2.698 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 2.258 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 2.184 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 2.433 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 2.129 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 2.652 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 = 0.965 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 0.907 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 0.875 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 0.857 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.087 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.42 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 = 0.853 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.274 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 0.837 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 1.652 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 0.999 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 1.375 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 = 1.327 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.087 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 0.813 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 0.866 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 0.973 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 1.404 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 = 2.407 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.532 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.654 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.374 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.189 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.145 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 = 3.665 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.672 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.72 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.303 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.222 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 = 1.472 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.176 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.035 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.152 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.789 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.641 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.063 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.392 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.149 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.675 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.094 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.983 Round = 12 minsplit = 21.0000 cp = 0.04091827 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 = 0.979 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.96 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.94 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.942 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.931 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.975 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.951 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.986 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.942 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.58 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.891 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.948 Round = 12 minsplit = 38.0000 cp = 0.04115755 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 = 0.956 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.998 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.934 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.982 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.969 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.00 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.962 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.963 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.973 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.543 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.951 Round = 11 minsplit = 42.0000 cp = 0.02284809 maxdepth = 24.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.001 Round = 12 minsplit = 88.0000 cp = 0.06845087 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
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 = 0.051 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.055 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.054 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.053 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.051 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.052 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.054 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.052 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.052 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.051 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.051 Round = 12 minsplit = 65.0000 cp = 0.0439355 maxdepth = 14.0000 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 = 0.05 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 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 = 0.051 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.052 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.049 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.051 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.04 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.046 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.046 Round = 12 minsplit = 14.0000 cp = 0.03307655 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 = 0.05 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 = 0.052 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.046 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.046 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.044 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.044 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.039 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.046 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 12 minsplit = 14.0000 cp = 0.03307406 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 = 0.045 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.046 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.044 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.046 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.045 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.041 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.044 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.044 Round = 12 minsplit = 99.0000 cp = 0.04356252 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
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-linux-x86_64-fedora-gcc
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