Last updated on 2026-08-04 14:52:17 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.1 | 12.53 | 498.51 | 511.04 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.1 | 8.56 | 343.29 | 351.85 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 1.0.1 | 10.00 | 470.71 | 480.71 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.1 | 312.70 | OK | |||
| r-devel-windows-x86_64 | 1.0.1 | 18.00 | 442.00 | 460.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 12.60 | 481.50 | 494.10 | ERROR | |
| r-release-linux-x86_64 | 1.0.0 | 10.83 | 488.74 | 499.57 | ERROR | |
| r-release-macos-arm64 | 1.0.1 | 3.00 | 173.00 | 176.00 | OK | |
| r-release-macos-x86_64 | 1.0.1 | 8.00 | 569.00 | 577.00 | OK | |
| r-release-windows-x86_64 | 1.0.0 | 14.00 | 444.00 | 458.00 | ERROR | |
| r-oldrel-macos-arm64 | 1.0.1 | 2.00 | 177.00 | 179.00 | OK | |
| r-oldrel-macos-x86_64 | 1.0.1 | 8.00 | 827.00 | 835.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.1 | 14.00 | 499.00 | 513.00 | OK |
Version: 1.0.1
Check: for new files in some other directories
Result: NOTE
Found the following files/directories:
‘~/tmp/scratch/Rtmp0Ax4n3’ ‘~/tmp/scratch/Rtmp15dERr’
‘~/tmp/scratch/Rtmp1HaaBg’ ‘~/tmp/scratch/Rtmp1Zsnxu’
‘~/tmp/scratch/Rtmp1e3eYt’ ‘~/tmp/scratch/Rtmp1mMPr8’
‘~/tmp/scratch/Rtmp2XBUAj’ ‘~/tmp/scratch/Rtmp2dg3ta’
‘~/tmp/scratch/Rtmp2lqTuM’ ‘~/tmp/scratch/Rtmp2s7PQi’
‘~/tmp/scratch/Rtmp3H8l7D’ ‘~/tmp/scratch/Rtmp3JVjsz’
‘~/tmp/scratch/Rtmp3S3Gir’ ‘~/tmp/scratch/Rtmp3Yg1lv’
‘~/tmp/scratch/Rtmp3nKwKq’ ‘~/tmp/scratch/Rtmp40Z2ut’
‘~/tmp/scratch/Rtmp4IIdA5’ ‘~/tmp/scratch/Rtmp4OfAig’
‘~/tmp/scratch/Rtmp4tc07d’ ‘~/tmp/scratch/Rtmp5KaIFN’
‘~/tmp/scratch/Rtmp5XZqZz’ ‘~/tmp/scratch/Rtmp5ZxM8n’
‘~/tmp/scratch/Rtmp5y4DjK’ ‘~/tmp/scratch/Rtmp62gfdq’
‘~/tmp/scratch/Rtmp6D4beh’ ‘~/tmp/scratch/Rtmp6Fcmdv’
‘~/tmp/scratch/Rtmp6lzv8j’ ‘~/tmp/scratch/Rtmp6wi0lf’
‘~/tmp/scratch/Rtmp70bvQK’ ‘~/tmp/scratch/Rtmp72CZ7s’
‘~/tmp/scratch/Rtmp76OHz9’ ‘~/tmp/scratch/Rtmp7Fc0uZ’
‘~/tmp/scratch/Rtmp7NapVQ’ ‘~/tmp/scratch/Rtmp7VCAGI’
‘~/tmp/scratch/Rtmp7dkXFF’ ‘~/tmp/scratch/Rtmp7yOJja’
‘~/tmp/scratch/Rtmp9dvsNY’ ‘~/tmp/scratch/Rtmp9mTcTb’
‘~/tmp/scratch/Rtmp9oxkED’ ‘~/tmp/scratch/RtmpAWXurR’
‘~/tmp/scratch/RtmpAgt3z1’ ‘~/tmp/scratch/RtmpB1LL8h’
‘~/tmp/scratch/RtmpB9FBSY’ ‘~/tmp/scratch/RtmpBrJSqh’
‘~/tmp/scratch/RtmpBszawQ’ ‘~/tmp/scratch/RtmpC4BXmd’
‘~/tmp/scratch/RtmpCrrd9s’ ‘~/tmp/scratch/RtmpCzxU75’
‘~/tmp/scratch/RtmpDBEm19’ ‘~/tmp/scratch/RtmpDcbBEQ’
‘~/tmp/scratch/RtmpDfzPIo’ ‘~/tmp/scratch/RtmpDmrVqd’
‘~/tmp/scratch/RtmpET5Zey’ ‘~/tmp/scratch/RtmpEdNy42’
‘~/tmp/scratch/RtmpEkp8fy’ ‘~/tmp/scratch/RtmpEwOdgm’
‘~/tmp/scratch/RtmpFWNJ1M’ ‘~/tmp/scratch/RtmpFadYut’
‘~/tmp/scratch/RtmpFnuikp’ ‘~/tmp/scratch/RtmpGQY9LZ’
‘~/tmp/scratch/RtmpGsdSzj’ ‘~/tmp/scratch/RtmpH2jXjb’
‘~/tmp/scratch/RtmpH753tQ’ ‘~/tmp/scratch/RtmpHbBuAZ’
‘~/tmp/scratch/RtmpHg2tii’ ‘~/tmp/scratch/RtmpJujyNj’
‘~/tmp/scratch/RtmpKCFu7d’ ‘~/tmp/scratch/RtmpKHwi30’
‘~/tmp/scratch/RtmpKWjOWM’ ‘~/tmp/scratch/RtmpKidsrP’
‘~/tmp/scratch/RtmpKoa3iv’ ‘~/tmp/scratch/RtmpLfvpBe’
‘~/tmp/scratch/RtmpLpoQU4’ ‘~/tmp/scratch/RtmpM8epBf’
‘~/tmp/scratch/RtmpMLhXRy’ ‘~/tmp/scratch/RtmpMPlxWV’
‘~/tmp/scratch/RtmpMpSFur’ ‘~/tmp/scratch/RtmpN3jha3’
‘~/tmp/scratch/RtmpNEMFIJ’ ‘~/tmp/scratch/RtmpNHw7Ed’
‘~/tmp/scratch/RtmpNzVWqQ’ ‘~/tmp/scratch/RtmpO6NUGS’
‘~/tmp/scratch/RtmpORiLvv’ ‘~/tmp/scratch/RtmpOaLoY5’
‘~/tmp/scratch/RtmpOpCXvJ’ ‘~/tmp/scratch/RtmpOsaJ8L’
‘~/tmp/scratch/RtmpP1amNw’ ‘~/tmp/scratch/RtmpPYLPdS’
‘~/tmp/scratch/RtmpQ6NEzk’ ‘~/tmp/scratch/RtmpQucf1n’
‘~/tmp/scratch/RtmpRTQIDW’ ‘~/tmp/scratch/RtmpSB9Eih’
‘~/tmp/scratch/RtmpSij98m’ ‘~/tmp/scratch/RtmpSxMuVv’
‘~/tmp/scratch/RtmpTmB1w0’ ‘~/tmp/scratch/RtmpUIAu2k’
‘~/tmp/scratch/RtmpUWISr9’ ‘~/tmp/scratch/RtmpUXOJOp’
‘~/tmp/scratch/RtmpUhNKDB’ ‘~/tmp/scratch/RtmpUukWrY’
‘~/tmp/scratch/RtmpUxDIM2’ ‘~/tmp/scratch/RtmpVLD3pO’
‘~/tmp/scratch/RtmpVRQ3jd’ ‘~/tmp/scratch/RtmpViC5yu’
‘~/tmp/scratch/RtmpViIZss’ ‘~/tmp/scratch/RtmpW1C5St’
‘~/tmp/scratch/RtmpWFzDjo’ ‘~/tmp/scratch/RtmpWSNzMh’
‘~/tmp/scratch/RtmpWTcRx7’ ‘~/tmp/scratch/RtmpWUz9MX’
‘~/tmp/scratch/RtmpWaSqQU’ ‘~/tmp/scratch/RtmpWgKl1u’
‘~/tmp/scratch/RtmpWgNDV5’ ‘~/tmp/scratch/RtmpXfOv56’
‘~/tmp/scratch/RtmpYDXwzn’ ‘~/tmp/scratch/RtmpYKav2g’
‘~/tmp/scratch/RtmpYWuPEW’ ‘~/tmp/scratch/RtmpZvoc8k’
‘~/tmp/scratch/RtmpZyc6Oj’ ‘~/tmp/scratch/Rtmpa96q0Z’
‘~/tmp/scratch/RtmpaFcOkO’ ‘~/tmp/scratch/RtmpaJJIDg’
‘~/tmp/scratch/RtmpaSLqqS’ ‘~/tmp/scratch/RtmpbCdw7J’
‘~/tmp/scratch/RtmpbpEZPH’ ‘~/tmp/scratch/RtmpcLkQ5e’
‘~/tmp/scratch/RtmpcNEYQO’ ‘~/tmp/scratch/RtmpckphoU’
‘~/tmp/scratch/RtmpdIMbor’ ‘~/tmp/scratch/RtmpdMsP7T’
‘~/tmp/scratch/RtmpdlWS08’ ‘~/tmp/scratch/Rtmpe4pZ4w’
‘~/tmp/scratch/RtmpemQr9l’ ‘~/tmp/scratch/RtmpemV92J’
‘~/tmp/scratch/RtmpfACEda’ ‘~/tmp/scratch/RtmpfB2QYB’
‘~/tmp/scratch/RtmpfRj3U8’ ‘~/tmp/scratch/Rtmpft93XA’
‘~/tmp/scratch/RtmpftfweS’ ‘~/tmp/scratch/RtmpgWehoz’
‘~/tmp/scratch/Rtmpgjl4iP’ ‘~/tmp/scratch/RtmpgkVP9c’
‘~/tmp/scratch/RtmphDWTtH’ ‘~/tmp/scratch/RtmphvZiNM’
‘~/tmp/scratch/RtmpjGvI7J’ ‘~/tmp/scratch/RtmpjOIYS3’
‘~/tmp/scratch/RtmpjgldUy’ ‘~/tmp/scratch/RtmpkLde72’
‘~/tmp/scratch/RtmpkfYGo2’ ‘~/tmp/scratch/RtmpksHe5e’
‘~/tmp/scratch/RtmplhBVBV’ ‘~/tmp/scratch/Rtmplrz0zt’
‘~/tmp/scratch/RtmplyeakT’ ‘~/tmp/scratch/Rtmpm6mDBl’
‘~/tmp/scratch/Rtmpml0BcJ’ ‘~/tmp/scratch/RtmpmvpX28’
‘~/tmp/scratch/Rtmpo6REeF’ ‘~/tmp/scratch/Rtmpo8xAvT’
‘~/tmp/scratch/RtmpoMO81z’ ‘~/tmp/scratch/RtmpoftEPz’
‘~/tmp/scratch/RtmpojnnWW’ ‘~/tmp/scratch/RtmppEubpy’
‘~/tmp/scratch/RtmppI8cjy’ ‘~/tmp/scratch/RtmppZnjFU’
‘~/tmp/scratch/Rtmppj9wu1’ ‘~/tmp/scratch/RtmppmaiSq’
‘~/tmp/scratch/Rtmpq1Wjx3’ ‘~/tmp/scratch/RtmpqIF2OD’
‘~/tmp/scratch/Rtmpqt4j2U’ ‘~/tmp/scratch/Rtmpr20xU3’
‘~/tmp/scratch/RtmprLKOKB’ ‘~/tmp/scratch/Rtmpre9YB2’
‘~/tmp/scratch/Rtmprjfk1Y’ ‘~/tmp/scratch/RtmprkGMBM’
‘~/tmp/scratch/RtmpsXtP5E’ ‘~/tmp/scratch/RtmpssF4Eo’
‘~/tmp/scratch/RtmptCTJCn’ ‘~/tmp/scratch/RtmptoeXa1’
‘~/tmp/scratch/Rtmpu59tYC’ ‘~/tmp/scratch/RtmpuSMXzU’
‘~/tmp/scratch/RtmpvVNqRB’ ‘~/tmp/scratch/Rtmpvp0QcA’
‘~/tmp/scratch/RtmpwWvsXr’ ‘~/tmp/scratch/RtmpwcyKCL’
‘~/tmp/scratch/RtmpxK0zyN’ ‘~/tmp/scratch/RtmpxLc8oK’
‘~/tmp/scratch/RtmpxkFvxu’ ‘~/tmp/scratch/RtmpxoslPu’
‘~/tmp/scratch/Rtmpxy3cgA’ ‘~/tmp/scratch/Rtmpy3m7Mm’
‘~/tmp/scratch/RtmpyFCWbp’ ‘~/tmp/scratch/RtmpyODjUC’
‘~/tmp/scratch/Rtmpz0pg4l’ ‘~/tmp/scratch/Rtmpzpk44S’
‘~/tmp/scratch/xvfb-run.01Ajlr’ ‘~/tmp/scratch/xvfb-run.0XAyqp’
‘~/tmp/scratch/xvfb-run.0l09or’ ‘~/tmp/scratch/xvfb-run.1bkKQo’
‘~/tmp/scratch/xvfb-run.1ivaf3’ ‘~/tmp/scratch/xvfb-run.1neRP2’
‘~/tmp/scratch/xvfb-run.2Z8via’ ‘~/tmp/scratch/xvfb-run.4LptBX’
‘~/tmp/scratch/xvfb-run.6cr1Ep’ ‘~/tmp/scratch/xvfb-run.8R4JbA’
‘~/tmp/scratch/xvfb-run.8gdRKE’ ‘~/tmp/scratch/xvfb-run.9H8I9p’
‘~/tmp/scratch/xvfb-run.9gx3ex’ ‘~/tmp/scratch/xvfb-run.B5paAN’
‘~/tmp/scratch/xvfb-run.BgZiFU’ ‘~/tmp/scratch/xvfb-run.C2zI0C’
‘~/tmp/scratch/xvfb-run.CaEIJQ’ ‘~/tmp/scratch/xvfb-run.Dpiikt’
‘~/tmp/scratch/xvfb-run.GXU5gx’ ‘~/tmp/scratch/xvfb-run.HGXFPg’
‘~/tmp/scratch/xvfb-run.IcH7d1’ ‘~/tmp/scratch/xvfb-run.JvWFr5’
‘~/tmp/scratch/xvfb-run.KBWCUz’ ‘~/tmp/scratch/xvfb-run.L79WHE’
‘~/tmp/scratch/xvfb-run.LY1NXp’ ‘~/tmp/scratch/xvfb-run.M3S2sF’
‘~/tmp/scratch/xvfb-run.NMp7nG’ ‘~/tmp/scratch/xvfb-run.NuL7cG’
‘~/tmp/scratch/xvfb-run.OgiroO’ ‘~/tmp/scratch/xvfb-run.P9Ymso’
‘~/tmp/scratch/xvfb-run.PJcjJI’ ‘~/tmp/scratch/xvfb-run.PjJLlZ’
‘~/tmp/scratch/xvfb-run.Q6hnzM’ ‘~/tmp/scratch/xvfb-run.QDFRSo’
‘~/tmp/scratch/xvfb-run.Qhxrbx’ ‘~/tmp/scratch/xvfb-run.QxtFLr’
‘~/tmp/scratch/xvfb-run.SSlgJn’ ‘~/tmp/scratch/xvfb-run.TZ0MKN’
‘~/tmp/scratch/xvfb-run.U2pQcJ’ ‘~/tmp/scratch/xvfb-run.UpRvdY’
‘~/tmp/scratch/xvfb-run.UxEqMf’ ‘~/tmp/scratch/xvfb-run.V0dPeL’
‘~/tmp/scratch/xvfb-run.VVbujz’ ‘~/tmp/scratch/xvfb-run.WI2gUM’
‘~/tmp/scratch/xvfb-run.WS2QES’ ‘~/tmp/scratch/xvfb-run.WSNKXk’
‘~/tmp/scratch/xvfb-run.Yk9FtJ’ ‘~/tmp/scratch/xvfb-run.a6CYPH’
‘~/tmp/scratch/xvfb-run.aDsntd’ ‘~/tmp/scratch/xvfb-run.aIY6gI’
‘~/tmp/scratch/xvfb-run.aW9q59’ ‘~/tmp/scratch/xvfb-run.aWusNC’
‘~/tmp/scratch/xvfb-run.b6y4cf’ ‘~/tmp/scratch/xvfb-run.bKTgUO’
‘~/tmp/scratch/xvfb-run.dyyage’ ‘~/tmp/scratch/xvfb-run.eKGuCE’
‘~/tmp/scratch/xvfb-run.fESeWM’ ‘~/tmp/scratch/xvfb-run.gdd9cT’
‘~/tmp/scratch/xvfb-run.hiUaq9’ ‘~/tmp/scratch/xvfb-run.iW4dlW’
‘~/tmp/scratch/xvfb-run.j6K3k2’ ‘~/tmp/scratch/xvfb-run.jHaOWj’
‘~/tmp/scratch/xvfb-run.kTjR1O’ ‘~/tmp/scratch/xvfb-run.kXeJzv’
‘~/tmp/scratch/xvfb-run.mVRiCt’ ‘~/tmp/scratch/xvfb-run.npQeSh’
‘~/tmp/scratch/xvfb-run.o95ZbX’ ‘~/tmp/scratch/xvfb-run.orVZVg’
‘~/tmp/scratch/xvfb-run.pCCGvk’ ‘~/tmp/scratch/xvfb-run.pCvUTg’
‘~/tmp/scratch/xvfb-run.pKBrlN’ ‘~/tmp/scratch/xvfb-run.paBF6j’
‘~/tmp/scratch/xvfb-run.qEdY14’ ‘~/tmp/scratch/xvfb-run.uVNBxt’
‘~/tmp/scratch/xvfb-run.wfpA2B’ ‘~/tmp/scratch/xvfb-run.x8kbfE’
‘~/tmp/scratch/xvfb-run.xEAvEv’ ‘~/tmp/scratch/xvfb-run.y9nM95’
‘~/tmp/scratch/xvfb-run.yfovB1’
‘/dev/shm/sm_segment.gimli1.1001.aa670000.0’
‘~/.cache/pocl/uncached/tempfile_0Jewyg’
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [398s/464s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
>
> Sys.setenv("OMP_THREAD_LIMIT" = 2)
> Sys.setenv("Ncpu" = 2)
>
> library(testthat)
> library(mlexperiments)
>
> test_check("mlexperiments")
Saving _problems/test-fold_equality-5.R
Saving _problems/test-glm-5.R
Saving _problems/test-glm_predictions-5.R
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 4.623 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 6.098 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 5.024 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 4.516 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 5.374 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 4.119 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 3.936 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.128 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.233 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.517 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.789 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.706 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.89 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 3.81 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 4.583 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 5.111 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 3.787 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 4.508 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 1.713 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 1.402 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.74 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 1.522 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.404 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.727 Round = 6 k = 52.0000 Value = -0.1290153
Best Parameters Found:
Round = 6 k = 52.0000 Value = -0.1290153
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 1.71 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.695 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 1.455 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 3.639 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 1.731 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 1.331 Round = 6 k = 51.0000 Value = -0.1243006
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1082897
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 1.506 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.501 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 1.451 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 1.622 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.465 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 1.403 Round = 6 k = 50.0000 Value = -0.1341896
Best Parameters Found:
Round = 2 k = 64.0000 Value = -0.1299503
CV fold: Fold1
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.291 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.466 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.808 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.32 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.264 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.52 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.379 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.031 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.304 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.725 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.532 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.283 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.465 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.599 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.471 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.535 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.444 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.447 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.81 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.586 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.569 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.339 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.273 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.82 Round = 12 minsplit = 78.0000 cp = 0.02853145 maxdepth = 30.0000 Value = -0.09558117
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.344 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.803 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.541 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.561 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.678 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.452 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.41 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.897 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.044 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.123 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.452 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.025 Round = 12 minsplit = 82.0000 cp = 0.07214655 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.399 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.923 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.581 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.683 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.564 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.779 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.552 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.822 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.577 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.947 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.829 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.354 Round = 12 minsplit = 78.0000 cp = 0.0395761 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.149 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.073 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.063 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.099 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 12 minsplit = 25.0000 cp = 0.05205146 maxdepth = 7.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.128 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.107 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.146 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.079 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 12 minsplit = 83.0000 cp = 0.08284277 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.139 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.084 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.069 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.199 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.098 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.086 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.111 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.268 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.096 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.145 Round = 12 minsplit = 12.0000 cp = 0.05804747 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.144 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.205 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.183 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.124 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.067 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.118 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.144 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.099 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.064 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.078 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.149 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.148 Round = 12 minsplit = 72.0000 cp = 0.05062197 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.R:3:1'): (code run outside of `test_that()`) ─────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.R:3:1'): (code run outside of `test_that()`) ───────────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.R:3:1'): (code run outside of `test_that()`) ───
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-patched-linux-x86_64
Version: 1.0.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [406s/471s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
>
> Sys.setenv("OMP_THREAD_LIMIT" = 2)
> Sys.setenv("Ncpu" = 2)
>
> library(testthat)
> library(mlexperiments)
>
> test_check("mlexperiments")
Saving _problems/test-fold_equality-5.R
Saving _problems/test-glm-5.R
Saving _problems/test-glm_predictions-5.R
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 5.272 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.866 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 4.62 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 7.056 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 4.866 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 4.686 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 4.365 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 4.147 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 4.369 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 4.216 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 4.356 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 4.513 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 4.212 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 4.632 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 3.511 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 4.442 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 3.668 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 4.199 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 1.736 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 1.581 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.387 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 1.648 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.49 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.486 Round = 6 k = 52.0000 Value = -0.1290153
Best Parameters Found:
Round = 6 k = 52.0000 Value = -0.1290153
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 1.746 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.953 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 3.908 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 2.377 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 1.57 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 1.613 Round = 6 k = 51.0000 Value = -0.1243006
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1082897
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 1.421 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.507 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 1.406 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 1.477 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.584 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 1.583 Round = 6 k = 50.0000 Value = -0.1341896
Best Parameters Found:
Round = 2 k = 64.0000 Value = -0.1299503
CV fold: Fold1
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Parameter settings [=======>------------------------------------] 2/11 ( 18%)
Parameter settings [===========>--------------------------------] 3/11 ( 27%)
Parameter settings [===============>----------------------------] 4/11 ( 36%)
Parameter settings [===================>------------------------] 5/11 ( 45%)
Parameter settings [=======================>--------------------] 6/11 ( 55%)
Parameter settings [===========================>----------------] 7/11 ( 64%)
Parameter settings [===============================>------------] 8/11 ( 73%)
Parameter settings [===================================>--------] 9/11 ( 82%)
Parameter settings [======================================>----] 10/11 ( 91%)
Parameter settings [===========================================] 11/11 (100%)
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold2
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold3
Parameter 'ncores' is ignored for learner 'LearnerLm'.
CV fold: Fold1
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.466 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.054 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.762 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.284 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.758 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.609 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.289 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.617 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 4.379 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.222 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.79 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.956 Round = 12 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.636 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.564 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.703 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.604 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.552 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.769 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.688 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.792 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.505 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.978 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.493 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.353 Round = 12 minsplit = 78.0000 cp = 0.02853145 maxdepth = 30.0000 Value = -0.09558117
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.532 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.186 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.62 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.439 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.118 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.795 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.446 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.731 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.317 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.258 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.035 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.355 Round = 12 minsplit = 82.0000 cp = 0.07214655 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.95 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.966 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.862 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.464 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.615 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.371 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.766 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.495 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.552 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.043 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.557 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.94 Round = 12 minsplit = 78.0000 cp = 0.0395761 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.135 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.13 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.081 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.078 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.074 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.079 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.079 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.162 Round = 12 minsplit = 25.0000 cp = 0.05205146 maxdepth = 7.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.125 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.09 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.211 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.10 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.103 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.148 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.081 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.075 Round = 12 minsplit = 83.0000 cp = 0.08284277 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.094 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.065 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.123 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.068 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.088 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.097 Round = 12 minsplit = 12.0000 cp = 0.05804747 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.077 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.088 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.071 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.076 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.143 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.101 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.069 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.072 Round = 12 minsplit = 72.0000 cp = 0.05062197 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.R:3:1'): (code run outside of `test_that()`) ─────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.R:3:1'): (code run outside of `test_that()`) ───────────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.R:3:1'): (code run outside of `test_that()`) ───
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-linux-x86_64
Version: 1.0.0
Check: tests
Result: ERROR
Running 'testthat.R' [331s]
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 = 3.29 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.22 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.33 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.26 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.19 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.22 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 3.26 Round = 1 k = 16.0000 Value = -0.1487759
elapsed = 3.01 Round = 2 k = 64.0000 Value = -0.123666
elapsed = 3.08 Round = 3 k = 10.0000 Value = -0.1638418
elapsed = 3.55 Round = 4 k = 34.0000 Value = -0.1321406
elapsed = 3.30 Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.33 Round = 6 k = 50.0000 Value = -0.1246077
Best Parameters Found:
Round = 5 k = 80.0000 Value = -0.1217828
elapsed = 3.27 Round = 1 k = 24.0000 Value = -0.1440678
elapsed = 3.30 Round = 2 k = 63.0000 Value = -0.1233522
elapsed = 3.10 Round = 3 k = 34.0000 Value = -0.1321406
elapsed = 3.44 Round = 4 k = 71.0000 Value = -0.1220967
elapsed = 2.99 Round = 5 k = 2.0000 Value = -0.2743252
elapsed = 3.52 Round = 6 k = 80.0000 Value = -0.1217828
Best Parameters Found:
Round = 6 k = 80.0000 Value = -0.1217828
Parameter settings [=============================>---------------] 2/3 ( 67%)
Parameter settings [=============================================] 3/3 (100%)
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 4 rows.
elapsed = 0.96 Round = 1 k = 16.0000 Value = -0.1520798
elapsed = 0.99 Round = 2 k = 64.0000 Value = -0.1313681
elapsed = 1.01 Round = 3 k = 10.0000 Value = -0.1859821
elapsed = 1.01 Round = 4 k = 34.0000 Value = -0.1398453
elapsed = 1.00 Round = 5 k = 65.0000 Value = -0.132307
elapsed = 1.10 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 = 0.97 Round = 1 k = 16.0000 Value = -0.1577268
elapsed = 1.00 Round = 2 k = 64.0000 Value = -0.1153539
elapsed = 0.94 Round = 3 k = 10.0000 Value = -0.1732636
elapsed = 1.02 Round = 4 k = 34.0000 Value = -0.1360669
elapsed = 1.08 Round = 5 k = 80.0000 Value = -0.1082897
elapsed = 0.98 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 = 1.03 Round = 1 k = 16.0000 Value = -0.1577195
elapsed = 1.01 Round = 2 k = 64.0000 Value = -0.1299503
elapsed = 0.90 Round = 3 k = 10.0000 Value = -0.1798522
elapsed = 0.98 Round = 4 k = 34.0000 Value = -0.1384196
elapsed = 1.05 Round = 5 k = 77.0000 Value = -0.1304132
elapsed = 0.96 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 = 2.92 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.32 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.02 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.31 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.20 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.25 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.06 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 3.26 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.92 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09196485
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.65 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2715003
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.78 Round = 11 minsplit = 100.0000 cp = 0.03567893 maxdepth = 4.0000 Value = -0.106403
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 2.70 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 = 1.22 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.41 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.36 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.42 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.26 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.22 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.13 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.06 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.16 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.09558117
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.75 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2853118
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.08 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.09746574
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.36 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 = 1.23 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.28 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.30 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.20 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.13 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.04 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.09 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.13 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.25 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.72 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2806083
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.06 Round = 11 minsplit = 83.0000 cp = 0.03306856 maxdepth = 6.0000 Value = -0.08333478
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.38 Round = 12 minsplit = 88.0000 cp = 0.06845091 maxdepth = 30.0000 Value = -0.08333478
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.08333478
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.08 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.11 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.20 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.23 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.42 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.35 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -0.1148897
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.19 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.31 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.29 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 0.77 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -0.2796667
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.27 Round = 11 minsplit = 10.0000 cp = 0.07788214 maxdepth = 15.0000 Value = -0.1130091
Classification: using 'mean misclassification error' as optimization metric.
elapsed = 1.25 Round = 12 minsplit = 88.0000 cp = 0.06845084 maxdepth = 30.0000 Value = -0.1130091
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -0.1130091
CV fold: Fold1
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================>---------------] 2/3 ( 67%)
Classification: using 'mean misclassification error' as optimization metric.
Parameter settings [=============================================] 3/3 (100%)
Classification: using 'mean misclassification error' as optimization metric.
CV fold: Fold1
CV fold: Fold2
CV fold: Fold3
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -27.95512
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 11 minsplit = 34.0000 cp = 0.0376811 maxdepth = 27.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 12 minsplit = 50.0000 cp = 0.01435421 maxdepth = 15.0000 Value = -23.45392
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.45392
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold1
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 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 = 0.06 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.04 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.04 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -32.38194
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -27.46583
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.04 Round = 12 minsplit = 21.0000 cp = 0.01106419 maxdepth = 30.0000 Value = -27.46583
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.46583
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 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 = 0.05 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 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 = 0.05 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -28.80713
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.04 Round = 11 minsplit = 69.0000 cp = 0.05877216 maxdepth = 20.0000 Value = -23.77709
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 12 minsplit = 46.0000 cp = 0.02893404 maxdepth = 30.0000 Value = -23.77709
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -23.77709
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Number of rows of initialization grid > than 'options("mlexperiments.bayesian.max_init")'...
... reducing initialization grid to 10 rows.
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 2 minsplit = 32.0000 cp = 0.0200 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 3 minsplit = 72.0000 cp = 0.1000 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 4 minsplit = 32.0000 cp = 0.0900 maxdepth = 27.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 5 minsplit = 52.0000 cp = 0.0200 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 6 minsplit = 2.0000 cp = 0.0400 maxdepth = 7.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.04 Round = 7 minsplit = 12.0000 cp = 0.0400 maxdepth = 17.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 8 minsplit = 32.0000 cp = 0.0600 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.08 Round = 9 minsplit = 2.0000 cp = 0.0800 maxdepth = 12.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.06 Round = 10 minsplit = 42.0000 cp = 0.0200 maxdepth = 2.0000 Value = -35.47854
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.05 Round = 11 minsplit = 10.0000 cp = 0.06931338 maxdepth = 20.0000 Value = -27.74913
Regression: using 'mean squared error' as optimization metric.
elapsed = 0.07 Round = 12 minsplit = 29.0000 cp = 0.09896765 maxdepth = 30.0000 Value = -27.74913
Best Parameters Found:
Round = 1 minsplit = 2.0000 cp = 0.0700 maxdepth = 22.0000 Value = -27.74913
CV fold: Fold1
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold2
CV progress [==================================>-----------------] 2/3 ( 67%)
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
CV fold: Fold3
CV progress [====================================================] 3/3 (100%)
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
Regression: using 'mean squared error' as optimization metric.
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-lints.R:10:5'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-fold_equality.R:3:1'): (code run outside of `test_that()`) ─────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-fold_equality.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm.R:3:1'): (code run outside of `test_that()`) ───────────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
── Error ('test-glm_predictions.R:3:1'): (code run outside of `test_that()`) ───
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
Backtrace:
▆
1. ├─stats::na.omit(data.table::as.data.table(PimaIndiansDiabetes2)) at test-glm_predictions.R:3:1
2. └─data.table::as.data.table(PimaIndiansDiabetes2)
[ FAIL 3 | WARN 3 | SKIP 1 | PASS 58 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-windows-x86_64