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calling tune_grid crashes RStudio (->factor variables?) #62

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felxcon opened this issue Mar 21, 2022 · 0 comments
Open

calling tune_grid crashes RStudio (->factor variables?) #62

felxcon opened this issue Mar 21, 2022 · 0 comments

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@felxcon
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felxcon commented Mar 21, 2022

Hi,
since yesterday I run into immediate crashes of RStudio when running the following code with more than 1 factor variables and multiple level, or multiple factor variables of 1 level. This might be associated with new versions of tidymodels, tune, parsnip, workflows, because I updated them yesterday, but I couldn't restore the previous version setting, that, if I remember well didn't cause RStudio to crash! (not 100% sure).
This is the code, try with factorized variables and without, that should reproduce the error [crossed fingers].
I am not deep into BRT like LightGBM, do variables need to be factorized to be "recognized" as such by the algorithm(s) or does this not matter at all because of the algorithm's nature like lightGBM?

Kindly, felix

library(doParallel) #include multithreadding and parallizing processess where possible
library(foreach) #Provides foreach looping construct
UseCores <- detectCores() -1
# #Register CoreCluster
cl       <- makeCluster(UseCores)
registerDoParallel(cl)

library(lightgbm)
pacman::p_load(
  janitor, #data cleaning
  recipes,
  rsample,
  parsnip,
  workflows,
  tune,
  dials,
  yardstick,
  treesnip,
  tidyverse,
  tidymodels,
  plyr,
  dplyr,
  tidyr,
  readr,
  stringr,
  gtools,
  ggplot2,
  reshape2,
  purrr,
  data.table
)

#this works
mmmf_mixedVAR_simple_qf = structure(list(QF2020scenario = c(41.6104850769043, 68.6856307983398, 
57.3654022216797, 28.7580642700195, 45.1602096557617, 47.2106628417969, 
68.6856307983398, 71.7784652709961, 24.4756469726562, 40.3135414123535, 
23.0632152557373, 24.1012935638428, 68.1585693359375, 63.6638069152832, 
41.6104850769043, 35.8073768615723, 68.6856307983398, 54.6796913146973, 
48.6273994445801, 41.6104850769043, 41.6104850769043, 89.0888595581055, 
50.85595703125, 41.8495635986328, 75.2088851928711, 37.8572235107422, 
76.5240020751953, 27.1052436828613, 34.5876998901367, 28.7580642700195, 
42.9002914428711, 27.1052436828613, 29.2019214630127, 34.5320816040039, 
40.723876953125, 57.3654022216797, 54.1622505187988, 41.5773963928223, 
39.8819427490234, 32.0537185668945, 40.9108467102051, 41.5037727355957, 
28.6135368347168, 47.2106628417969, 68.6856307983398, 36.1073341369629, 
40.440845489502, 48.0962562561035, 74.1079177856445, 23.0632152557373, 
58.8290863037109, 50.85595703125, 24.9130783081055, 87.9564056396484, 
65.1510391235352, 28.7580642700195, 28.6135368347168, 48.6273994445801, 
56.6535720825195, 31.4044914245605, 89.0888595581055, 42.9002914428711, 
40.723876953125, 54.8065490722656, 48.5243873596191, 41.2597579956055, 
22.1660785675049, 39.8819427490234, 27.2910995483398, 56.6535720825195, 
40.723876953125, 41.6104850769043, 58.8290863037109, 37.8572235107422, 
34.5320816040039, 79.2940444946289, 22.6065940856934, 57.3654022216797, 
77.4911727905273, 26.6769046783447, 74.1079177856445, 45.1602096557617, 
79.2940444946289, 36.1073341369629, 28.7580642700195, 68.1585693359375, 
46.0501861572266, 27.2910995483398, 48.1491203308105, 71.7784652709961, 
68.0657424926758, 54.1622505187988, 44.9057807922363, 26.5627517700195, 
93.6683654785156, 23.0632152557373, 38.9476852416992, 48.6273994445801, 
28.7580642700195, 40.440845489502), MMMFsize = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L), .Label = c("624.9", "1249.8", "1874.6", "1874.7", 
"2499.5", "2499.6", "3124.4", "3124.5", "3749.3", "3749.4", "4999.1", 
"4999.2", "6248.9"), class = "factor"), DEM = c(1378.26611328125, 
974.038024902344, 1185.93322753906, 671.142700195312, 1395.73779296875, 
1257.41296386719, 863.424499511719, 1140.35339355469, 1240.84362792969, 
846.944030761719, 864.497497558594, 1451.91491699219, 913.910522460938, 
1063.34252929688, 1784.55932617188, 1390.81921386719, 1312.2763671875, 
1656.35778808594, 1273.79150390625, 1221.74926757812, 1278.97338867188, 
1039.95471191406, 1548.49108886719, 1124.38513183594, 1080.16455078125, 
895.399963378906, 1248.04418945312, 1637.8017578125, 1257.97521972656, 
697.554016113281, 1007.21697998047, 1405.5810546875, 1640.0732421875, 
680.0546875, 1188.71472167969, 1348.49426269531, 1692.85485839844, 
809.52734375, 1069.23974609375, 832.667541503906, 1056.05895996094, 
983.139587402344, 720.094604492188, 1692.974609375, 1284.35400390625, 
1618.52624511719, 925.88134765625, 1287.73107910156, 917.59375, 
740.259094238281, 1020.67596435547, 1245.60473632812, 1178.15710449219, 
1194.00207519531, 1027.35168457031, 694.300720214844, 717.023986816406, 
1905.86767578125, 1113.28002929688, 669.358154296875, 1170.1513671875, 
1143.10107421875, 1163.02563476562, 1591.00939941406, 1369.50964355469, 
924.715576171875, 1223.99816894531, 986.636840820312, 1388.10412597656, 
1064.22509765625, 1019.59216308594, 876.469787597656, 1090.61096191406, 
1315.27526855469, 1266.10583496094, 1157.1943359375, 1229.54321289062, 
1376.40209960938, 1154.36730957031, 1365.8759765625, 840.7255859375, 
947.099365234375, 1402.03540039062, 1509.984375, 711.320678710938, 
864.457153320312, 1516.41235351562, 1297.19689941406, 1970.48754882812, 
992.990478515625, 1284.78002929688, 1398.86865234375, 1365.95983886719, 
1524.95874023438, 1135.64111328125, 1386.25695800781, 1407.48962402344, 
1354.19921875, 702.702270507812, 1183.13366699219)), row.names = c(NA, 
-100L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000022c227e1ef0>)

#this crashes
mmmf_mixedVAR_simple_qf = structure(list(QF2020scenario = c(41.6104850769043, 68.6856307983398, 
57.3654022216797, 28.7580642700195, 45.1602096557617, 47.2106628417969, 
68.6856307983398, 71.7784652709961, 24.4756469726562, 40.3135414123535, 
23.0632152557373, 24.1012935638428, 68.1585693359375, 63.6638069152832, 
41.6104850769043, 35.8073768615723, 68.6856307983398, 54.6796913146973, 
48.6273994445801, 41.6104850769043, 41.6104850769043, 89.0888595581055, 
50.85595703125, 41.8495635986328, 75.2088851928711, 37.8572235107422, 
76.5240020751953, 27.1052436828613, 34.5876998901367, 28.7580642700195, 
42.9002914428711, 27.1052436828613, 29.2019214630127, 34.5320816040039, 
40.723876953125, 57.3654022216797, 54.1622505187988, 41.5773963928223, 
39.8819427490234, 32.0537185668945, 40.9108467102051, 41.5037727355957, 
28.6135368347168, 47.2106628417969, 68.6856307983398, 36.1073341369629, 
40.440845489502, 48.0962562561035, 74.1079177856445, 23.0632152557373, 
58.8290863037109, 50.85595703125, 24.9130783081055, 87.9564056396484, 
65.1510391235352, 28.7580642700195, 28.6135368347168, 48.6273994445801, 
56.6535720825195, 31.4044914245605, 89.0888595581055, 42.9002914428711, 
40.723876953125, 54.8065490722656, 48.5243873596191, 41.2597579956055, 
22.1660785675049, 39.8819427490234, 27.2910995483398, 56.6535720825195, 
40.723876953125, 41.6104850769043, 58.8290863037109, 37.8572235107422, 
34.5320816040039, 79.2940444946289, 22.6065940856934, 57.3654022216797, 
77.4911727905273, 26.6769046783447, 74.1079177856445, 45.1602096557617, 
79.2940444946289, 36.1073341369629, 28.7580642700195, 68.1585693359375, 
46.0501861572266, 27.2910995483398, 48.1491203308105, 71.7784652709961, 
68.0657424926758, 54.1622505187988, 44.9057807922363, 26.5627517700195, 
93.6683654785156, 23.0632152557373, 38.9476852416992, 48.6273994445801, 
28.7580642700195, 40.440845489502), QF2020baseline = c(131.406265258789, 
181.709228515625, 169.186233520508, 419.544281005859, 140.61865234375, 
146.94059753418, 181.709228515625, 188.410766601562, 94.6697769165039, 
342.659301757812, 91.3541030883789, 93.9609985351562, 68.1585693359375, 
173.479064941406, 278.51025390625, 257.252197265625, 181.709228515625, 
165.460083007812, 149.839279174805, 41.6104850769043, 41.6104850769043, 
89.0888595581055, 83.1039886474609, 280.182220458984, 75.2088851928711, 
37.8572235107422, 200.087020874023, 244.59294128418, 116.486618041992, 
252.553466796875, 134.824676513672, 27.1052436828613, 256.261657714844, 
467.705383300781, 132.259353637695, 169.186233520508, 337.075317382812, 
351.096618652344, 126.392868041992, 108.81022644043, 130.170455932617, 
132.241760253906, 253.748397827148, 307.326568603516, 181.709228515625, 
129.052230834961, 128.383041381836, 148.36784362793, 193.594207763672, 
252.822570800781, 58.8290863037109, 50.85595703125, 24.9130783081055, 
472.049530029297, 65.1510391235352, 252.553466796875, 28.6135368347168, 
79.4798583984375, 511.481689453125, 270.176391601562, 89.0888595581055, 
134.824676513672, 132.259353637695, 166.476776123047, 149.569839477539, 
41.2597579956055, 87.522834777832, 126.392868041992, 104.307945251465, 
56.6535720825195, 132.259353637695, 41.6104850769043, 163.606430053711, 
124.32772064209, 34.5320816040039, 205.243103027344, 90.1506805419922, 
169.186233520508, 434.497650146484, 102.199165344238, 193.594207763672, 
140.61865234375, 438.991790771484, 129.052230834961, 252.553466796875, 
183.919052124023, 144.040237426758, 27.2910995483398, 310.386657714844, 
188.410766601562, 68.0657424926758, 164.270843505859, 139.579650878906, 
102.060707092285, 235.561004638672, 23.0632152557373, 126.467643737793, 
149.839279174805, 252.553466796875, 128.383041381836), HSG = structure(c(1L, 
3L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 3L, 
1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 
1L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 
1L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 
3L, 1L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 
1L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 
1L, 1L, 3L), .Label = c("3", "3.5", "4"), class = "factor")), row.names = c(NA, 
-100L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000022c227e1ef0>)

# set the random seed so we can reproduce any simulated results.
set.seed(1234)

# load the housing data and clean names
mmmf_mixedVAR_simple_janitor_clean = mmmf_mixedVAR_simple_qf %>% janitor::clean_names()

# split into training and testing datasets. Stratify by Sale price 
mmmf_mixedVAR_simple_janitor_clean_split <- rsample::initial_split(
  mmmf_mixedVAR_simple_janitor_clean, 
  prop = 0.8, 
  strata = qf2020scenario
)

# Pre processing 
preprocessing_recipe <- 
  recipes::recipe(qf2020scenario ~ ., data = training(mmmf_mixedVAR_simple_janitor_clean_split)) %>%
  #convert categorical variables to factors
  recipes::step_string2factor(all_nominal()) %>%
  # combine low frequency factor levels
  recipes::step_other(all_nominal(), threshold = 0.01) %>%
  # remove no variance predictors which provide no predictive information 
  recipes::step_nzv(all_nominal()) %>%
  prep()

# Cross validate 
mmmf_mixedVAR_simple_janitor_clean_split_preproc_cv_folds <- 
  recipes::bake(
    preprocessing_recipe, 
    new_data = training(mmmf_mixedVAR_simple_janitor_clean_split)
  ) %>% rsample::vfold_cv(v = 5)

# lightgbm model specification
lightgbm_model<- 
  parsnip::boost_tree(
    min_n = tune(), #min_data_in_leaf
    tree_depth = tune(), #max_depth
    trees = tune(), #num_iterations
    learn_rate = tune(), #learning_rate
    loss_reduction = tune(), #min_gain_to_split
    mtry = tune()#, #feature_fraction
#    sample_size = tune() #bagging_fraction
  ) %>% set_engine("lightgbm") %>% set_mode("regression"
  ) %>% set_args(
    num_threads = 3,
    num_leaves = 131072, #
#    bagging_fraction = 0.1, # lets test 0.1 to 0.9 in steps of 0.1
#    early_stopping_round = 5,
    boosting = "goss",
#    bagging_freq = 5,
    tree_learner = "data",
    extra_trees = T,
    monotone_constraints_method = "advanced",
    feature_pre_filter = F,
    pre_partition = T
  )

# ///grid specification by dials package to fill in the model above
# grid specification
lightgbm_params <- 
  dials::parameters(
    min_n(),
    tree_depth(),
    trees(),
    learn_rate(),
    loss_reduction(),
    mtry()#,
#    sample_size = sample_prop(range = c(0.1,0.9),trans=NULL)
  ) %>% update(mtry = finalize(mtry(), mmmf_mixedVAR_simple_janitor_clean %>% select(-qf2020scenario))) #
#mtry and sample_size need to be provided with a range of how much to sample in (sample_size) and from how many predictor to select (mtry)
#mtry will use in this annotation any of predictors without the deselected(-)

# ///and the grid to look in 
# Experimental designs for computer experiments are used
# to construct parameter grids that try to cover the parameter space such that
# any portion of the space has an observed combination that is not too far from
# it.
lgbm_grid <- 
  dials::grid_max_entropy(
    lightgbm_params, 
    size = 7
  )
# To tune our model, we perform grid search over our xgboost_grid’s grid space
# to identify the hyperparameter values that have the lowest prediction error.

# Workflow setup
# /// (contains the work)
lgbm_wf <- 
  workflows::workflow() %>%
  add_model(lightgbm_model
  ) %>% 
  add_formula(qf2020scenario ~ .)

# /// so far little to no computation has been performed except for
# /// preprocessing calculations

# hyperparameter tuning
# //// this is where the machine starts to smoke!
set_dependency("boost_tree", eng = "lightgbm", "lightgbm")
set_dependency("boost_tree", eng = "lightgbm", "treesnip")

lgbm_tuned <- tune::tune_grid(
  object = lgbm_wf,
  resamples = mmmf_mixedVAR_simple_janitor_clean_split_preproc_cv_folds,
  grid = lgbm_grid,
  metrics = yardstick::metric_set(rmse, rsq, mae),
  control = tune::control_grid(verbose = F)
)

Created on 2022-03-21 by the reprex package (v2.0.1)

Session info 
sessioninfo::session_info()
#> - Session info ---------------------------------------------------------------
#>  setting  value
#>  version  R version 4.1.3 (2022-03-10)
#>  os       Windows 10 x64 (build 19044)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  German_Germany.1252
#>  ctype    German_Germany.1252
#>  tz       Europe/Prague
#>  date     2022-03-21
#>  pandoc   2.14.0.3 @ C:/Program Files/RStudio/bin/pandoc/ (via rmarkdown)
#> 
#> - Packages -------------------------------------------------------------------
#>  package      * version    date (UTC) lib source
#>  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.1.3)
#>  backports      1.4.1      2021-12-13 [1] CRAN (R 4.1.2)
#>  broom        * 0.7.12     2022-01-28 [1] CRAN (R 4.1.3)
#>  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.1.3)
#>  class          7.3-20     2022-01-16 [1] CRAN (R 4.1.3)
#>  cli            3.2.0      2022-02-14 [1] CRAN (R 4.1.3)
#>  codetools      0.2-18     2020-11-04 [1] CRAN (R 4.1.3)
#>  colorspace     2.0-3      2022-02-21 [1] CRAN (R 4.1.3)
#>  crayon         1.5.0      2022-02-14 [1] CRAN (R 4.1.3)
#>  data.table   * 1.14.2     2021-09-27 [1] CRAN (R 4.1.3)
#>  DBI            1.1.2      2021-12-20 [1] CRAN (R 4.1.3)
#>  dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.1.3)
#>  dials        * 0.1.0      2022-01-31 [1] CRAN (R 4.1.3)
#>  DiceDesign     1.9        2021-02-13 [1] CRAN (R 4.1.3)
#>  digest         0.6.29     2021-12-01 [1] CRAN (R 4.1.3)
#>  doParallel   * 1.0.17     2022-02-07 [1] CRAN (R 4.1.3)
#>  dplyr        * 1.0.8      2022-02-08 [1] CRAN (R 4.1.3)
#>  ellipsis       0.3.2      2021-04-29 [1] CRAN (R 4.1.3)
#>  evaluate       0.15       2022-02-18 [1] CRAN (R 4.1.3)
#>  fansi          1.0.2      2022-01-14 [1] CRAN (R 4.1.3)
#>  fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.1.3)
#>  forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.1.3)
#>  foreach      * 1.5.2      2022-02-02 [1] CRAN (R 4.1.3)
#>  fs             1.5.2      2021-12-08 [1] CRAN (R 4.1.3)
#>  furrr          0.2.3      2021-06-25 [1] CRAN (R 4.1.3)
#>  future         1.24.0     2022-02-19 [1] CRAN (R 4.1.3)
#>  future.apply   1.8.1      2021-08-10 [1] CRAN (R 4.1.3)
#>  generics       0.1.2      2022-01-31 [1] CRAN (R 4.1.3)
#>  ggplot2      * 3.3.5      2021-06-25 [1] CRAN (R 4.1.3)
#>  globals        0.14.0     2020-11-22 [1] CRAN (R 4.1.1)
#>  glue           1.6.2      2022-02-24 [1] CRAN (R 4.1.3)
#>  gower          1.0.0      2022-02-03 [1] CRAN (R 4.1.2)
#>  GPfit          1.0-8      2019-02-08 [1] CRAN (R 4.1.3)
#>  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.1.3)
#>  gtools       * 3.9.2      2021-06-06 [1] CRAN (R 4.1.3)
#>  hardhat        0.2.0      2022-01-24 [1] CRAN (R 4.1.3)
#>  haven          2.4.3      2021-08-04 [1] CRAN (R 4.1.3)
#>  highr          0.9        2021-04-16 [1] CRAN (R 4.1.3)
#>  hms            1.1.1      2021-09-26 [1] CRAN (R 4.1.3)
#>  htmltools      0.5.2      2021-08-25 [1] CRAN (R 4.1.3)
#>  httr           1.4.2      2020-07-20 [1] CRAN (R 4.1.3)
#>  infer        * 1.0.0      2021-08-13 [1] CRAN (R 4.1.3)
#>  ipred          0.9-12     2021-09-15 [1] CRAN (R 4.1.3)
#>  iterators    * 1.0.14     2022-02-05 [1] CRAN (R 4.1.3)
#>  janitor      * 2.1.0      2021-01-05 [1] CRAN (R 4.1.3)
#>  jsonlite       1.8.0      2022-02-22 [1] CRAN (R 4.1.3)
#>  knitr          1.37       2021-12-16 [1] CRAN (R 4.1.3)
#>  lattice        0.20-45    2021-09-22 [1] CRAN (R 4.1.3)
#>  lava           1.6.10     2021-09-02 [1] CRAN (R 4.1.3)
#>  lhs            1.1.4      2022-02-20 [1] CRAN (R 4.1.3)
#>  lifecycle      1.0.1      2021-09-24 [1] CRAN (R 4.1.3)
#>  lightgbm     * 3.3.2      2022-01-14 [1] CRAN (R 4.1.3)
#>  listenv        0.8.0      2019-12-05 [1] CRAN (R 4.1.3)
#>  lubridate      1.8.0      2021-10-07 [1] CRAN (R 4.1.3)
#>  magrittr       2.0.2      2022-01-26 [1] CRAN (R 4.1.3)
#>  MASS           7.3-55     2022-01-16 [1] CRAN (R 4.1.3)
#>  Matrix         1.4-0      2021-12-08 [1] CRAN (R 4.1.3)
#>  modeldata    * 0.1.1      2021-07-14 [1] CRAN (R 4.1.3)
#>  modelr         0.1.8      2020-05-19 [1] CRAN (R 4.1.3)
#>  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.1.3)
#>  nnet           7.3-17     2022-01-16 [1] CRAN (R 4.1.3)
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#>  parallelly     1.30.0     2021-12-17 [1] CRAN (R 4.1.2)
#>  parsnip      * 0.2.1      2022-03-17 [1] CRAN (R 4.1.3)
#>  pillar         1.7.0      2022-02-01 [1] CRAN (R 4.1.3)
#>  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.1.3)
#>  plyr         * 1.8.6      2020-03-03 [1] CRAN (R 4.1.3)
#>  pROC           1.18.0     2021-09-03 [1] CRAN (R 4.1.3)
#>  prodlim        2019.11.13 2019-11-17 [1] CRAN (R 4.1.3)
#>  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.1.3)
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#>  treesnip     * 0.1.0.9000 2022-03-21 [1] Github (curso-r/treesnip@3e4c220)
#>  tune         * 0.2.0      2022-03-19 [1] CRAN (R 4.1.3)
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#> 
#>  [1] C:/Users/Animosaeure/Documents/R/R-4.1.3/library
#> 
#> ------------------------------------------------------------------------------

and this works, too (tidymodels/tune#476; example from tidymodels/tune#460):

library(tidymodels)
data(two_class_dat)

data_train <- two_class_dat[-(1:10), ]
data_test  <- two_class_dat[  1:10 , ]
folds <- vfold_cv(data_train, v = 3, strata = A)
folds
#> #  3-fold cross-validation using stratification 
#> # A tibble: 3 x 2
#>   splits            id   
#>   <list>            <chr>
#> 1 <split [520/261]> Fold1
#> 2 <split [521/260]> Fold2
#> 3 <split [521/260]> Fold3

bt_cls_spec <- 
  boost_tree(trees = 15) %>% 
  set_mode("regression") %>% 
  set_engine("lightgbm")
#> Error in `check_spec_mode_engine_val()`:
#> ! Engine 'lightgbm' is not supported for `boost_tree()`. See `show_engines('boost_tree')`.


bt_cls_spec %>% 
  tune_grid(A ~ .,
            resamples = folds,
            grid = 5)
#> Error in tune_grid(., A ~ ., resamples = folds, grid = 5): Objekt 'bt_cls_spec' nicht gefunden

Created on 2022-03-21 by the reprex package (v2.0.1)

Session info 
sessioninfo::session_info()
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#>  language (EN)
#>  collate  German_Germany.1252
#>  ctype    German_Germany.1252
#>  tz       Europe/Prague
#>  date     2022-03-21
#>  pandoc   2.14.0.3 @ C:/Program Files/RStudio/bin/pandoc/ (via rmarkdown)
#> 
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#> 
#>  [1] C:/Users/Animosaeure/Documents/R/R-4.1.3/library
#> 
#> ------------------------------------------------------------------------------
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