Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Tuning k-means fails when running in parallel #182

Open
KazBarker opened this issue Apr 26, 2024 · 0 comments
Open

Tuning k-means fails when running in parallel #182

KazBarker opened this issue Apr 26, 2024 · 0 comments

Comments

@KazBarker
Copy link

The problem

I'm having trouble with tuning k-means clustering in parallel. Everything works as expected when running sequentially, but in parallel the following warning is returned:

Warning: All models failed. See the `.notes` column.

No error is thrown so the code continues running, but the returned data frame is empty.

Reproducible example

library(doParallel)
#> Loading required package: foreach
#> Loading required package: iterators
#> Loading required package: parallel
library(tidyverse)
#> Warning: package 'tidyverse' was built under R version 4.3.3
#> Warning: package 'ggplot2' was built under R version 4.3.3
#> Warning: package 'tidyr' was built under R version 4.3.3
#> Warning: package 'readr' was built under R version 4.3.3
#> Warning: package 'dplyr' was built under R version 4.3.3
#> Warning: package 'stringr' was built under R version 4.3.3
#> Warning: package 'lubridate' was built under R version 4.3.3
library(tidymodels)
#> Warning: package 'dials' was built under R version 4.3.3
#> Warning: package 'scales' was built under R version 4.3.3
#> Warning: package 'parsnip' was built under R version 4.3.3
#> Warning: package 'recipes' was built under R version 4.3.3
#> Warning: package 'workflows' was built under R version 4.3.3
library(tidyclust)
#> Warning: package 'tidyclust' was built under R version 4.3.3
#> 
#> Attaching package: 'tidyclust'
#> The following objects are masked from 'package:parsnip':
#> 
#>     knit_engine_docs, list_md_problems
library(tune)

my_parallel <- parallel::makePSOCKcluster(parallel::detectCores(logical = FALSE))
doParallel::registerDoParallel(my_parallel)

set.seed(123)

penguin_data <- modeldata::penguins %>% drop_na()
data_form <- ~bill_length_mm + bill_depth_mm

penguin_recipe <- recipe(data_form, data = penguin_data) %>% 
  step_normalize(all_predictors())

kmeans_spec <- k_means(num_clusters = tune(),
                       engine = 'stats',
                       mode = 'partition')

kmeans_wkfl <- workflow() %>% 
  add_recipe(penguin_recipe) %>% 
  add_model(kmeans_spec)

cl_resamples <- vfold_cv(penguin_data, v = 2)
cl_grid <- tibble(num_clusters = 1:10)

tuning_result <- tune_cluster(kmeans_wkfl,
                              resamples = cl_resamples,
                              grid = cl_grid,
                              metrics = cluster_metric_set(sse_within_total))
#> Warning: All models failed. See the `.notes` column.
tuning_result$.notes
#> [[1]]
#> NULL
#> 
#> [[2]]
#> NULL

Created on 2024-04-26 with reprex v2.1.0

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.2 (2023-10-31 ucrt)
#>  os       Windows 11 x64 (build 22631)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       America/New_York
#>  date     2024-04-26
#>  pandoc   3.1.7 @ C:/Users/Kbark/AppData/Local/Pandoc/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package      * version    date (UTC) lib source
#>  backports      1.4.1      2021-12-13 [1] CRAN (R 4.3.0)
#>  broom        * 1.0.5      2023-06-09 [1] CRAN (R 4.3.1)
#>  class          7.3-22     2023-05-03 [2] CRAN (R 4.3.2)
#>  cli            3.6.2      2023-12-11 [1] CRAN (R 4.3.3)
#>  codetools      0.2-19     2023-02-01 [2] CRAN (R 4.3.2)
#>  colorspace     2.1-0      2023-01-23 [1] CRAN (R 4.3.1)
#>  data.table     1.15.2     2024-02-29 [1] CRAN (R 4.3.3)
#>  dials        * 1.2.1      2024-02-22 [1] CRAN (R 4.3.3)
#>  DiceDesign     1.10       2023-12-07 [1] CRAN (R 4.3.2)
#>  digest         0.6.35     2024-03-11 [1] CRAN (R 4.3.3)
#>  doParallel   * 1.0.17     2022-02-07 [1] CRAN (R 4.3.2)
#>  dplyr        * 1.1.4      2023-11-17 [1] CRAN (R 4.3.3)
#>  evaluate       0.23       2023-11-01 [1] CRAN (R 4.3.3)
#>  fansi          1.0.6      2023-12-08 [1] CRAN (R 4.3.3)
#>  fastmap        1.1.1      2023-02-24 [1] CRAN (R 4.3.1)
#>  forcats      * 1.0.0      2023-01-29 [1] CRAN (R 4.3.1)
#>  foreach      * 1.5.2      2022-02-02 [1] CRAN (R 4.3.2)
#>  fs             1.6.3      2023-07-20 [1] CRAN (R 4.3.1)
#>  furrr          0.3.1      2022-08-15 [1] CRAN (R 4.3.2)
#>  future         1.33.1     2023-12-22 [1] CRAN (R 4.3.2)
#>  future.apply   1.11.1     2023-12-21 [1] CRAN (R 4.3.2)
#>  generics       0.1.3      2022-07-05 [1] CRAN (R 4.3.1)
#>  ggplot2      * 3.5.0      2024-02-23 [1] CRAN (R 4.3.3)
#>  globals        0.16.3     2024-03-08 [1] CRAN (R 4.3.3)
#>  glue           1.7.0      2024-01-09 [1] CRAN (R 4.3.3)
#>  gower          1.0.1      2022-12-22 [1] CRAN (R 4.3.1)
#>  GPfit          1.0-8      2019-02-08 [1] CRAN (R 4.3.2)
#>  gtable         0.3.4      2023-08-21 [1] CRAN (R 4.3.1)
#>  hardhat        1.3.1      2024-02-02 [1] CRAN (R 4.3.2)
#>  hms            1.1.3      2023-03-21 [1] CRAN (R 4.3.1)
#>  htmltools      0.5.7      2023-11-03 [1] CRAN (R 4.3.3)
#>  infer        * 1.0.6      2024-01-31 [1] CRAN (R 4.3.2)
#>  ipred          0.9-14     2023-03-09 [1] CRAN (R 4.3.2)
#>  iterators    * 1.0.14     2022-02-05 [1] CRAN (R 4.3.2)
#>  knitr          1.45       2023-10-30 [1] CRAN (R 4.3.3)
#>  lattice        0.21-9     2023-10-01 [2] CRAN (R 4.3.2)
#>  lava           1.8.0      2024-03-05 [1] CRAN (R 4.3.3)
#>  lhs            1.1.6      2022-12-17 [1] CRAN (R 4.3.2)
#>  lifecycle      1.0.4      2023-11-07 [1] CRAN (R 4.3.3)
#>  listenv        0.9.1      2024-01-29 [1] CRAN (R 4.3.2)
#>  lubridate    * 1.9.3      2023-09-27 [1] CRAN (R 4.3.3)
#>  magrittr       2.0.3      2022-03-30 [1] CRAN (R 4.3.1)
#>  MASS           7.3-60     2023-05-04 [2] CRAN (R 4.3.2)
#>  Matrix         1.6-5      2024-01-11 [1] CRAN (R 4.3.3)
#>  modeldata    * 1.3.0      2024-01-21 [1] CRAN (R 4.3.2)
#>  modelenv       0.1.1      2023-03-08 [1] CRAN (R 4.3.2)
#>  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.3.1)
#>  nnet           7.3-19     2023-05-03 [2] CRAN (R 4.3.2)
#>  parallelly     1.37.1     2024-02-29 [1] CRAN (R 4.3.3)
#>  parsnip      * 1.2.0      2024-02-16 [1] CRAN (R 4.3.3)
#>  pillar         1.9.0      2023-03-22 [1] CRAN (R 4.3.1)
#>  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.3.1)
#>  prodlim        2023.08.28 2023-08-28 [1] CRAN (R 4.3.2)
#>  purrr        * 1.0.2      2023-08-10 [1] CRAN (R 4.3.1)
#>  R.cache        0.16.0     2022-07-21 [1] CRAN (R 4.3.3)
#>  R.methodsS3    1.8.2      2022-06-13 [1] CRAN (R 4.3.1)
#>  R.oo           1.26.0     2024-01-24 [1] CRAN (R 4.3.2)
#>  R.utils        2.12.3     2023-11-18 [1] CRAN (R 4.3.3)
#>  R6             2.5.1      2021-08-19 [1] CRAN (R 4.3.1)
#>  Rcpp           1.0.12     2024-01-09 [1] CRAN (R 4.3.3)
#>  readr        * 2.1.5      2024-01-10 [1] CRAN (R 4.3.3)
#>  recipes      * 1.0.10     2024-02-18 [1] CRAN (R 4.3.3)
#>  reprex         2.1.0      2024-01-11 [1] CRAN (R 4.3.3)
#>  rlang          1.1.3      2024-01-10 [1] CRAN (R 4.3.3)
#>  rmarkdown      2.26       2024-03-05 [1] CRAN (R 4.3.3)
#>  rpart          4.1.21     2023-10-09 [2] CRAN (R 4.3.2)
#>  rsample      * 1.2.0      2023-08-23 [1] CRAN (R 4.3.2)
#>  rstudioapi     0.15.0     2023-07-07 [1] CRAN (R 4.3.1)
#>  scales       * 1.3.0      2023-11-28 [1] CRAN (R 4.3.3)
#>  sessioninfo    1.2.2      2021-12-06 [1] CRAN (R 4.3.3)
#>  stringi        1.8.3      2023-12-11 [1] CRAN (R 4.3.2)
#>  stringr      * 1.5.1      2023-11-14 [1] CRAN (R 4.3.3)
#>  styler         1.10.2     2023-08-29 [1] CRAN (R 4.3.3)
#>  survival       3.5-8      2024-02-14 [1] CRAN (R 4.3.3)
#>  tibble       * 3.2.1      2023-03-20 [1] CRAN (R 4.3.1)
#>  tidyclust    * 0.2.1      2024-02-29 [1] CRAN (R 4.3.3)
#>  tidymodels   * 1.1.1      2023-08-24 [1] CRAN (R 4.3.2)
#>  tidyr        * 1.3.1      2024-01-24 [1] CRAN (R 4.3.3)
#>  tidyselect     1.2.1      2024-03-11 [1] CRAN (R 4.3.3)
#>  tidyverse    * 2.0.0      2023-02-22 [1] CRAN (R 4.3.3)
#>  timechange     0.3.0      2024-01-18 [1] CRAN (R 4.3.3)
#>  timeDate       4032.109   2023-12-14 [1] CRAN (R 4.3.2)
#>  tune         * 1.2.1      2024-04-18 [1] CRAN (R 4.3.2)
#>  tzdb           0.4.0      2023-05-12 [1] CRAN (R 4.3.1)
#>  utf8           1.2.4      2023-10-22 [1] CRAN (R 4.3.3)
#>  vctrs          0.6.5      2023-12-01 [1] CRAN (R 4.3.3)
#>  withr          3.0.0      2024-01-16 [1] CRAN (R 4.3.3)
#>  workflows    * 1.1.4      2024-02-19 [1] CRAN (R 4.3.3)
#>  workflowsets * 1.0.1      2023-04-06 [1] CRAN (R 4.3.2)
#>  xfun           0.42       2024-02-08 [1] CRAN (R 4.3.3)
#>  yaml           2.3.8      2023-12-11 [1] CRAN (R 4.3.2)
#>  yardstick    * 1.3.0      2024-01-19 [1] CRAN (R 4.3.2)
#> 
#>  [1] C:/Users/Kbark/AppData/Local/R/win-library/4.3
#>  [2] C:/Program Files/R/R-4.3.2/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
1 participant
@KazBarker