parallel-processing - 在堆叠学习器（分类学习器的集合/堆栈）中重新采样的并行化不起作用

Question

下面的代码工作正常，但是，我有兴趣并行运行它。我在内部尝试了不同的计划future，future.apply但无法管理。任何帮助表示赞赏。我在 Windows 操作系统上运行，8 个内核。

library(mlr3verse)
library(future.apply)
#> Warning: package 'future.apply' was built under R version 3.6.3
#> Loading required package: future
#> Warning: package 'future' was built under R version 3.6.3
library(future)
future::plan(multicore)

tsk_clf = tsk("sonar")
tsk_clf$col_roles$stratum = tsk_clf$target_names #stratification


lda  = lrn("classif.lda", predict_type = "response")
svm =  lrn("classif.svm", type = "C-classification", kernel= "radial",predict_type = "response")
xgb = lrn("classif.xgboost", predict_type = "response")
ranger_lrn = lrn("classif.ranger", predict_type = "response",importance ="permutation")

level_1 =
  gunion(list(
    PipeOpLearnerCV$new(lda, id = "lda_cv_l1"),
    PipeOpLearnerCV$new(svm, id = "svm_cv_l1"),
    PipeOpLearnerCV$new(xgb, id = "xgb_cv_l1")
  ))

level_2 = level_1 %>>%
  PipeOpFeatureUnion$new(3, id = "u2") %>>%
  PipeOpLearner$new(ranger_lrn,
                    id = "ranger_l2")
lrn = GraphLearner$new(level_2)
lrn$
  train(tsk_clf)$
  predict(tsk_clf)$
  score()
#> INFO  [17:04:06.984] Applying learner 'classif.lda' on task 'sonar' (iter 3/3) 
#> INFO  [17:04:07.052] Applying learner 'classif.lda' on task 'sonar' (iter 1/3) 
#> INFO  [17:04:07.097] Applying learner 'classif.lda' on task 'sonar' (iter 2/3) 
#> INFO  [17:04:07.340] Applying learner 'classif.svm' on task 'sonar' (iter 1/3) 
#> INFO  [17:04:07.382] Applying learner 'classif.svm' on task 'sonar' (iter 2/3) 
#> INFO  [17:04:07.430] Applying learner 'classif.svm' on task 'sonar' (iter 3/3) 
#> INFO  [17:04:08.627] Applying learner 'classif.xgboost' on task 'sonar' (iter 3/3) 
#> INFO  [17:04:08.672] Applying learner 'classif.xgboost' on task 'sonar' (iter 2/3) 
#> INFO  [17:04:08.715] Applying learner 'classif.xgboost' on task 'sonar' (iter 1/3)
#> classif.ce 
#> 0.01923077

^{由reprex 包于 2020-12-15 创建(v0.3.0)}

devtools::session_info()
#> - Session info ----------------------------------------------------------
#>  setting  value                       
#>  version  R version 3.6.1 (2019-07-05)
#>  os       Windows 10 x64              
#>  system   x86_64, mingw32             
#>  ui       RTerm                       
#>  language (EN)                        
#>  collate  English_United States.1252  
#>  ctype    English_United States.1252  
#>  tz       Europe/Berlin               
#>  date     2020-12-15                  
#> 
#> - Packages --------------------------------------------------------------
#>  package       * version date       lib source        
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#>  backports       1.1.4   2019-04-10 [1] CRAN (R 3.6.0)
#>  bbotk           0.2.0   2020-07-24 [1] CRAN (R 3.6.3)
#>  callr           3.5.1   2020-10-13 [1] CRAN (R 3.6.3)
#>  checkmate       2.0.0   2020-02-06 [1] CRAN (R 3.6.3)
#>  class           7.3-17  2020-04-26 [1] CRAN (R 3.6.3)
#>  cli             2.1.0   2020-10-12 [1] CRAN (R 3.6.3)
#>  codetools       0.2-16  2018-12-24 [1] CRAN (R 3.6.0)
#>  colorspace      1.4-1   2019-03-18 [1] CRAN (R 3.6.3)
#>  crayon          1.3.4   2017-09-16 [1] CRAN (R 3.6.0)
#>  data.table      1.13.0  2020-07-24 [1] CRAN (R 3.6.3)
#>  desc            1.2.0   2018-05-01 [1] CRAN (R 3.6.3)
#>  devtools        2.3.2   2020-09-18 [1] CRAN (R 3.6.3)
#>  digest          0.6.18  2018-10-10 [1] CRAN (R 3.6.0)
#>  dplyr           1.0.2   2020-08-18 [1] CRAN (R 3.6.3)
#>  e1071           1.7-3   2019-11-26 [1] CRAN (R 3.6.3)
#>  ellipsis        0.3.1   2020-05-15 [1] CRAN (R 3.6.3)
#>  evaluate        0.13    2019-02-12 [1] CRAN (R 3.6.0)
#>  fansi           0.4.0   2018-10-05 [1] CRAN (R 3.6.0)
#>  fs              1.5.0   2020-07-31 [1] CRAN (R 3.6.3)
#>  future        * 1.18.0  2020-07-09 [1] CRAN (R 3.6.3)
#>  future.apply  * 1.6.0   2020-07-01 [1] CRAN (R 3.6.3)
#>  generics        0.1.0   2020-10-31 [1] CRAN (R 3.6.3)
#>  ggplot2         3.3.2   2020-06-19 [1] CRAN (R 3.6.3)
#>  globals         0.12.5  2019-12-07 [1] CRAN (R 3.6.1)
#>  glue            1.4.2   2020-08-27 [1] CRAN (R 3.6.3)
#>  gtable          0.3.0   2019-03-25 [1] CRAN (R 3.6.3)
#>  highr           0.8     2019-03-20 [1] CRAN (R 3.6.0)
#>  htmltools       0.3.6   2017-04-28 [1] CRAN (R 3.6.0)
#>  knitr           1.22    2019-03-08 [1] CRAN (R 3.6.0)
#>  lattice         0.20-41 2020-04-02 [1] CRAN (R 3.6.3)
#>  lgr             0.3.4   2020-03-20 [1] CRAN (R 3.6.3)
#>  lifecycle       0.2.0   2020-03-06 [1] CRAN (R 3.6.3)
#>  listenv         0.8.0   2019-12-05 [1] CRAN (R 3.6.3)
#>  magrittr        1.5     2014-11-22 [1] CRAN (R 3.6.0)
#>  MASS            7.3-52  2020-08-18 [1] CRAN (R 3.6.3)
#>  Matrix          1.2-18  2019-11-27 [1] CRAN (R 3.6.3)
#>  memoise         1.1.0   2017-04-21 [1] CRAN (R 3.6.3)
#>  mlr3          * 0.5.0   2020-08-07 [1] CRAN (R 3.6.3)
#>  mlr3filters   * 0.3.0   2020-07-18 [1] CRAN (R 3.6.3)
#>  mlr3learners  * 0.3.0   2020-08-29 [1] CRAN (R 3.6.3)
#>  mlr3measures    0.2.0   2020-06-27 [1] CRAN (R 3.6.3)
#>  mlr3misc        0.5.0   2020-08-13 [1] CRAN (R 3.6.3)
#>  mlr3pipelines * 0.2.1   2020-08-18 [1] CRAN (R 3.6.3)
#>  mlr3tuning    * 0.2.0   2020-07-28 [1] CRAN (R 3.6.3)
#>  mlr3verse     * 0.1.3   2020-07-06 [1] CRAN (R 3.6.3)
#>  mlr3viz       * 0.4.0   2020-10-05 [1] CRAN (R 3.6.3)
#>  munsell         0.5.0   2018-06-12 [1] CRAN (R 3.6.3)
#>  paradox       * 0.4.0   2020-07-21 [1] CRAN (R 3.6.3)
#>  pillar          1.4.6   2020-07-10 [1] CRAN (R 3.6.3)
#>  pkgbuild        1.1.0   2020-07-13 [1] CRAN (R 3.6.3)
#>  pkgconfig       2.0.2   2018-08-16 [1] CRAN (R 3.6.0)
#>  pkgload         1.1.0   2020-05-29 [1] CRAN (R 3.6.3)
#>  prettyunits     1.0.2   2015-07-13 [1] CRAN (R 3.6.0)
#>  processx        3.4.4   2020-09-03 [1] CRAN (R 3.6.3)
#>  ps              1.3.4   2020-08-11 [1] CRAN (R 3.6.3)
#>  purrr           0.3.4   2020-04-17 [1] CRAN (R 3.6.3)
#>  R6              2.4.1   2019-11-12 [1] CRAN (R 3.6.3)
#>  ranger          0.12.1  2020-01-10 [1] CRAN (R 3.6.3)
#>  Rcpp            1.0.1   2019-03-17 [1] CRAN (R 3.6.0)
#>  remotes         2.2.0   2020-07-21 [1] CRAN (R 3.6.3)
#>  rlang           0.4.7   2020-07-09 [1] CRAN (R 3.6.3)
#>  rmarkdown       1.12    2019-03-14 [1] CRAN (R 3.6.0)
#>  rprojroot       1.3-2   2018-01-03 [1] CRAN (R 3.6.0)
#>  scales          1.1.1   2020-05-11 [1] CRAN (R 3.6.3)
#>  sessioninfo     1.1.1   2018-11-05 [1] CRAN (R 3.6.3)
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#>  stringr         1.4.0   2019-02-10 [1] CRAN (R 3.6.0)
#>  testthat        2.3.2   2020-03-02 [1] CRAN (R 3.6.3)
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#>  tidyselect      1.1.0   2020-05-11 [1] CRAN (R 3.6.3)
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#>  uuid            0.1-4   2020-02-26 [1] CRAN (R 3.6.3)
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#>  withr           2.3.0   2020-09-22 [1] CRAN (R 3.6.3)
#>  xfun            0.6     2019-04-02 [1] CRAN (R 3.6.0)
#>  xgboost         1.2.0.1 2020-09-02 [1] CRAN (R 3.6.3)
#>  yaml            2.2.0   2018-07-25 [1] CRAN (R 3.6.0)
#> 
#> [1] C:/Users/mshey/Anaconda3/envs/rstudio/lib/R/library

score 1 · Accepted Answer

在我看来很好。请注意，该multicore模式在 Windows 上不可用，并且回退到sequential. 这可能是这里的罪魁祸首吗？

PS：下次遇到并行化/运行时问题时，对运行时进行基准测试可能会有所帮助；）

library(mlr3verse)
#> Loading required package: mlr3
#> Loading required package: mlr3filters
#> Loading required package: mlr3learners
#> Loading required package: mlr3pipelines
#> Loading required package: mlr3tuning
#> Loading required package: mlr3viz
#> Loading required package: paradox
library(future.apply)
#> Loading required package: future
library(future)
library(lgr)

lgr::get_logger("mlr3")$set_threshold("fatal")

tsk_clf <- tsk("sonar")
tsk_clf$col_roles$stratum <- tsk_clf$target_names # stratification


lda <- lrn("classif.lda", predict_type = "response")
svm <- lrn("classif.svm", type = "C-classification", kernel = "radial", predict_type = "response")
xgb <- lrn("classif.xgboost", predict_type = "response")
ranger_lrn <- lrn("classif.ranger", predict_type = "response", importance = "permutation")

level_1 <-
  gunion(list(
    PipeOpLearnerCV$new(lda, id = "lda_cv_l1"),
    PipeOpLearnerCV$new(svm, id = "svm_cv_l1"),
    PipeOpLearnerCV$new(xgb, id = "xgb_cv_l1")
  ))

level_2 <- level_1 %>>%
  PipeOpFeatureUnion$new(3, id = "u2") %>>%
  PipeOpLearner$new(ranger_lrn,
    id = "ranger_l2"
  )
lrn <- GraphLearner$new(level_2)

# parallel
plan(multicore)
time <- Sys.time()
lrn$
  train(tsk_clf)$
  predict(tsk_clf)$
  score()
#> classif.ce 
#> 0.01923077
Sys.time() - time
#> Time difference of 2.994049 secs

# sequential
plan(sequential)
lrn$
  train(tsk_clf)$
  predict(tsk_clf)$
  score()
#> classif.ce 
#> 0.01923077
Sys.time() - time
#> Time difference of 4.276779 secs

^{由reprex 包于 2020-12-20 创建(v0.3.0)}

会话信息

devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                                      
#>  version  R version 4.0.3 Patched (2020-12-10 r79607)
#>  os       macOS Big Sur 10.16                        
#>  system   x86_64, darwin17.0                         
#>  ui       X11                                        
#>  language (EN)                                       
#>  collate  en_US.UTF-8                                
#>  ctype    en_US.UTF-8                                
#>  tz       Europe/Berlin                              
#>  date     2020-12-20                                 
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package       * version    date       lib source                          
#>  assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.0.3)                  
#>  backports       1.2.1      2020-12-09 [1] CRAN (R 4.0.3)                  
#>  bbotk           0.2.2      2020-12-20 [1] Github (mlr-org/bbotk@5acf598)  
#>  callr           3.5.1      2020-10-13 [1] CRAN (R 4.0.3)                  
#>  checkmate       2.0.0      2020-02-06 [1] CRAN (R 4.0.3)                  
#>  class           7.3-17     2020-04-26 [2] CRAN (R 4.0.3)                  
#>  cli             2.2.0      2020-11-20 [1] CRAN (R 4.0.3)                  
#>  codetools       0.2-18     2020-11-04 [2] CRAN (R 4.0.3)                  
#>  colorspace      2.0-0      2020-11-11 [1] CRAN (R 4.0.3)                  
#>  crayon          1.3.4      2017-09-16 [1] CRAN (R 4.0.3)                  
#>  data.table      1.13.4     2020-12-08 [1] CRAN (R 4.0.3)                  
#>  desc            1.2.0      2018-05-01 [1] CRAN (R 4.0.3)                  
#>  devtools        2.3.2      2020-09-18 [1] CRAN (R 4.0.3)                  
#>  digest          0.6.27     2020-10-24 [1] CRAN (R 4.0.3)                  
#>  dplyr           1.0.2      2020-08-18 [1] CRAN (R 4.0.3)                  
#>  e1071           1.7-4      2020-10-14 [1] CRAN (R 4.0.3)                  
#>  ellipsis        0.3.1      2020-05-15 [1] CRAN (R 4.0.3)                  
#>  evaluate        0.14       2019-05-28 [1] CRAN (R 4.0.3)                  
#>  fansi           0.4.1      2020-01-08 [1] CRAN (R 4.0.3)                  
#>  fs              1.5.0      2020-07-31 [1] CRAN (R 4.0.3)                  
#>  future        * 1.21.0     2020-12-10 [1] CRAN (R 4.0.3)                  
#>  future.apply  * 1.6.0      2020-07-01 [1] CRAN (R 4.0.3)                  
#>  generics        0.1.0      2020-10-31 [1] CRAN (R 4.0.3)                  
#>  ggplot2         3.3.2      2020-06-19 [1] CRAN (R 4.0.3)                  
#>  globals         0.14.0     2020-11-22 [1] CRAN (R 4.0.3)                  
#>  glue            1.4.2      2020-08-27 [1] CRAN (R 4.0.3)                  
#>  gtable          0.3.0      2019-03-25 [1] CRAN (R 4.0.3)                  
#>  highr           0.8        2019-03-20 [1] CRAN (R 4.0.3)                  
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#>  magrittr        2.0.1      2020-11-17 [1] CRAN (R 4.0.3)                  
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#>  Matrix          1.2-18     2019-11-27 [2] CRAN (R 4.0.3)                  
#>  memoise         1.1.0      2017-04-21 [1] CRAN (R 4.0.3)                  
#>  mlr3          * 0.9.0-9000 2020-12-20 [1] Github (mlr-org/mlr3@f9ac360)   
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#> 
#> [1] /Users/pjs/Library/R/4.0/library
#> [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

parallel-processing - 在堆叠学习器（分类学习器的集合/堆栈）中重新采样的并行化不起作用

1 回答 1

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