Parallelization on resampling within a stacked learner (ensemble/stack of classification learners) doesn't work

问题

The below code works fine, however, I am interested to run it in parallel. I have tried different plans within future and future.apply but couldn't managed. Any help appreciated. I am running on windows OS, 8 cores.

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

^{Created on 2020-12-15 by the reprex package (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                  
#> 
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#> 
#> [1] C:/Users/mshey/Anaconda3/envs/rstudio/lib/R/library

回答1:

Looks fine to me. Note that multicore mode is not available on Windows and falls back to sequential. Might this be the culprit here?

PS: Next time you face a parallelization/runtime issue, benchmarking the runtime might help ;)

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

^{Created on 2020-12-20 by the reprex package (v0.3.0)}

Session info

devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                                      
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#>  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                                 
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#>  xgboost         1.2.0.1    2020-09-02 [1] CRAN (R 4.0.3)                  
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#> 
#> [1] /Users/pjs/Library/R/4.0/library
#> [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

来源：https://stackoverflow.com/questions/65291768/parallelization-on-resampling-within-a-stacked-learner-ensemble-stack-of-classi

标签

parallel-processing

resampling

mlr

mlr3

r-future