add example using the with foreach::foreach() in tune_bayes #157
Comments
brunocarlin
changed the title
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Can you be more specific? That function is used internally. Do you mean how to use parallel processing? |
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Yes for example I use And it works fine on Linux, on windows or using makeCluster the tune_bayes fails |
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Forking doesn't work outside of linux and macOS. For Windows, you could use library(doParallel)
cl <- makePSOCKcluster(parallel::detectCores(logical = FALSE))
registerDoParallel(cl)It is very much worth looking at the |
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Really cool package(doParallel), I will try it on windows tomorrow, if it works I will close the thread, if it doesn't I will try to make some reprex, sorry for the inconvenience it is so cool using this package (tune and tidymodels in general) |
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It seems Windowns fails somehow to use multiple workers library(tidymodels)
#> Registered S3 method overwritten by 'xts':
#> method from
#> as.zoo.xts zoo
#> -- Attaching packages ---------------------------------------------------- tidymodels 0.0.3 --
#> v broom 0.5.3 v purrr 0.3.3
#> v dials 0.0.4 v recipes 0.1.9
#> v dplyr 0.8.3 v rsample 0.0.5
#> v ggplot2 3.2.1 v tibble 2.1.3
#> v infer 0.5.1 v yardstick 0.0.4
#> v parsnip 0.0.5
#> -- Conflicts ------------------------------------------------------- tidymodels_conflicts() --
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x ggplot2::margin() masks dials::margin()
#> x recipes::step() masks stats::step()
#> x recipes::yj_trans() masks scales::yj_trans()
library(tune)
library(kernlab)
#>
#> Attaching package: 'kernlab'
#> The following object is masked from 'package:purrr':
#>
#> cross
#> The following object is masked from 'package:ggplot2':
#>
#> alpha
#> The following object is masked from 'package:scales':
#>
#> alpha
# Load data
load(url("http://bit.ly/seg-data"))
segmentationData <-
segmentationData %>%
select(-Case, -Cell, -contains("Centroid"))
set.seed(8567)
tr_te_split <- initial_split(segmentationData)
seg_train <- training(tr_te_split)
seg_test <- testing(tr_te_split)
set.seed(5121)
folds <- vfold_cv(seg_train, repeats = 2)
seg_pre_proc <-
recipe(Class ~ ., data = seg_train) %>%
step_YeoJohnson(all_predictors()) %>%
step_normalize(all_predictors()) %>%
step_pca(all_predictors(), num_comp = tune()) %>%
step_downsample(Class)
svm_mod <-
svm_rbf(mode = "classification", cost = tune(), rbf_sigma = tune()) %>%
set_engine("kernlab")
library(workflows)
svm_wflow <-
workflow() %>%
add_model(svm_mod) %>%
add_recipe(seg_pre_proc)
svm_set <- parameters(svm_wflow)
svm_set <- svm_set %>% update(num_comp = num_comp(c(1, 20)))
set.seed(1291)
library(doParallel)
#> Loading required package: foreach
#>
#> Attaching package: 'foreach'
#> The following objects are masked from 'package:purrr':
#>
#> accumulate, when
#> Loading required package: iterators
#> Loading required package: parallel
cl <- makePSOCKcluster(parallel::detectCores(logical = FALSE))
registerDoParallel(cl)
foreach::getDoParWorkers()
#> [1] 2
search_res <-
tune_bayes(
svm_wflow,
resamples = folds,
# To use non-default parameter ranges
param_info = svm_set,
# Generate five at semi-random to start
initial = 3,
iter = 30,
# How to measure performance?
metrics = metric_set(roc_auc),
control = control_bayes(no_improve = 20, verbose = TRUE)
)
#>
#> > Generating a set of 3 initial parameter results
#> Warning: All models failed in tune_grid(). See the `.notes` column.
#> v Initialization complete
#>
#> Error: All of the models failed.Created on 2020-01-25 by the reprex package (v0.3.0) Session infodevtools::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 3.6.2 (2019-12-12)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz America/Sao_Paulo
#> date 2020-01-25
#>
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#>
#> [1] C:/Users/bcarl2/OneDrive - Monsanto/Migrated from My PC/Documents/R/win-library/3.6
#> [2] C:/Users/bcarl2/OneDrive - Monsanto/Migrated from My PC/Documents/R/R-3.6.2/library |
This was referenced Jan 29, 2020
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I'm guessing the problem is that the examples in the workshop, similar to the one above, where based on the process forking used by {doMC}, and that we might have to be more explicit about how to expose pkgs and objects to the runners for parallel backends which don't do process forking. ({doMC}, only available for unix-compatible systems such as macOS and Linux uses process forking, which (more or less) makes the entire environment (vars, pkgs) available to all the parallel processes.) I don't know enough about the internals of {tune} or parallel processing on windows to know for sure whether we can just fix this with a bunch of |
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Solved it using:
library(tidymodels)
#> -- Attaching packages ----------------------------------------------------------------------------- tidymodels 0.0.3 --
#> v broom 0.5.4 v purrr 0.3.3
#> v dials 0.0.4 v recipes 0.1.9
#> v dplyr 0.8.3 v rsample 0.0.5
#> v ggplot2 3.2.1 v tibble 2.1.3
#> v infer 0.5.1 v yardstick 0.0.5
#> v parsnip 0.0.5
#> -- Conflicts -------------------------------------------------------------------------------- tidymodels_conflicts() --
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x ggplot2::margin() masks dials::margin()
#> x recipes::step() masks stats::step()
#> x recipes::yj_trans() masks scales::yj_trans()
library(tune)
library(kernlab)
#>
#> Attaching package: 'kernlab'
#> The following object is masked from 'package:purrr':
#>
#> cross
#> The following object is masked from 'package:ggplot2':
#>
#> alpha
#> The following object is masked from 'package:scales':
#>
#> alpha
load(url("http://bit.ly/seg-data"))
segmentationData <-
segmentationData %>%
select(-Case, -Cell, -contains("Centroid"))
set.seed(8567)
tr_te_split <- initial_split(segmentationData)
seg_train <- training(tr_te_split)
seg_test <- testing(tr_te_split)
set.seed(5121)
folds <- vfold_cv(seg_train, repeats = 2)
seg_pre_proc <-
recipe(Class ~ ., data = seg_train) %>%
step_YeoJohnson(all_predictors()) %>%
step_normalize(all_predictors()) %>%
step_pca(all_predictors(), num_comp = tune()) %>%
step_downsample(Class)
svm_mod <-
svm_rbf(mode = "classification", cost = tune(), rbf_sigma = tune()) %>%
set_engine("kernlab")
library(workflows)
svm_wflow <-
workflow() %>%
add_model(svm_mod) %>%
add_recipe(seg_pre_proc)
svm_set <- parameters(svm_wflow)
svm_set <- svm_set %>% update(num_comp = num_comp(c(1, 20)))
set.seed(1291)
library(doParallel)
#> Loading required package: foreach
#>
#> Attaching package: 'foreach'
#> The following objects are masked from 'package:purrr':
#>
#> accumulate, when
#> Loading required package: iterators
#> Loading required package: parallel
cl <- makePSOCKcluster(parallel::detectCores(logical = FALSE))
registerDoParallel(cl)
foreach::getDoParWorkers()
#> [1] 2
clusterEvalQ(cl, {library(tidymodels)})
#> [[1]]
#> [1] "yardstick" "tibble" "rsample" "tidyr" "recipes"
#> [6] "purrr" "parsnip" "infer" "ggplot2" "dplyr"
#> [11] "dials" "scales" "broom" "tidymodels" "stats"
#> [16] "graphics" "grDevices" "utils" "datasets" "methods"
#> [21] "base"
#>
#> [[2]]
#> [1] "yardstick" "tibble" "rsample" "tidyr" "recipes"
#> [6] "purrr" "parsnip" "infer" "ggplot2" "dplyr"
#> [11] "dials" "scales" "broom" "tidymodels" "stats"
#> [16] "graphics" "grDevices" "utils" "datasets" "methods"
#> [21] "base"
tune_bayes(
svm_wflow,
resamples = folds,
# To use non-default parameter ranges
param_info = svm_set,
# Generate five at semi-random to start
initial = 3,
iter = 5,
# How to measure performance?
metrics = metric_set(roc_auc),
control = control_bayes(no_improve = 20, verbose = TRUE)
)
#>
#> > Generating a set of 3 initial parameter results
#> v Initialization complete
#>
#> Optimizing roc_auc using the expected improvement
#>
#> -- Iteration 1 --------------------------------------------------------------------------------------------------------
#>
#> i Current best: roc_auc=0.8653 (@iter 0)
#> i Gaussian process model
#> v Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i cost=0.00382, rbf_sigma=5.23e-06, num_comp=3
#> i Estimating performance
#> v Estimating performance
#> (x) Newest results: roc_auc=0.8521 (+/-0.00759)
#>
#> -- Iteration 2 --------------------------------------------------------------------------------------------------------
#>
#> i Current best: roc_auc=0.8653 (@iter 0)
#> i Gaussian process model
#> v Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i cost=30.3, rbf_sigma=1.2e-10, num_comp=20
#> i Estimating performance
#> v Estimating performance
#> (x) Newest results: roc_auc=0.865 (+/-0.00694)
#>
#> -- Iteration 3 --------------------------------------------------------------------------------------------------------
#>
#> i Current best: roc_auc=0.8653 (@iter 0)
#> i Gaussian process model
#> v Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i cost=0.021, rbf_sigma=0.996, num_comp=3
#> i Estimating performance
#> v Estimating performance
#> (x) Newest results: roc_auc=0.8562 (+/-0.00732)
#>
#> -- Iteration 4 --------------------------------------------------------------------------------------------------------
#>
#> i Current best: roc_auc=0.8653 (@iter 0)
#> i Gaussian process model
#> v Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i cost=0.00168, rbf_sigma=1.67e-10, num_comp=20
#> i Estimating performance
#> v Estimating performance
#> (x) Newest results: roc_auc=0.7917 (+/-0.034)
#>
#> -- Iteration 5 --------------------------------------------------------------------------------------------------------
#>
#> i Current best: roc_auc=0.8653 (@iter 0)
#> i Gaussian process model
#> v Gaussian process model
#> i Generating 5000 candidates
#> i Predicted candidates
#> i cost=0.001, rbf_sigma=1.27e-09, num_comp=8
#> i Estimating performance
#> v Estimating performance
#> (x) Newest results: roc_auc=0.7922 (+/-0.0341)
#> # 10-fold cross-validation repeated 2 times
#> # A tibble: 120 x 6
#> splits id id2 .metrics .notes .iter
#> * <list> <chr> <chr> <list> <list> <dbl>
#> 1 <split [1.4K/152]> Repeat1 Fold01 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 2 <split [1.4K/152]> Repeat1 Fold02 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 3 <split [1.4K/152]> Repeat1 Fold03 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 4 <split [1.4K/152]> Repeat1 Fold04 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 5 <split [1.4K/152]> Repeat1 Fold05 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 6 <split [1.4K/151]> Repeat1 Fold06 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 7 <split [1.4K/151]> Repeat1 Fold07 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 8 <split [1.4K/151]> Repeat1 Fold08 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 9 <split [1.4K/151]> Repeat1 Fold09 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> 10 <split [1.4K/151]> Repeat1 Fold10 <tibble [3 x 6]> <tibble [0 x 1]> 0
#> # ... with 110 more rowsCreated on 2020-02-03 by the reprex package (v0.3.0) |
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Can you guys show how to do it in the examples of the function?
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