Error using tune_race_anova() with spatialsample objects and parallel computing #74

jdberson · 2023-05-30T05:59:10Z

The problem

I'm having trouble using the tune_race_anova() function with a spatial_block_cv object from the spatialsample package whilst using parallel computing. I receive the error “There were no valid metrics for the ANOVA model.” and the show_notes(.Last.tune.result) gives Error in FUN(): ! x must be a vector, not a <sfc_POINT/sfc> object. The error does not occur if I don't use parallel computing.

I get a similar error when using the tune_grid() function from the tune package. However, if I specify control = control_grid(pkgs = "sf"), the tune_grid() function will work with the spatial_block_cv object and parallel computing.

I think the issue is that specifying control = control_race(pkgs = "sf") in the tune_race_anova() function is not being passed to control$pkgs (line 232 in the function code). I can get the tune_race_anova() function to work with parallel computing and a spatial_block_cv object if I modify the function by including “sf” in the list of packages passed to control$pkgs.

The reprex shows tuning using the tune_grid() function, with and without the control = control_grid(pkgs = "sf") argument to show the error and how it is addressed; as well as tuning using the tune_race_anova() function with and without the control = control_race(pkgs = "sf") to show that the error remains.

I did have some success using the workaround suggested for #39 which I have included at the end of the reprex.

Thanks for your help!

Reproducible example

# Load packages and prepare data ------------------------------------------

library(tidymodels)
library(spatialsample)
library(finetune)
library(sf)
#> Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE

tidymodels_prefer()

# Function to clean up parallel computing backends (if needed) from:
# paste0("https://stackoverflow.com/questions/64519640/",
# "error-in-summary-connectionconnection-invalid-connection")
unregister_dopar <- function() {
  env <- foreach:::.foreachGlobals
  rm(list=ls(name=env), pos=env)
}

# Data
data("ames", package = "modeldata")

# Convert to sf object for spatial resampling
ames_sf <- sf::st_as_sf(
  x = ames[1:200, ],
  coords = c("Longitude", "Latitude"),
  crs = 4326
)

# Resampling --------------------------------------------------------------

# Spatial resampling using the spatialsample package
set.seed(123)
spatial_block_folds <- spatial_block_cv(ames_sf, v = 5)

# Model specification -----------------------------------------------------

bart_spec <-
  parsnip::bart(trees = tune()) |>
  set_mode("regression") |>
  set_engine("dbarts")

bart_rec <-
  recipe(Sale_Price ~ Year_Built + Bldg_Type + Gr_Liv_Area,
         data = ames)

bart_wflow <-
  workflow() |>
  add_model(bart_spec) |>
  add_recipe(bart_rec)

# Grid tuning using the tune package --------------------------------------

## Grid tuning without control - gives an error message
cores <- parallel::detectCores(logical = FALSE)
cl <- parallel::makePSOCKcluster(cores)
doParallel::registerDoParallel(cl)

tune_grid_no_control <-
  bart_wflow |>
  tune_grid(
    resamples = spatial_block_folds
  )
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
#> information.

# Show what the error message was
show_notes(.Last.tune.result)
#> unique notes:
#> ─────────────────────────────────────────────────────
#> Error in `FUN()`:
#> ! `x` must be a vector, not a <sfc_POINT/sfc> object.

parallel::stopCluster(cl)
unregister_dopar()

## Grid tuning specifying the sf package in control - no error message
cl <- parallel::makePSOCKcluster(cores)
doParallel::registerDoParallel(cl)

tune_grid_with_control <-
  bart_wflow |>
  tune_grid(
    resamples = spatial_block_folds,
    control = control_grid(pkgs = "sf")
  )

parallel::stopCluster(cl)
unregister_dopar()

# Racing method using the finetune package --------------------------------

## Racing method without control - gives the same error message
cl <- parallel::makePSOCKcluster(cores)
doParallel::registerDoParallel(cl)

tune_race_no_control <-
  bart_wflow |>
  tune_race_anova(
    resamples = spatial_block_folds
  )
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
#> information.
#> Error in `test_parameters_gls()`:
#> ! There were no valid metrics for the ANOVA model.
#> Backtrace:
#>     ▆
#>  1. ├─finetune::tune_race_anova(bart_wflow, resamples = spatial_block_folds)
#>  2. └─finetune:::tune_race_anova.workflow(bart_wflow, resamples = spatial_block_folds)
#>  3.   └─finetune:::tune_race_anova_workflow(...)
#>  4.     └─finetune:::test_parameters_gls(res, control$alpha)
#>  5.       └─rlang::abort("There were no valid metrics for the ANOVA model.")

# Show what the error message was
show_notes(.Last.tune.result)
#> unique notes:
#> ─────────────────────────────────────────────────────
#> Error in `FUN()`:
#> ! `x` must be a vector, not a <sfc_POINT/sfc> object.

parallel::stopCluster(cl)
unregister_dopar()


## Racing method specifying the sf package in control - error remains
cl <- parallel::makePSOCKcluster(cores)
doParallel::registerDoParallel(cl)

tune_race_no_control <-
  bart_wflow |>
  tune_race_anova(
    resamples = spatial_block_folds,
    control = control_race(pkgs = "sf")
  )
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
#> information.
#> Error in `test_parameters_gls()`:
#> ! There were no valid metrics for the ANOVA model.
#> Backtrace:
#>     ▆
#>  1. ├─finetune::tune_race_anova(...)
#>  2. └─finetune:::tune_race_anova.workflow(...)
#>  3.   └─finetune:::tune_race_anova_workflow(...)
#>  4.     └─finetune:::test_parameters_gls(res, control$alpha)
#>  5.       └─rlang::abort("There were no valid metrics for the ANOVA model.")

# Show what the error message was
show_notes(.Last.tune.result)
#> unique notes:
#> ─────────────────────────────────────────────────────
#> Error in `FUN()`:
#> ! `x` must be a vector, not a <sfc_POINT/sfc> object.

parallel::stopCluster(cl)
unregister_dopar()

## Racing method using result from tune_grid() as the initial argument 

# This comes from the suggested workaround for another issue. See: 
# https://github.com/tidymodels/finetune/issues/39#issuecomment-1132266958

# I think this works:
cl <- parallel::makePSOCKcluster(cores)
doParallel::registerDoParallel(cl)
bart_rs <- 
  bart_wflow |>
  tune_grid(resamples = spatial_block_folds,
            control = control_grid(pkgs = "sf"),
            grid = 3)
tune_race_init <-
  bart_wflow |>
  tune_race_anova(
    resamples = spatial_block_folds,
    iter = 3,
    initial = bart_rs
    )
#> Warning: The `...` are not used in this function but one or more objects were
#> passed: 'iter', 'initial'

parallel::stopCluster(cl)
unregister_dopar()

^{Created on 2023-05-30 with reprex v2.0.2}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
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#>  version  R version 4.2.3 (2023-03-15 ucrt)
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#>  language (EN)
#>  collate  English_Australia.utf8
#>  ctype    English_Australia.utf8
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#>  date     2023-05-30
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The text was updated successfully, but these errors were encountered:

simonpcouch · 2024-01-19T21:30:35Z

Thanks for the issue, @jdberson!

Does #100 do the trick for you? You can install it with devtools::install_github("tidymodels/finetune#100").

jdberson · 2024-01-21T23:58:24Z

Hi @simonpcouch

Thanks very much for looking into this. Unfortunately I'm still getting an error message when using parallel computing.

I've included a reprex in case it's useful.

library(tidymodels)
library(spatialsample)
library(finetune)
library(sf)
#> Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUE

# Data
data("ames", package = "modeldata")

# Convert to sf object for spatial resampling
ames_sf <- sf::st_as_sf(
  x = ames[1:200, ],
  coords = c("Longitude", "Latitude"),
  crs = 4326
)

# Spatial resampling using the spatialsample package
set.seed(123)
spatial_block_folds <- spatial_block_cv(ames_sf, v = 5)


# Workflow
bart_spec <-
  parsnip::bart(trees = tune()) |>
  set_mode("regression") |>
  set_engine("dbarts")

bart_rec <-
  recipe(Sale_Price ~ Year_Built + Bldg_Type + Gr_Liv_Area,
    data = ames
  )

bart_wflow <-
  workflow() |>
  add_model(bart_spec) |>
  add_recipe(bart_rec)


# Tuning using tune_race_anova()
cores <- parallel::detectCores(logical = FALSE)
cl <- parallel::makePSOCKcluster(cores)
doParallel::registerDoParallel(cl)

tune_race_with_control <-
  bart_wflow |>
  tune_race_anova(
    resamples = spatial_block_folds,
    control = control_race(pkgs = "sf")
  )
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
#> information.
#> Error in `test_parameters_gls()`:
#> ! There were no valid metrics for the ANOVA model.
#> Backtrace:
#>     ▆
#>  1. ├─finetune::tune_race_anova(...)
#>  2. └─finetune:::tune_race_anova.workflow(...)
#>  3.   └─finetune:::tune_race_anova_workflow(...)
#>  4.     └─finetune:::test_parameters_gls(res, control$alpha, opt_metric_time)
#>  5.       └─cli::cli_abort("There were no valid metrics for the ANOVA model.")
#>  6.         └─rlang::abort(...)

# Show more information
show_notes(.Last.tune.result)
#> unique notes:
#> ─────────────────────────────────────────────────────
#> Error in `vec_size()`:
#> ! `x` must be a vector, not a <sfc_POINT/sfc> object.

^{Created on 2024-01-22 with reprex v2.0.2}

Session info

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.3.2 (2023-10-31 ucrt)
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#>  [1] C:/Users/jacob/AppData/Local/R/win-library/4.3
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#> 
#> ──────────────────────────────────────────────────────────────────────────────

simonpcouch · 2024-01-22T21:46:33Z

Ah, I see. #74 should do the trick. :)

simonpcouch added the bug an unexpected problem or unintended behavior label Oct 31, 2023

simonpcouch mentioned this issue Jan 22, 2024

load the pkgs specified in control$pkgs #101

Merged

simonpcouch closed this as completed in #101 Jan 22, 2024

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Error using tune_race_anova() with spatialsample objects and parallel computing #74

Error using tune_race_anova() with spatialsample objects and parallel computing #74

jdberson commented May 30, 2023

simonpcouch commented Jan 19, 2024

jdberson commented Jan 21, 2024

simonpcouch commented Jan 22, 2024

Error using tune_race_anova() with spatialsample objects and parallel computing #74

Error using tune_race_anova() with spatialsample objects and parallel computing #74

Comments

jdberson commented May 30, 2023

The problem

Reproducible example

simonpcouch commented Jan 19, 2024

jdberson commented Jan 21, 2024

simonpcouch commented Jan 22, 2024