Closed
Description
The problem
When a custom recipe step is created, it can be used in a tuning grid when the tuning grid is run sequentially, ie. control_grid(allow_par = FALSE), but not when the tuning grid is run in parallel, ie. allow_par = TRUE.
When run in parallel the following error occurs (in this case I am using the step_percentile example custom step):
- "recipe: Error in UseMethod("prep"): no applicable method for 'prep' applied to an object of class "c('step_percentile', 'step')""
Reproducible example
Below I used the example code from the recipes package for creating a custom step, and from the tune package for running a model through a tuning grid, with a few modifications that are mostly noted. You will see at the bottom of the example the "no applicable method for 'prep'" error. I should also note that I've tried adding step_percentile to a package and adding pkgs="MyPackage" to the control_grid, but this did not make a difference.
In a second version version of the code, which I've placed underneath, I've modified it to run sequentially and it runs correctly without any issues.
You can skip to the last 20 lines of the example to see the actual relevant code. Above that is just the definition of the custom step.
Failing Parallel Version
library(tidymodels)
#> -- Attaching packages ------------------------------------------------------------------------------------------------------------------------------------ tidymodels 0.1.0 --
#> v broom 0.5.5 v recipes 0.1.10
#> v dials 0.0.5 v rsample 0.0.6
#> v dplyr 0.8.5 v tibble 3.0.0
#> v ggplot2 3.3.0 v tune 0.0.1
#> v infer 0.5.1 v workflows 0.1.1
#> v parsnip 0.0.5 v yardstick 0.0.6
#> v purrr 0.3.3
#> -- 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()
library(mlbench)
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
### RECIPES
step_percentile <- function(
recipe, ...,
role = NA,
trained = FALSE,
ref_dist = NULL,
approx = FALSE,
options = list(probs = (0:100)/100, names = TRUE),
skip = FALSE,
id = rand_id("percentile")
) {
## The variable selectors are not immediately evaluated by using
## the `quos` function in `rlang`. `ellipse_check` captures the
## values and also checks to make sure that they are not empty.
terms <- recipes::ellipse_check(...)
recipes::add_step(
recipe,
step_percentile_new(
terms = terms,
trained = trained,
role = role,
ref_dist = ref_dist,
approx = approx,
options = options,
skip = skip,
id = id
)
)
}
step_percentile_new <-
function(terms, role, trained, ref_dist, approx, options, skip, id) {
step(
subclass = "percentile",
terms = terms,
role = role,
trained = trained,
ref_dist = ref_dist,
approx = approx,
options = options,
skip = skip,
id = id
)
}
get_pctl <- function(x, args) {
args$x <- x
do.call("quantile", args)
}
prep.step_percentile <- function(x, training, info = NULL, ...) {
col_names <- recipes::terms_select(terms = x$terms, info = info)
## You can add error trapping for non-numeric data here and so on. See the
## `check_type` function to do this for basic types.
## We'll use the names later so
if (x$options$names == FALSE)
rlang::abort("`names` should be set to TRUE")
if (!x$approx) {
ref_dist <- training[, col_names]
} else {
ref_dist <- purrr::map(training[, col_names], get_pctl, args = x$options)
}
## Use the constructor function to return the updated object.
## Note that `trained` is set to TRUE
step_percentile_new(
terms = x$terms,
trained = TRUE,
role = x$role,
ref_dist = ref_dist,
approx = x$approx,
options = x$options,
skip = x$skip,
id = x$id
)
}
## Two helper functions
pctl_by_mean <- function(x, ref) mean(ref <= x)
pctl_by_approx <- function(x, ref) {
## go from 1 column tibble to vector
x <- getElement(x, names(x))
## get the percentiles values from the names (e.g. "10%")
p_grid <- as.numeric(gsub("%$", "", names(ref)))
approx(x = ref, y = p_grid, xout = x)$y/100
}
bake.step_percentile <- function(object, new_data, ...) {
require(tibble)
## For illustration (and not speed), we will loop through the affected variables
## and do the computations
vars <- names(object$ref_dist)
for (i in vars) {
if (!object$approx) {
## We can use `apply` since tibbles do not drop dimensions:
new_data[, i] <- apply(new_data[, i], 1, pctl_by_mean,
ref = object$ref_dist[, i])
} else
new_data[, i] <- pctl_by_approx(new_data[, i], object$ref_dist[[i]])
}
## Always convert to tibbles on the way out
as_tibble(new_data)
}
### TUNE
data(Ionosphere)
Ionosphere <- Ionosphere %>% select(-V2)
svm_mod <-
svm_rbf(cost = tune(), rbf_sigma = tune()) %>%
set_mode("classification") %>%
set_engine("kernlab")
iono_rec <-
recipe(Class ~ ., data = Ionosphere) %>%
# In case V1 is has a single value sampled
step_zv(recipes::all_predictors()) %>%
# convert it to a dummy variable
step_dummy(V1) %>%
# Scale it the same as the others
step_range(matches("V1_")) %>%
step_percentile(recipes::all_numeric()) # custom step added to the recipe
set.seed(4943)
iono_rs <- bootstraps(Ionosphere, times = 5) # times reduced to speed up sequential model tuning
roc_vals <- metric_set(roc_auc)
cluster = makeCluster(detectCores()) # Create a cluster
registerDoParallel(cluster)
ctrl <- control_grid(verbose = FALSE, allow_par = TRUE) # Run in parallel
set.seed(35)
grid_form <-
tune_grid(
iono_rec,
model = svm_mod,
resamples = iono_rs,
metrics = roc_vals,
control = ctrl
)
#> Warning: All models failed in tune_grid(). See the `.notes` column.
print(grid_form$.notes[[1]])
#> # A tibble: 1 x 1
#> .notes
#> <chr>
#> 1 "recipe: Error in UseMethod(\"prep\"): no applicable method for 'prep' applie~Created on 2020-04-03 by the reprex package (v0.3.0)
Session info
devtools::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 3.6.3 (2020-02-29)
#> os Windows Server x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz America/New_York
#> date 2020-04-03
#>
#> - Packages -------------------------------------------------------------------
#> package * version date lib source
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#>
#> [1] D:/R/Library/3.6
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library(tidymodels)
#> -- Attaching packages ------------------------------------------------------------------------------------------------------------------------------------ tidymodels 0.1.0 --
#> v broom 0.5.5 v recipes 0.1.10
#> v dials 0.0.5 v rsample 0.0.6
#> v dplyr 0.8.5 v tibble 3.0.0
#> v ggplot2 3.3.0 v tune 0.0.1
#> v infer 0.5.1 v workflows 0.1.1
#> v parsnip 0.0.5 v yardstick 0.0.6
#> v purrr 0.3.3
#> -- 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()
library(mlbench)
### RECIPES
step_percentile <- function(
recipe, ...,
role = NA,
trained = FALSE,
ref_dist = NULL,
approx = FALSE,
options = list(probs = (0:100)/100, names = TRUE),
skip = FALSE,
id = rand_id("percentile")
) {
## The variable selectors are not immediately evaluated by using
## the `quos` function in `rlang`. `ellipse_check` captures the
## values and also checks to make sure that they are not empty.
terms <- recipes::ellipse_check(...)
recipes::add_step(
recipe,
step_percentile_new(
terms = terms,
trained = trained,
role = role,
ref_dist = ref_dist,
approx = approx,
options = options,
skip = skip,
id = id
)
)
}
step_percentile_new <-
function(terms, role, trained, ref_dist, approx, options, skip, id) {
step(
subclass = "percentile",
terms = terms,
role = role,
trained = trained,
ref_dist = ref_dist,
approx = approx,
options = options,
skip = skip,
id = id
)
}
get_pctl <- function(x, args) {
args$x <- x
do.call("quantile", args)
}
prep.step_percentile <- function(x, training, info = NULL, ...) {
col_names <- recipes::terms_select(terms = x$terms, info = info)
## You can add error trapping for non-numeric data here and so on. See the
## `check_type` function to do this for basic types.
## We'll use the names later so
if (x$options$names == FALSE)
rlang::abort("`names` should be set to TRUE")
if (!x$approx) {
ref_dist <- training[, col_names]
} else {
ref_dist <- purrr::map(training[, col_names], get_pctl, args = x$options)
}
## Use the constructor function to return the updated object.
## Note that `trained` is set to TRUE
step_percentile_new(
terms = x$terms,
trained = TRUE,
role = x$role,
ref_dist = ref_dist,
approx = x$approx,
options = x$options,
skip = x$skip,
id = x$id
)
}
## Two helper functions
pctl_by_mean <- function(x, ref) mean(ref <= x)
pctl_by_approx <- function(x, ref) {
## go from 1 column tibble to vector
x <- getElement(x, names(x))
## get the percentiles values from the names (e.g. "10%")
p_grid <- as.numeric(gsub("%$", "", names(ref)))
approx(x = ref, y = p_grid, xout = x)$y/100
}
bake.step_percentile <- function(object, new_data, ...) {
require(tibble)
## For illustration (and not speed), we will loop through the affected variables
## and do the computations
vars <- names(object$ref_dist)
for (i in vars) {
if (!object$approx) {
## We can use `apply` since tibbles do not drop dimensions:
new_data[, i] <- apply(new_data[, i], 1, pctl_by_mean,
ref = object$ref_dist[, i])
} else
new_data[, i] <- pctl_by_approx(new_data[, i], object$ref_dist[[i]])
}
## Always convert to tibbles on the way out
as_tibble(new_data)
}
### TUNE
data(Ionosphere)
Ionosphere <- Ionosphere %>% select(-V2)
svm_mod <-
svm_rbf(cost = tune(), rbf_sigma = tune()) %>%
set_mode("classification") %>%
set_engine("kernlab")
iono_rec <-
recipe(Class ~ ., data = Ionosphere) %>%
# In case V1 is has a single value sampled
step_zv(recipes::all_predictors()) %>%
# convert it to a dummy variable
step_dummy(V1) %>%
# Scale it the same as the others
step_range(matches("V1_")) %>%
step_percentile(recipes::all_numeric()) # custom step added to the recipe
set.seed(4943)
iono_rs <- bootstraps(Ionosphere, times = 5) # times reduced to speed up sequential model tuning
roc_vals <- metric_set(roc_auc)
ctrl <- control_grid(verbose = FALSE, allow_par = FALSE)
set.seed(35)
grid_form <-
tune_grid(
iono_rec,
model = svm_mod,
resamples = iono_rs,
metrics = roc_vals,
control = ctrl
)
print(grid_form)
#> # Bootstrap sampling
#> # A tibble: 5 x 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [351/120]> Bootstrap1 <tibble [10 x 5]> <tibble [0 x 1]>
#> 2 <split [351/130]> Bootstrap2 <tibble [10 x 5]> <tibble [0 x 1]>
#> 3 <split [351/137]> Bootstrap3 <tibble [10 x 5]> <tibble [0 x 1]>
#> 4 <split [351/141]> Bootstrap4 <tibble [10 x 5]> <tibble [0 x 1]>
#> 5 <split [351/131]> Bootstrap5 <tibble [10 x 5]> <tibble [0 x 1]>Created on 2020-04-03 by the reprex package (v0.3.0)
Session info
devtools::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
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[-]Custom recipe steps don't work when tuning is down in parallel[/-][+]Custom recipe steps don't work when tuning is done in parallel[/+]ksizaidi-h commentedon Jun 18, 2020
I have the same bug when I try tuning in parallel using a step form the
themispackage.lucasc400 commentedon Oct 4, 2020
I experienced the same issue with themis::step_smote()
I used the following codes to create a parallel cluster, as described in https://tune.tidymodels.org/articles/extras/optimizations.html:
all_cores <- parallel::detectCores(logical = FALSE) library(doParallel) cl <- makePSOCKcluster(all_cores) registerDoParallel(cl)where I got an error message of
"recipe: Error in UseMethod("prep"): no applicable method for 'prep' applied to an object of class "c('step_smote', 'step')".
The same codes worked when I used themis::step_downsample() instead. I don't think I could use the !!variable method as suggested in the link, because I wanted to put a column name in step_smote(). I can post my codes if needed.
Is this issue fixed and I missed out something or is this still something under development? I see the pull request that has been merged but I don't see how I can fix it (sorry I'm quite new to GitHub).
juliasilge commentedon Oct 5, 2020
The fix in the PR linked here was done after the latest CRAN release. Do you have the development version of the package from GitHub? You can install via:
We are planning a new CRAN release for tune pretty soon, so that this fix will be available more broadly. If you are able, can you try this out and see if your problem is fixed?
juliasilge commentedon Oct 7, 2020
I misspoke! This is fixed in the current version of recipes:
Open a new issue if you continue to have a problem after updating! 🙌
github-actions commentedon Mar 6, 2021
This issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue.