nealec
June 20, 2022, 8:46pm
1
Can I just check something please. I am moving over a piece of work from caret to tidymodels. I am running a random forest model on my data and then predicting on the test dataset that contains 1 row of predictors.
In caret this runs fine but in tidymodels I am getting the error below;
Error in array(STATS, dims[perm]) : 'dims' cannot be of length 0
This error goes away if I duplicate the test data so I have 2 rows in my test dataset. Is there a limitation within tidymodels that you need to predict on >1 rows of predictors? If not then I'll put together a full reprex and send through.
nealec
June 20, 2022, 11:52pm
2
Digging further, the error is during the predict.workflow function "forge_predictors", specifically at the final process below;
mold <- extract_mold(fittedWorkflow)
blueprint <- mold$blueprint
forge <- hardhat::forge(testData, blueprint = mold$blueprint)
cleaned <- blueprint$forge$clean(blueprint = blueprint, new_data = testData, outcomes = TRUE)
blueprint <- cleaned$blueprint
predictors <- cleaned$predictors
outcomes <- cleaned$outcomes
extras <- cleaned$extras
processed <- blueprint$forge$process(blueprint = blueprint,
predictors = predictors,
outcomes = outcomes,
extras = extras)
Error in array(STATS, dims[perm]) : 'dims' cannot be of length 0
In addition: Warning message:
In sweep(res[, names(object$sds)], 2, object$sds, "/") :
STATS is longer than the extent of 'dim(x)[MARGIN]'
My original recipe contained a step_normalize(all_predictors). Removing this from the recipe means the predict() now works and the error does not appear.
Seem therefore to be an issue with forge() when you have a preprocess recipe step and only 1 row of new_data to predict on.
Max
June 21, 2022, 4:32am
3
Can you please provide a minimal reprex (reproducible example)? The goal of a reprex is to make it as easy as possible for me to recreate your problem so that I can fix it: please help me help you!
If you've never heard of a reprex before, start by reading "What is a reprex ", and follow the advice further down that page.
nealec
June 21, 2022, 8:46am
5
library(tidymodels)
data("car_prices")
cars_split <- car_prices %>%
dplyr::select(Price, Mileage, Cylinder, Doors) %>%
rsample::initial_time_split(prop = 1 - (1/NROW(car_prices)))
carsTrain <- rsample::training(cars_split)
carsTest <- rsample::testing(cars_split)
model_rf <-
parsnip::rand_forest(
mode = "regression",
mtry = tune(),
trees = tune(),
min_n = tune()
) %>%
parsnip::set_engine("ranger")
cars_folds <-
rsample::vfold_cv(data = carsTrain, v = 5, repeats = 1)
cars_recipe <- carsTrain %>%
recipes::recipe(Price ~ .) %>%
recipes::step_normalize(all_predictors())
cars_wflow <-
workflows::workflow() %>%
workflows::add_recipe(cars_recipe) %>%
workflows::add_model(model_rf)
set.seed(123)
cars_fit <-
cars_wflow %>% tune::tune_grid(
resamples = cars_folds,
metrics = yardstick::metric_set(yardstick::mae),
control = tune::control_grid(save_pred = TRUE)
)
#> i Creating pre-processing data to finalize unknown parameter: mtry
cars_fitFinalParams <-
tune::select_best(cars_fit)
model_rfFinal <-
finalize_model(model_rf, cars_fitFinalParams)
cars_fitFinal <-
fit(cars_wflow %>%
update_model(model_rfFinal),
carsTrain)
cars_fitFinalPred <-
predict(cars_fitFinal, new_data = carsTest)
#> Warning in sweep(res[, names(object$sds)], 2, object$sds, "/"): STATS is longer
#> than the extent of 'dim(x)[MARGIN]'
#> Error in array(STATS, dims[perm]): 'dims' cannot be of length 0
Created on 2022-06-21 by the reprex package (v2.0.1)
Thanks for the reprex, @nealec !
I don't see this error when I run your reprex, but some recent changes we've made to our packages may have addressed this issue.
Could you update your installs of core tidymodels packages with install.packages(c("parsnip", "recipes", "tune", "workflows", "yardstick", "hardhat")), restart R, and try to re-run? If you continue to see this error, could you share your sessioninfo::session_info()?
EDIT: clarify install code
nealec
June 21, 2022, 8:10pm
7
Hi Simon, still there I'm afraid. Regards, Chris
library(tidymodels)
data("car_prices")
cars_split <- car_prices %>%
dplyr::select(Price, Mileage, Cylinder, Doors) %>%
rsample::initial_time_split(prop = 1 - (1/NROW(car_prices)))
carsTrain <- rsample::training(cars_split)
carsTest <- rsample::testing(cars_split)
model_rf <-
parsnip::rand_forest(
mode = "regression",
mtry = tune(),
trees = tune(),
min_n = tune()
) %>%
parsnip::set_engine("ranger")
cars_folds <-
rsample::vfold_cv(data = carsTrain, v = 5, repeats = 1)
cars_recipe <- carsTrain %>%
recipes::recipe(Price ~ .) %>%
recipes::step_normalize(all_predictors())
cars_wflow <-
workflows::workflow() %>%
workflows::add_recipe(cars_recipe) %>%
workflows::add_model(model_rf)
set.seed(123)
cars_fit <-
cars_wflow %>% tune::tune_grid(
resamples = cars_folds,
metrics = yardstick::metric_set(yardstick::mae),
control = tune::control_grid(save_pred = TRUE)
)
#> i Creating pre-processing data to finalize unknown parameter: mtry
cars_fitFinalParams <-
tune::select_best(cars_fit)
model_rfFinal <-
finalize_model(model_rf, cars_fitFinalParams)
cars_fitFinal <-
fit(cars_wflow %>%
update_model(model_rfFinal),
carsTrain)
cars_fitFinalPred <-
predict(cars_fitFinal, new_data = carsTest)
#> Warning in sweep(res[, names(object$sds)], 2, object$sds, "/"): STATS is longer
#> than the extent of 'dim(x)[MARGIN]'
#> Error in array(STATS, dims[perm]): 'dims' cannot be of length 0
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.5 (2021-03-31)
#> os macOS Big Sur 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_GB.UTF-8
#> ctype en_GB.UTF-8
#> tz Europe/London
#> date 2022-06-21
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
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#> rmarkdown 2.11 2021-09-14 [1] CRAN (R 4.0.2)
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#>
#> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
Created on 2022-06-21 by the reprex package (v2.0.1)
Thanks for the quick response, @nealec .
When I try to install the package versions listed in your session info, I see a few conflicts:
> pak::pak(c("tidymodels/parsnip@284252b", "tidymodels/recipes@ce1c657",
+ "tidymodels/tune@1856421", "tidymodels/workflows@9a9e231",
+ "tidymodels/yardstick@e56b69f"))
Error: Cannot install packages:
* tidymodels/tune@1856421:
* Can't install dependency tidymodels/parsnip@feature/case-weights
* Can't install dependency tidymodels/workflows
* Can't install dependency tidymodels/yardstick
* tidymodels/workflows: Conflicts with tidymodels/workflows@9a9e231
* tidymodels/yardstick: Conflicts with tidymodels/yardstick@e56b69f
* tidymodels/parsnip@feature/case-weights: Conflicts with tidymodels/parsnip@284252b
You'll indeed need to update your tidymodels package installs.
If you'd like to continue using developmental versions, the following code will update the key players:
pak::pak(c("tidymodels/parsnip", "tidymodels/recipes",
"tidymodels/tune", "tidymodels/workflows",
"tidymodels/yardstick", "tidymodels/hardhat"))
nealec
June 21, 2022, 10:30pm
9
I had loaded those development versions to test cases weights - if the issue is related to those you may want to let @Max know. Either way everything is now updated and still seeing the error unfortunately .
library(tidymodels)
data("car_prices")
cars_split <- car_prices %>%
dplyr::select(Price, Mileage, Cylinder, Doors) %>%
rsample::initial_time_split(prop = 1 - (1/NROW(car_prices)))
carsTrain <- rsample::training(cars_split)
carsTest <- rsample::testing(cars_split)
model_rf <-
parsnip::rand_forest(
mode = "regression",
mtry = tune(),
trees = tune(),
min_n = tune()
) %>%
parsnip::set_engine("ranger")
cars_folds <-
rsample::vfold_cv(data = carsTrain, v = 5, repeats = 1)
cars_recipe <- carsTrain %>%
recipes::recipe(Price ~ .) %>%
recipes::step_normalize(all_predictors())
cars_wflow <-
workflows::workflow() %>%
workflows::add_recipe(cars_recipe) %>%
workflows::add_model(model_rf)
set.seed(123)
cars_fit <-
cars_wflow %>% tune::tune_grid(
resamples = cars_folds,
metrics = yardstick::metric_set(yardstick::mae),
control = tune::control_grid(save_pred = TRUE)
)
#> i Creating pre-processing data to finalize unknown parameter: mtry
cars_fitFinalParams <-
tune::select_best(cars_fit)
model_rfFinal <-
finalize_model(model_rf, cars_fitFinalParams)
cars_fitFinal <-
fit(cars_wflow %>%
update_model(model_rfFinal),
carsTrain)
cars_fitFinalPred <-
predict(cars_fitFinal, new_data = carsTest)
#> Warning in sweep(res[, names(object$sds)], 2, object$sds, "/"): STATS is longer
#> than the extent of 'dim(x)[MARGIN]'
#> Error in array(STATS, dims[perm]): 'dims' cannot be of length 0
tidymodels_update()
#> All packages up-to-date
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.5 (2021-03-31)
#> os macOS Big Sur 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_GB.UTF-8
#> ctype en_GB.UTF-8
#> tz Europe/London
#> date 2022-06-21
#> pandoc 2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
#>
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#> tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.0.5)
#> timeDate 3043.102 2018-02-21 [1] CRAN (R 4.0.2)
#> tune * 0.2.0.9001 2022-05-09 [1] Github (tidymodels/tune@1856421)
#> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.0.2)
#> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.0.5)
#> withr 2.5.0 2022-03-03 [1] CRAN (R 4.0.5)
#> workflows * 0.2.6.9001 2022-05-09 [1] Github (tidymodels/workflows@9a9e231)
#> workflowsets * 0.2.1 2022-03-15 [1] CRAN (R 4.0.5)
#> xfun 0.31 2022-05-10 [1] CRAN (R 4.0.5)
#> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.0.5)
#> yardstick * 1.0.0 2022-06-06 [1] CRAN (R 4.0.5)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
#>
#> ──────────────────────────────────────────────────────────────────────────────
Created on 2022-06-21 by the reprex package (v2.0.1)
nealec
June 21, 2022, 10:41pm
10
Ignore above. Despite updating tidymodels I was still running some of the case weights development versions (as seen in session info). Fully removing and reinstalling to prod versions has cleared the error.
Please let @Max know the link to the case weight versions.
library(tidymodels)
data("car_prices")
cars_split <- car_prices %>%
dplyr::select(Price, Mileage, Cylinder, Doors) %>%
rsample::initial_time_split(prop = 1 - (1/NROW(car_prices)))
carsTrain <- rsample::training(cars_split)
carsTest <- rsample::testing(cars_split)
model_rf <-
parsnip::rand_forest(
mode = "regression",
mtry = tune(),
trees = tune(),
min_n = tune()
) %>%
parsnip::set_engine("ranger")
cars_folds <-
rsample::vfold_cv(data = carsTrain, v = 5, repeats = 1)
cars_recipe <- carsTrain %>%
recipes::recipe(Price ~ .) %>%
recipes::step_normalize(all_predictors())
cars_wflow <-
workflows::workflow() %>%
workflows::add_recipe(cars_recipe) %>%
workflows::add_model(model_rf)
set.seed(123)
cars_fit <-
cars_wflow %>% tune::tune_grid(
resamples = cars_folds,
metrics = yardstick::metric_set(yardstick::mae),
control = tune::control_grid(save_pred = TRUE)
)
#> i Creating pre-processing data to finalize unknown parameter: mtry
cars_fitFinalParams <-
tune::select_best(cars_fit)
model_rfFinal <-
finalize_model(model_rf, cars_fitFinalParams)
cars_fitFinal <-
fit(cars_wflow %>%
update_model(model_rfFinal),
carsTrain)
cars_fitFinalPred <-
predict(cars_fitFinal, new_data = carsTest)
tidymodels_update()
#> All packages up-to-date
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.5 (2021-03-31)
#> os macOS Big Sur 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_GB.UTF-8
#> ctype en_GB.UTF-8
#> tz Europe/London
#> date 2022-06-21
#> pandoc 2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
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#> backports 1.4.1 2021-12-13 [1] CRAN (R 4.0.2)
#> broom * 0.8.0 2022-04-13 [1] CRAN (R 4.0.5)
#> class 7.3-20 2022-01-13 [1] CRAN (R 4.0.5)
#> cli 3.3.0 2022-04-25 [1] CRAN (R 4.0.5)
#> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.0.5)
#> colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.0.5)
#> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.0.5)
#> DBI 1.1.3 2022-06-18 [1] CRAN (R 4.0.5)
#> dials * 1.0.0 2022-06-14 [1] CRAN (R 4.0.5)
#> DiceDesign 1.9 2021-02-13 [1] CRAN (R 4.0.2)
#> digest 0.6.29 2021-12-01 [1] CRAN (R 4.0.2)
#> dplyr * 1.0.9 2022-04-28 [1] CRAN (R 4.0.5)
#> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.0.2)
#> evaluate 0.15 2022-02-18 [1] CRAN (R 4.0.5)
#> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.0.5)
#> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.0.2)
#> foreach 1.5.2 2022-02-02 [1] CRAN (R 4.0.5)
#> fs 1.5.2 2021-12-08 [1] CRAN (R 4.0.2)
#> furrr 0.3.0 2022-05-04 [1] CRAN (R 4.0.5)
#> future 1.26.1 2022-05-27 [1] CRAN (R 4.0.5)
#> future.apply 1.9.0 2022-04-25 [1] CRAN (R 4.0.5)
#> generics 0.1.2 2022-01-31 [1] CRAN (R 4.0.5)
#> ggplot2 * 3.3.6 2022-05-03 [1] CRAN (R 4.0.5)
#> globals 0.15.0 2022-05-09 [1] CRAN (R 4.0.5)
#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.0.5)
#> gower 1.0.0 2022-02-03 [1] CRAN (R 4.0.5)
#> GPfit 1.0-8 2019-02-08 [1] CRAN (R 4.0.2)
#> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.2)
#> hardhat 1.1.0 2022-06-10 [1] CRAN (R 4.0.5)
#> highr 0.9 2021-04-16 [1] CRAN (R 4.0.2)
#> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.0.2)
#> infer * 1.0.2 2022-06-10 [1] CRAN (R 4.0.5)
#> ipred 0.9-13 2022-06-02 [1] CRAN (R 4.0.5)
#> iterators 1.0.14 2022-02-05 [1] CRAN (R 4.0.5)
#> knitr 1.39 2022-04-26 [1] CRAN (R 4.0.5)
#> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.0.2)
#> lava 1.6.10 2021-09-02 [1] CRAN (R 4.0.2)
#> lhs 1.1.5 2022-03-22 [1] CRAN (R 4.0.5)
#> lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.0.2)
#> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.0.2)
#> lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.0.2)
#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.0.5)
#> MASS 7.3-57 2022-04-22 [1] CRAN (R 4.0.5)
#> Matrix 1.4-1 2022-03-23 [1] CRAN (R 4.0.5)
#> modeldata * 0.1.1.9000 2022-05-09 [1] Github (tidymodels/modeldata@78d1b89)
#> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.2)
#> nnet 7.3-17 2022-01-13 [1] CRAN (R 4.0.5)
#> parallelly 1.32.0 2022-06-07 [1] CRAN (R 4.0.5)
#> parsnip * 1.0.0 2022-06-16 [1] CRAN (R 4.0.5)
#> pillar 1.7.0 2022-02-01 [1] CRAN (R 4.0.5)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.2)
#> prodlim 2019.11.13 2019-11-17 [1] CRAN (R 4.0.2)
#> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.0.2)
#> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.0.2)
#> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.0.5)
#> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.0.5)
#> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.0.2)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.0.2)
#> ranger * 0.14.1 2022-06-18 [1] CRAN (R 4.0.5)
#> Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.0.5)
#> recipes * 0.2.0 2022-02-18 [1] CRAN (R 4.0.5)
#> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.0.2)
#> rlang 1.0.2 2022-03-04 [1] CRAN (R 4.0.5)
#> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.0.5)
#> rpart 4.1.16 2022-01-24 [1] CRAN (R 4.0.5)
#> rsample * 0.1.1 2021-11-08 [1] CRAN (R 4.0.2)
#> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.0.2)
#> scales * 1.2.0 2022-04-13 [1] CRAN (R 4.0.5)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.0.2)
#> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.0.2)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
#> styler 1.7.0 2022-03-13 [1] CRAN (R 4.0.5)
#> survival 3.3-1 2022-03-03 [1] CRAN (R 4.0.5)
#> tibble * 3.1.7 2022-05-03 [1] CRAN (R 4.0.5)
#> tidymodels * 0.2.0 2022-03-19 [1] CRAN (R 4.0.5)
#> tidyr * 1.2.0 2022-02-01 [1] CRAN (R 4.0.5)
#> tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.0.5)
#> timeDate 3043.102 2018-02-21 [1] CRAN (R 4.0.2)
#> tune * 0.2.0 2022-03-19 [1] CRAN (R 4.0.5)
#> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.0.2)
#> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.0.5)
#> withr 2.5.0 2022-03-03 [1] CRAN (R 4.0.5)
#> workflows * 0.2.6 2022-03-18 [1] CRAN (R 4.0.5)
#> workflowsets * 0.2.1 2022-03-15 [1] CRAN (R 4.0.5)
#> xfun 0.31 2022-05-10 [1] CRAN (R 4.0.5)
#> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.0.5)
#> yardstick * 1.0.0 2022-06-06 [1] CRAN (R 4.0.5)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
#>
#> ──────────────────────────────────────────────────────────────────────────────
Created on 2022-06-21 by the reprex package (v2.0.1)
1 Like
system
July 12, 2022, 10:42pm
11
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