parsnip
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tidymodels
Unable to tune penalty for `glmnet` with non-default family
The problem
I'm unable to tune the penalty hyperparameter for a glmnet model specification with non-default family engine argument. I've traced the error down to the multi_predict generic not having a method for generalized model fits (class _glmnetfit).
I have also tried passing specific path_values as an engine argument (which I think will need to be done to correctly compare penalty values) but that didn't resolve the underlying issue with multi_predict.
Reproducible example
library(tidyverse)
library(tidymodels)
recipe <- recipe(hp ~ ., data = mtcars)
lasso <- linear_reg(
mixture = 1,
penalty = !!tune()
) %>%
set_engine(
engine = 'glmnet',
family = gaussian(link = 'log')
)
wflow <- workflow(recipe, lasso)
grid <- grid_max_entropy(penalty(), size = 10)
folds <- vfold_cv(mtcars)
res <- tune_grid(
object = wflow,
resamples = folds,
grid = grid
)
#> x Fold01: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold02: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold03: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold04: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold05: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold06: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold07: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold08: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold09: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold10: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> Warning: All models failed. See the `.notes` column.
res %>% collect_notes %>% distinct(note) %>% reduce(c) %>% cli::cli_ul()
#> • Error in `parsnip::multi_predict()`: ! No `multi_predict` method exists for
#> objects with classes '_glmnetfit', 'model_fit'
Created on 2022-05-31 by the reprex package (v2.0.1)
Hello @cb12991! I'm not able to reproduce the error using CRAN versions or dev versions of the core tidymodels packages, Can you include the session information in your reprex using sessioninfo::session_info()?
CRAN reprex
library(tidyverse)
library(tidymodels)
recipe <- recipe(hp ~ ., data = mtcars)
lasso <- linear_reg(
mixture = 1,
penalty = !!tune()
) %>%
set_engine(
engine = 'glmnet'
)
wflow <- workflow(recipe, lasso)
grid <- grid_max_entropy(penalty(), size = 10)
folds <- vfold_cv(mtcars)
res <- tune_grid(
object = wflow,
resamples = folds,
grid = grid
)
res
#> # Tuning results
#> # 10-fold cross-validation
#> # A tibble: 10 × 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [28/4]> Fold01 <tibble [20 × 5]> <tibble [0 × 3]>
#> 2 <split [28/4]> Fold02 <tibble [20 × 5]> <tibble [0 × 3]>
#> 3 <split [29/3]> Fold03 <tibble [20 × 5]> <tibble [0 × 3]>
#> 4 <split [29/3]> Fold04 <tibble [20 × 5]> <tibble [0 × 3]>
#> 5 <split [29/3]> Fold05 <tibble [20 × 5]> <tibble [0 × 3]>
#> 6 <split [29/3]> Fold06 <tibble [20 × 5]> <tibble [0 × 3]>
#> 7 <split [29/3]> Fold07 <tibble [20 × 5]> <tibble [0 × 3]>
#> 8 <split [29/3]> Fold08 <tibble [20 × 5]> <tibble [0 × 3]>
#> 9 <split [29/3]> Fold09 <tibble [20 × 5]> <tibble [0 × 3]>
#> 10 <split [29/3]> Fold10 <tibble [20 × 5]> <tibble [0 × 3]>
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.0 (2022-04-22)
#> os macOS Monterey 12.2.1
#> system aarch64, darwin20
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/Los_Angeles
#> date 2022-05-31
#> pandoc 2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
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#> future 1.25.0 2022-04-24 [1] CRAN (R 4.2.0)
#> future.apply 1.9.0 2022-04-25 [1] CRAN (R 4.2.0)
#> generics 0.1.2 2022-01-31 [1] CRAN (R 4.2.0)
#> ggplot2 * 3.3.6 2022-05-03 [1] CRAN (R 4.2.0)
#> glmnet * 4.1-4 2022-04-15 [1] CRAN (R 4.2.0)
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#> hardhat 0.2.0 2022-01-24 [1] CRAN (R 4.2.0)
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#> infer * 1.0.0 2021-08-13 [1] CRAN (R 4.2.0)
#> ipred 0.9-12 2021-09-15 [1] CRAN (R 4.2.0)
#> iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.0)
#> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0)
#> knitr 1.39 2022-04-26 [1] CRAN (R 4.2.0)
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#> parsnip * 0.2.1 2022-03-17 [1] CRAN (R 4.2.0)
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#>
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#>
#> ──────────────────────────────────────────────────────────────────────────────
Dev regrex
library(tidyverse)
library(tidymodels)
recipe <- recipe(hp ~ ., data = mtcars)
lasso <- linear_reg(
mixture = 1,
penalty = !!tune()
) %>%
set_engine(
engine = 'glmnet'
)
wflow <- workflow(recipe, lasso)
grid <- grid_max_entropy(penalty(), size = 10)
folds <- vfold_cv(mtcars)
res <- tune_grid(
object = wflow,
resamples = folds,
grid = grid
)
res
#> # Tuning results
#> # 10-fold cross-validation
#> # A tibble: 10 × 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [28/4]> Fold01 <tibble [20 × 5]> <tibble [0 × 3]>
#> 2 <split [28/4]> Fold02 <tibble [20 × 5]> <tibble [0 × 3]>
#> 3 <split [29/3]> Fold03 <tibble [20 × 5]> <tibble [0 × 3]>
#> 4 <split [29/3]> Fold04 <tibble [20 × 5]> <tibble [0 × 3]>
#> 5 <split [29/3]> Fold05 <tibble [20 × 5]> <tibble [0 × 3]>
#> 6 <split [29/3]> Fold06 <tibble [20 × 5]> <tibble [0 × 3]>
#> 7 <split [29/3]> Fold07 <tibble [20 × 5]> <tibble [0 × 3]>
#> 8 <split [29/3]> Fold08 <tibble [20 × 5]> <tibble [0 × 3]>
#> 9 <split [29/3]> Fold09 <tibble [20 × 5]> <tibble [0 × 3]>
#> 10 <split [29/3]> Fold10 <tibble [20 × 5]> <tibble [0 × 3]>
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.0 (2022-04-22)
#> os macOS Monterey 12.2.1
#> system aarch64, darwin20
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/Los_Angeles
#> date 2022-05-31
#> pandoc 2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.2.0)
#> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
#> broom * 0.8.0 2022-04-13 [1] CRAN (R 4.2.0)
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Created on 2022-05-31 by the reprex package (v2.0.1)
Sure, but it doesn't look like you changed the family argument from the default "gaussian". Using the default does not generate the error for me either.
Updated Reprex
library(tidyverse)
library(tidymodels)
grid <- grid_max_entropy(penalty(), size = 10)
folds <- vfold_cv(mtcars)
recipe <- recipe(hp ~ ., data = mtcars)
lasso <- linear_reg(
mixture = 1,
penalty = !!tune()
) %>%
set_engine(
engine = 'glmnet'
)
wflow <- workflow(recipe, lasso)
res <- tune_grid(
object = wflow,
resamples = folds,
grid = grid
)
res %>% collect_notes %>% distinct(note) %>% reduce(c) %>% cli::cli_ul()
lasso_loglink <- linear_reg(
mixture = 1,
penalty = !!tune()
) %>%
set_engine(
engine = 'glmnet',
family = gaussian(link = 'log')
)
wflow_loglink <- wflow %>% update_model(lasso_loglink)
res_loglink <- tune_grid(
object = wflow_loglink,
resamples = folds,
grid = grid
)
#> x Fold01: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold02: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold03: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold04: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold05: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold06: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold07: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold08: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold09: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> x Fold10: preprocessor 1/1, model 1/1 (predictions): Error in `parsnip::multi_pred...
#> Warning: All models failed. See the `.notes` column.
res_loglink %>% collect_notes %>% distinct(note) %>% reduce(c) %>% cli::cli_ul()
#> • Error in `parsnip::multi_predict()`: ! No `multi_predict` method exists for
#> objects with classes '_glmnetfit', 'model_fit'
sessioninfo::session_info()
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#> ──────────────────────────────────────────────────────────────────────────────
Created on 2022-05-31 by the reprex package (v2.0.1)
I got it! More minimal reprex below:
Much like how we have multi_predict._elnet() we should have mutli_predict. _glmnetfit()
library(parsnip)
lasso <- linear_reg(penalty = 1) %>%
set_engine(
engine = 'glmnet',
family = gaussian(link = 'log')
)
fit(lasso, hp ~ ., data = mtcars) %>%
multi_predict(new_data = mtcars)
#> Error in `multi_predict()`:
#> ! No `multi_predict` method exists for objects with classes '_glmnetfit', 'model_fit'
lasso <- linear_reg(penalty = 1) %>%
set_engine(
engine = 'glmnet'
)
fit(lasso, hp ~ ., data = mtcars) %>%
multi_predict(new_data = mtcars)
#> # A tibble: 32 × 1
#> .pred
#> <list>
#> 1 <tibble [1 × 2]>
#> 2 <tibble [1 × 2]>
#> 3 <tibble [1 × 2]>
#> 4 <tibble [1 × 2]>
#> 5 <tibble [1 × 2]>
#> 6 <tibble [1 × 2]>
#> 7 <tibble [1 × 2]>
#> 8 <tibble [1 × 2]>
#> 9 <tibble [1 × 2]>
#> 10 <tibble [1 × 2]>
#> # … with 22 more rows
Created on 2022-05-31 by the reprex package (v2.0.1)
Yes. The object type going out of glmnet::glmnet() is not well documented:
An object with S3 class
"glmnet","*", where"*"is "elnet", "lognet", "multnet", "fishnet" (poisson), "coxnet" or "mrelnet" for the various types of models. If the model was created with relax=TRUE then this class has a prefix class of "relaxed".
It implies that there would always be a more specific class for us to work off of.
I'm facing similar problems using the "mgaussian" family argument to glmnet.
Is a solution to this issue in the works, or should I use the glmnet package's own predict methods for the time being?
#483 is the reason for this
@frankhezemans this is on my todo list, and I'll take a look at the "mgaussian" option as well.
@frankhezemans regarding the "mgaussian" option, we could look into supporting this family as well but that would likely be restricted to model fitting via parsnip: parsnip can support a multivariate response but the packages for performance metrics and tuning currently don't have infrastructure for multivariate responses. If such a "parsnip only" solution would be helpful to you (without the tuning), please open a separate issue for this here on the parsnip repo. Thank you!
Thank you @hfrick for following up on this. I am aware of the limited support for evaluation of models with multivariate response data. In the meantime, I have written some slightly hacky functions and scripts to serve the needs of my specific project. Thus, from my perspective, a "parsnip only" solution is not urgently needed. But thanks again for your support!
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.
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