Unable to tune penalty for `glmnet` with non-default family

[cb12991]

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)}

[EmilHvitfeldt]

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 ───────────────────────────────────────────────────────────────────
#>  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)
#>  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.2.0)
#>  class          7.3-20     2022-01-16 [1] CRAN (R 4.2.0)
#>  cli            3.3.0      2022-04-25 [1] CRAN (R 4.2.0)
#>  codetools      0.2-18     2020-11-04 [1] CRAN (R 4.2.0)
#>  colorspace     2.0-3      2022-02-21 [1] CRAN (R 4.2.0)
#>  crayon         1.5.1      2022-03-26 [1] CRAN (R 4.2.0)
#>  DBI            1.1.2      2021-12-20 [1] CRAN (R 4.2.0)
#>  dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.2.0)
#>  dials        * 0.1.1      2022-04-06 [1] CRAN (R 4.2.0)
#>  DiceDesign     1.9        2021-02-13 [1] CRAN (R 4.2.0)
#>  digest         0.6.29     2021-12-01 [1] CRAN (R 4.2.0)
#>  dplyr        * 1.0.9      2022-04-28 [1] CRAN (R 4.2.0)
#>  ellipsis       0.3.2      2021-04-29 [1] CRAN (R 4.2.0)
#>  evaluate       0.15       2022-02-18 [1] CRAN (R 4.2.0)
#>  fansi          1.0.3      2022-03-24 [1] CRAN (R 4.2.0)
#>  fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.2.0)
#>  forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.2.0)
#>  foreach        1.5.2      2022-02-02 [1] CRAN (R 4.2.0)
#>  fs             1.5.2      2021-12-08 [1] CRAN (R 4.2.0)
#>  furrr          0.3.0      2022-05-04 [1] CRAN (R 4.2.0)
#>  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)
#>  globals        0.15.0     2022-05-09 [1] CRAN (R 4.2.0)
#>  glue           1.6.2      2022-02-24 [1] CRAN (R 4.2.0)
#>  gower          1.0.0      2022-02-03 [1] CRAN (R 4.2.0)
#>  GPfit          1.0-8      2019-02-08 [1] CRAN (R 4.2.0)
#>  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.2.0)
#>  hardhat        0.2.0      2022-01-24 [1] CRAN (R 4.2.0)
#>  haven          2.5.0      2022-04-15 [1] CRAN (R 4.2.0)
#>  highr          0.9        2021-04-16 [1] CRAN (R 4.2.0)
#>  hms            1.1.1      2021-09-26 [1] CRAN (R 4.2.0)
#>  htmltools      0.5.2      2021-08-25 [1] CRAN (R 4.2.0)
#>  httr           1.4.3      2022-05-04 [1] CRAN (R 4.2.0)
#>  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)
#>  lattice        0.20-45    2021-09-22 [1] CRAN (R 4.2.0)
#>  lava           1.6.10     2021-09-02 [1] CRAN (R 4.2.0)
#>  lhs            1.1.5      2022-03-22 [1] CRAN (R 4.2.0)
#>  lifecycle      1.0.1      2021-09-24 [1] CRAN (R 4.2.0)
#>  listenv        0.8.0      2019-12-05 [1] CRAN (R 4.2.0)
#>  lubridate      1.8.0      2021-10-07 [1] CRAN (R 4.2.0)
#>  magrittr       2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
#>  MASS           7.3-56     2022-03-23 [1] CRAN (R 4.2.0)
#>  Matrix       * 1.4-1      2022-03-23 [1] CRAN (R 4.2.0)
#>  modeldata    * 0.1.1      2021-07-14 [1] CRAN (R 4.2.0)
#>  modelr         0.1.8      2020-05-19 [1] CRAN (R 4.2.0)
#>  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.2.0)
#>  nnet           7.3-17     2022-01-16 [1] CRAN (R 4.2.0)
#>  parallelly     1.31.1     2022-04-22 [1] CRAN (R 4.2.0)
#>  parsnip      * 0.2.1      2022-03-17 [1] CRAN (R 4.2.0)
#>  pillar         1.7.0      2022-02-01 [1] CRAN (R 4.2.0)
#>  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.2.0)
#>  plyr           1.8.7      2022-03-24 [1] CRAN (R 4.2.0)
#>  pROC           1.18.0     2021-09-03 [1] CRAN (R 4.2.0)
#>  prodlim        2019.11.13 2019-11-17 [1] CRAN (R 4.2.0)
#>  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.2.0)
#>  R.cache        0.15.0     2021-04-30 [1] CRAN (R 4.2.0)
#>  R.methodsS3    1.8.1      2020-08-26 [1] CRAN (R 4.2.0)
#>  R.oo           1.24.0     2020-08-26 [1] CRAN (R 4.2.0)
#>  R.utils        2.11.0     2021-09-26 [1] CRAN (R 4.2.0)
#>  R6             2.5.1      2021-08-19 [1] CRAN (R 4.2.0)
#>  Rcpp           1.0.8.3    2022-03-17 [1] CRAN (R 4.2.0)
#>  readr        * 2.1.2      2022-01-30 [1] CRAN (R 4.2.0)
#>  readxl         1.4.0      2022-03-28 [1] CRAN (R 4.2.0)
#>  recipes      * 0.2.0      2022-02-18 [1] CRAN (R 4.2.0)
#>  reprex         2.0.1      2021-08-05 [1] CRAN (R 4.2.0)
#>  rlang          1.0.2      2022-03-04 [1] CRAN (R 4.2.0)
#>  rmarkdown      2.14       2022-04-25 [1] CRAN (R 4.2.0)
#>  rpart          4.1.16     2022-01-24 [1] CRAN (R 4.2.0)
#>  rsample      * 0.1.1      2021-11-08 [1] CRAN (R 4.2.0)
#>  rstudioapi     0.13       2020-11-12 [1] CRAN (R 4.2.0)
#>  rvest          1.0.2      2021-10-16 [1] CRAN (R 4.2.0)
#>  scales       * 1.2.0      2022-04-13 [1] CRAN (R 4.2.0)
#>  sessioninfo    1.2.2      2021-12-06 [1] CRAN (R 4.2.0)
#>  shape          1.4.6      2021-05-19 [1] CRAN (R 4.2.0)
#>  stringi        1.7.6      2021-11-29 [1] CRAN (R 4.2.0)
#>  stringr      * 1.4.0      2019-02-10 [1] CRAN (R 4.2.0)
#>  styler         1.7.0      2022-03-13 [1] CRAN (R 4.2.0)
#>  survival       3.3-1      2022-03-03 [1] CRAN (R 4.2.0)
#>  tibble       * 3.1.7      2022-05-03 [1] CRAN (R 4.2.0)
#>  tidymodels   * 0.2.0      2022-03-19 [1] CRAN (R 4.2.0)
#>  tidyr        * 1.2.0      2022-02-01 [1] CRAN (R 4.2.0)
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#>  tidyverse    * 1.3.1      2021-04-15 [1] CRAN (R 4.2.0)
#>  timeDate       3043.102   2018-02-21 [1] CRAN (R 4.2.0)
#>  tune         * 0.2.0      2022-03-19 [1] CRAN (R 4.2.0)
#>  tzdb           0.3.0      2022-03-28 [1] CRAN (R 4.2.0)
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#>  withr          2.5.0      2022-03-03 [1] CRAN (R 4.2.0)
#>  workflows    * 0.2.6      2022-03-18 [1] CRAN (R 4.2.0)
#>  workflowsets * 0.2.1      2022-03-15 [1] CRAN (R 4.2.0)
#>  xfun           0.31       2022-05-10 [1] CRAN (R 4.2.0)
#>  xml2           1.3.3      2021-11-30 [1] CRAN (R 4.2.0)
#>  yaml           2.3.5      2022-02-21 [1] CRAN (R 4.2.0)
#>  yardstick    * 0.0.9      2021-11-22 [1] CRAN (R 4.2.0)
#> 
#>  [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

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)
#>  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.2.0)
#>  class          7.3-20     2022-01-16 [1] CRAN (R 4.2.0)
#>  cli            3.3.0      2022-04-25 [1] CRAN (R 4.2.0)
#>  codetools      0.2-18     2020-11-04 [1] CRAN (R 4.2.0)
#>  colorspace     2.0-3      2022-02-21 [1] CRAN (R 4.2.0)
#>  crayon         1.5.1      2022-03-26 [1] CRAN (R 4.2.0)
#>  DBI            1.1.2      2021-12-20 [1] CRAN (R 4.2.0)
#>  dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.2.0)
#>  dials        * 0.1.1      2022-04-06 [1] CRAN (R 4.2.0)
#>  DiceDesign     1.9        2021-02-13 [1] CRAN (R 4.2.0)
#>  digest         0.6.29     2021-12-01 [1] CRAN (R 4.2.0)
#>  dplyr        * 1.0.9      2022-04-28 [1] CRAN (R 4.2.0)
#>  ellipsis       0.3.2      2021-04-29 [1] CRAN (R 4.2.0)
#>  evaluate       0.15       2022-02-18 [1] CRAN (R 4.2.0)
#>  fansi          1.0.3      2022-03-24 [1] CRAN (R 4.2.0)
#>  fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.2.0)
#>  forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.2.0)
#>  foreach        1.5.2      2022-02-02 [1] CRAN (R 4.2.0)
#>  fs             1.5.2      2021-12-08 [1] CRAN (R 4.2.0)
#>  furrr          0.3.0      2022-05-04 [1] CRAN (R 4.2.0)
#>  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)
#>  globals        0.15.0     2022-05-09 [1] CRAN (R 4.2.0)
#>  glue           1.6.2      2022-02-24 [1] CRAN (R 4.2.0)
#>  gower          1.0.0      2022-02-03 [1] CRAN (R 4.2.0)
#>  GPfit          1.0-8      2019-02-08 [1] CRAN (R 4.2.0)
#>  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.2.0)
#>  hardhat        0.2.0.9000 2022-05-31 [1] Github (tidymodels/hardhat@2074ccb)
#>  haven          2.5.0      2022-04-15 [1] CRAN (R 4.2.0)
#>  highr          0.9        2021-04-16 [1] CRAN (R 4.2.0)
#>  hms            1.1.1      2021-09-26 [1] CRAN (R 4.2.0)
#>  htmltools      0.5.2      2021-08-25 [1] CRAN (R 4.2.0)
#>  httr           1.4.3      2022-05-04 [1] CRAN (R 4.2.0)
#>  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)
#>  lattice        0.20-45    2021-09-22 [1] CRAN (R 4.2.0)
#>  lava           1.6.10     2021-09-02 [1] CRAN (R 4.2.0)
#>  lhs            1.1.5      2022-03-22 [1] CRAN (R 4.2.0)
#>  lifecycle      1.0.1      2021-09-24 [1] CRAN (R 4.2.0)
#>  listenv        0.8.0      2019-12-05 [1] CRAN (R 4.2.0)
#>  lubridate      1.8.0      2021-10-07 [1] CRAN (R 4.2.0)
#>  magrittr       2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
#>  MASS           7.3-56     2022-03-23 [1] CRAN (R 4.2.0)
#>  Matrix       * 1.4-1      2022-03-23 [1] CRAN (R 4.2.0)
#>  modeldata    * 0.1.1      2021-07-14 [1] CRAN (R 4.2.0)
#>  modelr         0.1.8      2020-05-19 [1] CRAN (R 4.2.0)
#>  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.2.0)
#>  nnet           7.3-17     2022-01-16 [1] CRAN (R 4.2.0)
#>  parallelly     1.31.1     2022-04-22 [1] CRAN (R 4.2.0)
#>  parsnip      * 0.2.1.9002 2022-05-31 [1] Github (tidymodels/parsnip@f2f24a0)
#>  pillar         1.7.0      2022-02-01 [1] CRAN (R 4.2.0)
#>  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.2.0)
#>  prodlim        2019.11.13 2019-11-17 [1] CRAN (R 4.2.0)
#>  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.2.0)
#>  R.cache        0.15.0     2021-04-30 [1] CRAN (R 4.2.0)
#>  R.methodsS3    1.8.1      2020-08-26 [1] CRAN (R 4.2.0)
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#> 
#> ──────────────────────────────────────────────────────────────────────────────

^{Created on 2022-05-31 by the reprex package (v2.0.1)}

[cb12991]

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()
#> ─ Session info ───────────────────────────────────────────────────────────────
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#> ──────────────────────────────────────────────────────────────────────────────

^{Created on 2022-05-31 by the reprex package (v2.0.1)}

[EmilHvitfeldt]

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)}

[topepo]

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.

[frankhezemans]

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?

[hfrick]

#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.

[hfrick]

@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!

[frankhezemans]

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!

[github-actions]

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