Is there a way to tune_grid() a glmnet without resamples?

Below is my current code, where

  • enet is elastic net model
  • recip is recipe
  • pen_mix_grid is different combinations of penalty and mixture values for glmnet, and
  • folds is a vfold_cv() resample
    outputt = tune_grid(enet,
                        preprocessor = recip,
                        grid = pen_mix_grid,
                        resamples = folds)

I would like it to only try different penalty and mixture values of glmnet, so I tried resamples = NULL and removing the argument without success. How can I make it ignore resamples argument?

What data would you like to model and what data would you like to predict?

Thanks for the reply.

Data would be df, and the formula would be y ~ ., both of which are included in recip.

I should rephrase: which rows of the data do you want for prediction and modeling?

tidymodel avoids predicting on data that was used to fit a model since it can easily lead to overfitting. Almost all of our resampling objects (which are required here) separate the two data sets.

Oh I see -- df is already the train data without test data (let's say df_train and df_test). I would like it to fit on all data (train + test, say df_all). Please let me know if that answers your question.

Sorry, before your questions, I guess I didn't quite understand the relevance, but I should have anticipated this. It makes a bit more sense why NULL resamples argument doesn't work.

Here are some options (clearly I prefer one :smiley:)

#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip

# best: subsplit the training data to use a validation set
resample <- validation_split(mtcars)
#> # Validation Set Split (0.75/0.25)  
#> # A tibble: 1 × 2
#>   splits         id        
#>   <list>         <chr>     
#> 1 <split [24/8]> validation

# not great: put the training set in both training and test. 
# we'll do this via bootstrapping with an extra option
resample <- 
  bootstraps(mtcars, times = 1, apparent = TRUE) %>% 
  filter(id == "Apparent")
#> # A tibble: 1 × 2
#>   splits          id      
#>   <list>          <chr>   
#> 1 <split [32/32]> Apparent

res <- 
  linear_reg() %>% 
  # Same code works for tune_grid
  fit_resamples(mpg ~ ., resample = apparent(mtcars))

Created on 2021-09-14 by the reprex package (v2.0.0)

Hmm I'm having trouble understanding this.

So let's say glmnet goes into this. Where would penalty and mixture be brute forced?

If you want to tune them give them a value of tune() and use tune_grid() as before.

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Just for future reference --

I'm just discovering that when using vfold_cv() or sliding_window() etc. instead of bootstrap with the same code, the output is model weights, not covariate weights. I guess only some resampling methods or only bootstrap returns covariate weights. Bit of a bummer, but oh well, bootstrapping it is.

Thanks a million

If you want the coefficients from the fitted models, you can get those by extracting the models. I.e. something like the control = control_grid( extract = function (x) x) ) in tune_grid (if this gets too big, I think you could butcher what you return further to only keep the coefficients). Then you can look at tune_results_object$.extracts and process that further. Or is that not what you meant?

We just wrote an article about this.

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In the article Learn - Working with model coefficients

But wait! We know that each glmnet fit contains all of the coefficients. This means, for a specific resample and value of mixture , the results are the same:

  # First bootstrap, first `mixture`, first `penalty`
  # First bootstrap, first `mixture`, second `penalty`
#> [1] TRUE

I've been trying to figure out the reason for the lack of change with different values of penalty as mentioned above, but I haven't had much luck. Why would different values of penalty result in the same coefficients? In the last graph, it seems to show different values for different penalties:

glmnet models produce coefficients for all value of the penalty for each model fit. The infrastructure in tidymodels gives a row for each penalty but the tidy method produces coefficients for all of them, so there will be replicate values. Take a look at this document for more details.

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