dmolitor / bolasso

Model consistent Lasso estimation through the bootstrap.

bolasso's Issues

Update GH Workflows for R CMD CHECK and pkgdown

Currently both are using v1 workflows which are deprecated and failing. Does not indicate anything wrong with the package, but needs to get updated at some point...

Great package!!! I love it!

don't change anything

`bolasso()` with `implement = "gamlr"` returns empty object when run in parallel

Will add a reprex below.

BoLasso Algorithm

Thanks for the package! I have a question about the algorithm.

In the paper the algorithm states that regularization parameter mu is set before the for loop and so every bootstrap sample is using the same one I think. In the algorithm you implemented, do you tune the penalty term for every bootstrap sample with k-fold cross validation?

I also tried the bootLassoOLS function from the HDCI package but get slightly different results. Your algorithm gives the best variable selection when fitting a OLS regression after though.

I also tried to implement the algorithm myself but also get different results so I am going wrong somewhere. Maybe I am getting the decay wrong. I am creating 128 bootsrtap samples and then find the best penalty term for each sample with 10 fold cross-validation and then refit the model with the best penalty term on the entire bootstrap sample again and record all the coefficients that are non zero.

Here is a code snippet of how it looks:

library(tidymodels)
library(tidyverse)
library(broom)

lasso_spec <- linear_reg(penalty = tune::tune(), mixture = 1) %>%
  set_engine("glmnet")

df_boot <- recipe_lasso %>% recipes::prep() %>% recipes::juice()
brain_folds <- rsample::bootstraps(df_boot, times = 128)
lambda_grid <- dials::grid_latin_hypercube(penalty(), size = 50)
  
res <- dplyr::tibble()
for (i in 1:nrow(brain_folds)) {

  df <- analysis(brain_folds$splits[[i]])
  recipe_lasso <- recipes::recipe(
    Cattell_TotalScore ~ ., data = df
  ) %>%
    recipes::step_dummy(all_nominal()) %>% 
    recipes::step_normalize(all_numeric(), -all_outcomes()) 
  
  wf_lasso <- workflows::workflow() %>%
    workflows::add_recipe(recipe_lasso) %>% 
    workflows::add_model(lasso_spec)
  
  folds <- rsample::vfold_cv(df, v = 10)
  
  lasso_grid <- tune::tune_grid(
    wf_lasso,
    metrics = metrics,
    resamples = folds,
    grid = lambda_grid
  )
  
  lowest_rmse <- lasso_grid %>%
    tune::select_best("rmse", maximize = FALSE)
  
  final_lasso <- finalize_workflow(
    wf_lasso,
    lowest_rmse
  ) %>% 
    parsnip::fit(df)
  
  res <- res %>% 
    dplyr::bind_rows(
      final_lasso %>%
        workflows::extract_fit_parsnip() %>% 
        broom::tidy() %>% 
        dplyr::filter(estimate != 0) %>% 
        dplyr::mutate(boot = paste0("boot: ", i))
    )
  print(i)
  
}

res %>% 
  dplyr::group_by(term) %>% 
  dplyr::summarise(
    n = dplyr::n(),
    avg = mean(estimate)
  ) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(prop = n/128) %>% 
  dplyr::filter(prop >= 0.98)

So my question would just be if you could just in 4-5 bullet points explain how you implemented the algorithm thta would be great so I can understand what was wrong with my code better. Thank you.

Parallelization across models in `coef` and `predict`

Currently, bolasso is implemented to estimate bootstrapped Lasso models in parallel. I want to extend this to the coef and predict methods as well. Although the performance gains are probably much, much smaller for both of these cases, it makes it more consistent and, in the case of extremely large data, could improve performance a fair amount.