High-Level Modeling Functions with 'torch'.

brulee

The R brulee package contains several basic modeling functions that use the torch package infrastructure, such as:

• neural networks
• linear regression
• logistic regression
• multinomial regression

Installation

You can install the released version of brulee from CRAN with:

install.packages("brulee")

And the development version from GitHub with:

# install.packages("pak")
pak::pak("tidymodels/brulee")

Example

brulee has formula, x/y, and recipe user interfaces for each function. For example:

library(brulee)
library(recipes)
library(yardstick)

data(bivariate, package = "modeldata")
set.seed(20)
nn_log_biv <- brulee_mlp(Class ~ log(A) + log(B), data = bivariate_train, 
                         epochs = 150, hidden_units = 3)

# We use the tidymodels semantics to always return a tibble when predicting
predict(nn_log_biv, bivariate_test, type = "prob") %>% 
  bind_cols(bivariate_test) %>% 
  roc_auc(Class, .pred_One)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 roc_auc binary         0.837

A recipe can also be used if the data require some sort of preprocessing (e.g., indicator variables, transformations, or standardization):

library(recipes)

rec <- 
  recipe(Class ~ ., data = bivariate_train) %>%  
  step_YeoJohnson(all_numeric_predictors()) %>% 
  step_normalize(all_numeric_predictors())

set.seed(20)
nn_rec_biv <- brulee_mlp(rec, data = bivariate_train, 
                         epochs = 150, hidden_units = 3)

# A little better
predict(nn_rec_biv, bivariate_test, type = "prob") %>% 
  bind_cols(bivariate_test) %>% 
  roc_auc(Class, .pred_One)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 roc_auc binary         0.866

Code of Conduct

Please note that the brulee project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.