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# brulee <a href="https://brulee.tidymodels.org/"><img src="man/figures/logo.png" align="right" height="139" alt="a dish of creme brulee on a striped background" /></a>

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

* [neural networks](https://brulee.tidymodels.org/reference/brulee_mlp.html)
* [linear regression](https://brulee.tidymodels.org/reference/brulee_linear_reg.html)
* [logistic regression](https://brulee.tidymodels.org/reference/brulee_logistic_reg.html)
* [multinomial regression](https://brulee.tidymodels.org/reference/brulee_multinomial_reg.html)
* [residual networks (ResNet)](https://brulee.tidymodels.org/reference/brulee_resnet.html)
* [regularization learning networks (RLN)](https://brulee.tidymodels.org/reference/brulee_rln.html)
* [AutoInt](https://brulee.tidymodels.org/reference/brulee_auto_int.html)
* [Self-Attention and Inter-sample Attention Transformer (Saint)](https://brulee.tidymodels.org/reference/brulee_saint.html)
* [Chronos2](https://brulee.tidymodels.org/reference/brulee_chronos.html) foundational model for forecasting
* Transformer-based foundation model TabICL

Chronos2 and TabICL are pretrained models, requiring a one-time download of about 500MB and 400MB, respectively. 

## Installation

You can install the released version of brulee from [CRAN](https://CRAN.R-project.org) with:

``` r
install.packages("brulee")
```

And the development version from [GitHub](https://github.com/tidymodels/brulee) with:

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

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



``` r
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, 
                         hidden_units = 3)

# We use the tidymodels semantics to always return a tibble when predicting
predict(nn_log_biv, bivariate_test, type = "prob") 
#> # A tibble: 710 × 2
#>    .pred_One .pred_Two
#>        <dbl>     <dbl>
#>  1     0.675     0.325
#>  2     0.673     0.327
#>  3     0.679     0.321
#>  4     0.688     0.312
#>  5     0.685     0.315
#>  6     0.679     0.321
#>  7     0.674     0.326
#>  8     0.681     0.319
#>  9     0.697     0.303
#> 10     0.675     0.325
#> # ℹ 700 more rows
```

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


``` r
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         0.867
```

## Code of Conduct
  
Please note that the brulee project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

