The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The R brulee package contains several basic modeling functions that use the torch package infrastructure, such as:
You can install the released version of brulee from CRAN with:
And the development version from GitHub with:
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 0.837A 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 0.866Please 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.