agua

agua enables users to fit, optimize, and evaluate models via h2o using a tidymodels interface.

Most users will not have to use aqua directly; the features can be accessed via a parsnip engine value of 'h2o'.

There are two main components in agua:

• parsnip engine definitions for the following models:
• Infrastructure for the tune and finetune packages (not yet implemented).

When fitting a parsnip model, the data are passed to the h2o server directly. For tuning, the data are passed once and instructions are given to h2o.grid() to process them.

This work is based on @stevenpawley’s h2oparsnip package. Additional work was done by Qiushi Yan for his 2022 summer internship at RStudio.

Installation

The CRAN version of the package can be installed via

install.packages("agua")

You can also install the development version of agua using:

require(pak)
pak::pak("tidymodels/agua")

Example

The following code demonstrates how to create a single model on the h2o server and how to make predictions.

library(tidymodels)
library(agua)

tidymodels_prefer()

# Start the h2o server before running models
logging <- capture.output(h2o::h2o.init())

# Demonstrate fitting parsnip models: 

if (h2o_running()) {

  # Specify the type of model
  spec <-
    rand_forest(mtry = 3, trees = 1000) %>%
    set_engine("h2o") %>%
    set_mode("regression")

  # Fit the model on the h2o server
  set.seed(1)
  mod <- fit(spec, mpg ~ ., data = mtcars)
  mod

  # Predictions
  predict(mod, head(mtcars))
  
  # When done
  h2o::h2o.shutdown(prompt = FALSE)
}

Code of Conduct

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