agua

agua enables users to fit, optimize, and evaluate models via H2O using tidymodels syntax. Most users will not have to use aqua directly; the features can be accessed via the new parsnip computational engine 'h2o'.

There are two main components in agua:

• New parsnip engine 'h2o' for many models, see Get started for a complete list.

• Infrastructure for the tune package.

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")

Examples

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

library(tidymodels)
library(agua)
library(h2o)
tidymodels_prefer()

# Start the h2o server before running models
h2o_start()

# Demonstrate fitting parsnip models: 
# Specify the type of model and the h2o engine 
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
#> parsnip model object
#> 
#> Model Details:
#> ==============
#> 
#> H2ORegressionModel: drf
#> Model ID:  DRF_model_R_1656520956148_1 
#> Model Summary: 
#>   number_of_trees number_of_internal_trees model_size_in_bytes min_depth
#> 1            1000                     1000              285914         4
#>   max_depth mean_depth min_leaves max_leaves mean_leaves
#> 1        10    6.70600         10         27    18.04100
#> 
#> 
#> H2ORegressionMetrics: drf
#> ** Reported on training data. **
#> ** Metrics reported on Out-Of-Bag training samples **
#> 
#> MSE:  4.354249
#> RMSE:  2.086684
#> MAE:  1.657823
#> RMSLE:  0.09848976
#> Mean Residual Deviance :  4.354249

# Predictions
predict(mod, head(mtcars))
#> # A tibble: 6 × 1
#>   .pred
#>   <dbl>
#> 1  20.9
#> 2  20.8
#> 3  23.3
#> 4  20.4
#> 5  17.9
#> 6  18.7

# When done
h2o_end()

Before using the 'h2o' engine, users need to run agua::h2o_start() or h2o::h2o.init() to start the h2o server, which will be storing data, models, and other values passed from the R session.

There are several package vignettes including:

• Introduction to agua

• Model tuning

• Automatic machine learning

• Parallel processing with agua and h2o

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.