agua allows users to fit and tune models using the H2O platform with tidymodels syntax. The package provides a new parsnip computational engine 'h2o' for various models and sets up additional infrastructure for tune.
Details
The package uses code initially written by Steven Pawley in his h2oparsnip package. Addition work was done by Qiushi Yan as a Posit summer intern.
There are two main components in agua:
New parsnip engine
'h2o'for many models, see the vignette 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 Posit.
Installation
The CRAN version of the package can be installed via
install.packages("agua")You can also install the development version of agua using:
Examples
The following code demonstrates how to create a single model on the h2o server and how to make predictions.
library(tidymodels)
library(agua)
# 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_1665517828283_1
#> Model Summary:
#> number_of_trees number_of_internal_trees model_size_in_bytes min_depth
#> 1 1000 1000 285916 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: 4.354
#> RMSE: 2.087
#> MAE: 1.658
#> RMSLE: 0.09849
#> Mean Residual Deviance : 4.354
# 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:
Author
Maintainer: Qiushi Yan qiushi.yann@gmail.com
Authors:
Max Kuhn max@posit.co (ORCID)
Steven Pawley dr.stevenpawley@gmail.com
Other contributors:
Posit Software, PBC [copyright holder, funder]