Science Score: 13.0%
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✓codemeta.json file
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○.zenodo.json file
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○Scientific vocabulary similarity
Low similarity (18.4%) to scientific vocabulary
Last synced: 9 months ago
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JSON representation
Repository
Create and evaluate models using 'tidymodels' and 'h2o'
Basic Info
- Host: GitHub
- Owner: tidymodels
- License: other
- Language: R
- Default Branch: main
- Homepage: https://agua.tidymodels.org
- Size: 7.37 MB
Statistics
- Stars: 23
- Watchers: 7
- Forks: 2
- Open Issues: 6
- Releases: 3
Created about 4 years ago
· Last pushed 11 months ago
Metadata Files
Readme
Changelog
License
Code of conduct
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# agua
[](https://app.codecov.io/gh/tidymodels/agua?branch=main)
[](https://github.com/tidymodels/agua/actions/workflows/R-CMD-check.yaml)
agua enables users to fit, optimize, and evaluate models via [H2O](https://h2o.ai/) 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](https://agua.tidymodels.org/articles/agua.html) 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](https://github.com/stevenpawley/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
```r
install.packages("agua")
```
You can also install the development version of agua using:
``` r
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.
```r
library(tidymodels)
library(agua)
library(h2o)
tidymodels_prefer()
```
```r
# 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
#>
#> 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](https://agua.tidymodels.org/articles/agua.html)
- [Model tuning](https://agua.tidymodels.org/articles/tune.html)
- [Automatic machine learning](https://agua.tidymodels.org/articles/auto_ml.html)
- [Parallel processing with agua and h2o](https://agua.tidymodels.org/articles/parallel.html)
## Code of Conduct
Please note that the agua 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.
Owner
- Name: tidymodels
- Login: tidymodels
- Kind: organization
- Repositories: 59
- Profile: https://github.com/tidymodels
GitHub Events
Total
- Issues event: 1
- Watch event: 1
- Push event: 6
- Create event: 1
Last Year
- Issues event: 1
- Watch event: 1
- Push event: 6
- Create event: 1
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 27
- Total pull requests: 29
- Average time to close issues: 2 months
- Average time to close pull requests: 11 days
- Total issue authors: 9
- Total pull request authors: 5
- Average comments per issue: 2.41
- Average comments per pull request: 1.38
- Merged pull requests: 24
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 1
- Pull request authors: 0
- Average comments per issue: 0.0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- topepo (12)
- qiushiyan (7)
- simonpcouch (2)
- coforfe (1)
- jeliason (1)
- TheMadHatter666 (1)
- hfrick (1)
- gouthaman87 (1)
- EmilHvitfeldt (1)
Pull Request Authors
- qiushiyan (16)
- simonpcouch (5)
- topepo (5)
- hfrick (3)
- gvelasq (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
-
Total downloads:
- cran 909 last-month
- Total docker downloads: 8
-
Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 0
(may contain duplicates) - Total versions: 9
- Total maintainers: 1
proxy.golang.org: github.com/tidymodels/agua
- Documentation: https://pkg.go.dev/github.com/tidymodels/agua#section-documentation
- License: other
-
Latest release: v0.1.4
published about 2 years ago
Rankings
Dependent packages count: 5.5%
Average: 5.7%
Dependent repos count: 5.9%
Last synced:
10 months ago
cran.r-project.org: agua
'tidymodels' Integration with 'h2o'
- Homepage: https://agua.tidymodels.org/
- Documentation: http://cran.r-project.org/web/packages/agua/agua.pdf
- License: MIT + file LICENSE
-
Latest release: 0.1.4
published about 2 years ago
Rankings
Stargazers count: 13.8%
Downloads: 19.5%
Average: 25.5%
Forks count: 28.8%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Maintainers (1)
Last synced:
10 months ago
Dependencies
.github/workflows/R-CMD-check.yaml
actions
- actions/checkout v3 composite
- r-lib/actions/check-r-package v2 composite
- r-lib/actions/setup-pandoc v2 composite
- r-lib/actions/setup-r v2 composite
- r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/lock.yaml
actions
- dessant/lock-threads v2 composite
.github/workflows/pkgdown.yaml
actions
- JamesIves/github-pages-deploy-action v4.4.1 composite
- actions/checkout v3 composite
- r-lib/actions/setup-pandoc v2 composite
- r-lib/actions/setup-r v2 composite
- r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/pr-commands.yaml
actions
- actions/checkout v3 composite
- r-lib/actions/pr-fetch v2 composite
- r-lib/actions/pr-push v2 composite
- r-lib/actions/setup-r v2 composite
- r-lib/actions/setup-r-dependencies v2 composite
.github/workflows/test-coverage.yaml
actions
- actions/checkout v3 composite
- actions/upload-artifact v3 composite
- r-lib/actions/setup-r v2 composite
- r-lib/actions/setup-r-dependencies v2 composite
DESCRIPTION
cran
- parsnip * depends
- cli * imports
- dials * imports
- dplyr * imports
- generics >= 0.1.3 imports
- ggplot2 * imports
- glue * imports
- h2o >= 3.38.0.1 imports
- hardhat >= 1.1.0 imports
- methods * imports
- pkgconfig * imports
- purrr * imports
- rlang * imports
- rsample * imports
- stats * imports
- tibble * imports
- tidyr * imports
- tune >= 1.0.1 imports
- vctrs * imports
- workflows * imports
- covr * suggests
- knitr * suggests
- modeldata * suggests
- recipes * suggests
- rmarkdown * suggests
- testthat >= 3.0.0 suggests