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orbital orbital website

R-CMD-check CRAN status Codecov test coverage Lifecycle: experimental

The goal of orbital is to enable running predictions of tidymodels workflows inside databases.

Installation

To install it, use:

install.packages("orbital")

You can install the development version of orbital from GitHub with:

# install.packages("devtools")
devtools::install_github("tidymodels/orbital")

Example

Given a fitted workflow

library(tidymodels)

rec_spec <- recipe(mpg ~ ., data = mtcars) |>
  step_normalize(all_numeric_predictors())

lm_spec <- linear_reg()

wf_spec <- workflow(rec_spec, lm_spec)

wf_fit <- fit(wf_spec, mtcars)

You can predict with it like normal.

predict(wf_fit, mtcars)
#> # A tibble: 32 × 1
#>    .pred
#>    <dbl>
#>  1  22.6
#>  2  22.1
#>  3  26.3
#>  4  21.2
#>  5  17.7
#>  6  20.4
#>  7  14.4
#>  8  22.5
#>  9  24.4
#> 10  18.7
#> # ℹ 22 more rows

We can get the same results by first creating an orbital object

library(orbital)
orbital_obj <- orbital(wf_fit)
orbital_obj
#> 
#> ── orbital Object ──────────────────────────────────────────────────────────────
#> • cyl = (cyl - 6.1875) / 1.785922
#> • disp = (disp - 230.7219) / 123.9387
#> • hp = (hp - 146.6875) / 68.56287
#> • drat = (drat - 3.596562) / 0.5346787
#> • wt = (wt - 3.21725) / 0.9784574
#> • qsec = (qsec - 17.84875) / 1.786943
#> • vs = (vs - 0.4375) / 0.5040161
#> • am = (am - 0.40625) / 0.4989909
#> • gear = (gear - 3.6875) / 0.7378041
#> • carb = (carb - 2.8125) / 1.6152
#> • .pred = 20.09062 + (cyl * -0.199024) + (disp * 1.652752) + (hp * -1.472 ...
#> ────────────────────────────────────────────────────────────────────────────────
#> 11 equations in total.

and then “predicting” with it using predict() to get the same results

predict(orbital_obj, as_tibble(mtcars))
#> # A tibble: 32 × 1
#>    .pred
#>    <dbl>
#>  1  22.6
#>  2  22.1
#>  3  26.3
#>  4  21.2
#>  5  17.7
#>  6  20.4
#>  7  14.4
#>  8  22.5
#>  9  24.4
#> 10  18.7
#> # ℹ 22 more rows

you can also predict in most SQL databases

library(DBI)
library(RSQLite)

con <- dbConnect(SQLite(), path = ":memory:")
db_mtcars <- copy_to(con, mtcars)

predict(orbital_obj, db_mtcars)
#> # Source:   SQL [?? x 1]
#> # Database: sqlite 3.47.0 []
#>    .pred
#>    <dbl>
#>  1  22.6
#>  2  22.1
#>  3  26.3
#>  4  21.2
#>  5  17.7
#>  6  20.4
#>  7  14.4
#>  8  22.5
#>  9  24.4
#> 10  18.7
#> # ℹ more rows

and spark databases

library(sparklyr)
#> 
#> Attaching package: 'sparklyr'
#> The following object is masked from 'package:purrr':
#> 
#>     invoke
#> The following object is masked from 'package:stats':
#> 
#>     filter
sc <- spark_connect(master = "local")

sc_mtcars <- copy_to(sc, mtcars, overwrite = TRUE)

predict(orbital_obj, sc_mtcars)
#> # Source:   SQL [?? x 1]
#> # Database: spark_connection
#>    .pred
#>    <dbl>
#>  1  22.6
#>  2  22.1
#>  3  26.3
#>  4  21.2
#>  5  17.7
#>  6  20.4
#>  7  14.4
#>  8  22.5
#>  9  24.4
#> 10  18.7
#> # ℹ more rows

Supported models and recipes steps

Full list of supported models and recipes steps can be found here: vignette("supported-models").

contributing

This 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.