Predict with 'tidymodels' Workflows in Databases

Turn 'tidymodels' workflows into objects containing the sufficient sequential equations to perform predictions. These smaller objects allow for low dependency prediction locally or directly in databases.

orbital

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 - 3.596563) / 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 = 20.09063 + (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 3.51.2 []
#>    .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)
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").

Python Version

We have created a python version of orbital that works on on fitted scikit learn models.

contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

• For questions and discussions about tidymodels packages, modeling, and machine learning, please post on Posit Community.

• If you think you have encountered a bug, please submit an issue.

• Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

• Check out further details on contributing guidelines for tidymodels packages and how to get help.