tidypredict

the Deck fork

This is our fork of CRAN's tidypredict version 0.4.8. Our fork has 2 differences:

• fixes a bug in SQL query generation for xgboost logistic regression models. This bugfix has been merged into the development version of tidypredict on github, but is not on CRAN as of 2021-02-04.
• we still use our own fork because of a second difference, which is that our fork rounds the feature values in the SQL query for an xgboost model to 2 decimal places and the predictions to 4 decimal places. This is because BigQuery has a character limit on the queries it can execute, and we don't have time to waste characters on extraneous significant figures 😉

Checks for the original development version

Goals

The main goal of tidypredict is to enable running predictions inside databases. It reads the model, extracts the components needed to calculate the prediction, and then creates an R formula that can be translated into SQL. In other words, it is able to parse a model such as this one:

model <- lm(mpg ~ wt + cyl, data = mtcars)

tidypredict can return a SQL statement that is ready to run inside the database. Because it uses dplyr’s database interface, it works with several databases back-ends, such as MS SQL:

tidypredict_sql(model, dbplyr::simulate_mssql())

## <SQL> 39.6862614802529 + (`wt` * -3.19097213898374) + (`cyl` * -1.5077949682598)

Installation

Install tidypredict from CRAN using:

# install.packages("tidypredict")

Or install the development version using devtools as follows:

# install.packages("remotes")
# remotes::install_github("tidymodels/tidypredict")

Functions

tidypredict has only a few functions, and it is not expected that number to grow much. The main focus at this time is to add more models to support.

Function	Description
tidypredict_fit()	Returns an R formula that calculates the prediction
tidypredict_sql()	Returns a SQL query based on the formula from tidypredict_fit()
tidypredict_to_column()	Adds a new column using the formula from tidypredict_fit()
tidypredict_test()	Tests tidyverse predictions against the model’s native predict() function
tidypredict_interval()	Same as tidypredict_fit() but for intervals (only works with lm and glm)
tidypredict_sql_interval()	Same as tidypredict_sql() but for intervals (only works with lm and glm)
parse_model()	Creates a list spec based on the R model
as_parsed_model()	Prepares an object to be recognized as a parsed model

How it works

Instead of translating directly to a SQL statement, tidypredict creates an R formula. That formula can then be used inside dplyr. The overall workflow would be as illustrated in the image above, and described here:

1. Fit the model using a base R model, or one from the packages listed in Supported Models
2. tidypredict reads model, and creates a list object with the necessary components to run predictions
3. tidypredict builds an R formula based on the list object
4. dplyr evaluates the formula created by tidypredict
5. dplyr translates the formula into a SQL statement, or any other interfaces.
6. The database executes the SQL statement(s) created by dplyr

Parsed model spec

tidypredict writes and reads a spec based on a model. Instead of simply writing the R formula directly, splitting the spec from the formula adds the following capabilities:

1. No more saving models as .rds - Specifically for cases when the model needs to be used for predictions in a Shiny app.
2. Beyond R models - Technically, anything that can write a proper spec, can be read into tidypredict. It also means, that the parsed model spec can become a good alternative to using PMML.

Supported models

The following models are supported by tidypredict:

• Linear Regression - lm()
• Generalized Linear model - glm()
• Random Forest models - randomForest::randomForest()
• Random Forest models, via ranger - ranger::ranger()
• MARS models - earth::earth()
• XGBoost models - xgboost::xgb.Booster.complete()
• Cubist models - Cubist::cubist()
• Tree models, via partykit - partykit::ctree()

parsnip

tidypredict supports models fitted via the parsnip interface. The ones confirmed currently work in tidypredict are:

• lm() - parsnip: linear_reg() with “lm” as the engine.
• randomForest::randomForest() - parsnip: rand_forest() with “randomForest” as the engine.
• ranger::ranger() - parsnip: rand_forest() with “ranger” as the engine.
• earth::earth() - parsnip: mars() with “earth” as the engine.

broom

The tidy() function from broom works with linear models parsed via tidypredict

pm <- parse_model(lm(wt ~ ., mtcars))
tidy(pm)

## # A tibble: 11 x 2
##    term        estimate
##    <chr>          <dbl>
##  1 (Intercept) -0.231  
##  2 mpg         -0.0417 
##  3 cyl         -0.0573 
##  4 disp         0.00669
##  5 hp          -0.00323
##  6 drat        -0.0901 
##  7 qsec         0.200  
##  8 vs          -0.0664 
##  9 am           0.0184 
## 10 gear        -0.0935 
## 11 carb         0.249

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 RStudio 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.