Random Forest, using Ranger • tidypredict

Function	Works
`tidypredict_fit()`, `tidypredict_sql()`, `parse_model()`	✔
`tidypredict_to_column()`	✗
`tidypredict_test()`	✗
`tidypredict_interval()`, `tidypredict_sql_interval()`	✗
`parsnip`	✔

How it works

Here is a simple ranger() model using the iris dataset:

library(dplyr)
library(tidypredict)
library(ranger)

model <- ranger(Species ~ ., data = iris, num.trees = 100)

Under the hood

The parser is based on the output from the ranger::treeInfo() function. It will return as many decision paths as there are non-NA rows in the prediction field.

treeInfo(model) %>%
  head()
#>   nodeID leftChild rightChild splitvarID splitvarName splitval terminal
#> 1      0         1          2          3  Petal.Width     1.75    FALSE
#> 2      1         3          4          2 Petal.Length     2.45    FALSE
#> 3      2         5          6          2 Petal.Length     4.85    FALSE
#> 4      3        NA         NA         NA         <NA>       NA     TRUE
#> 5      4         7          8          2 Petal.Length     5.40    FALSE
#> 6      5        NA         NA         NA         <NA>       NA     TRUE
#>   prediction
#> 1       <NA>
#> 2       <NA>
#> 3       <NA>
#> 4     setosa
#> 5       <NA>
#> 6  virginica

The output from parse_model() is transformed into a dplyr, a.k.a Tidy Eval, formula. The entire decision tree becomes one dplyr::case_when() statement

tidypredict_fit(model)[1]
#> [[1]]
#> case_when(Petal.Length < 2.45 & Petal.Width < 1.75 ~ "setosa", 
#>     Petal.Length < 4.85 & Petal.Width >= 1.75 ~ "virginica", 
#>     Petal.Length >= 4.85 & Petal.Width >= 1.75 ~ "virginica", 
#>     Petal.Length < 5.4 & Petal.Length >= 2.45 & Petal.Width < 
#>         1.75 ~ "versicolor", Petal.Length >= 5.4 & Petal.Length >= 
#>         2.45 & Petal.Width < 1.75 ~ "virginica")

From there, the Tidy Eval formula can be used anywhere where it can be operated. tidypredict provides three paths:

Use directly inside dplyr, mutate(iris, !! tidypredict_fit(model))
Use tidypredict_to_column(model) to a piped command set
Use tidypredict_to_sql(model) to retrieve the SQL statement

parsnip

tidypredict also supports ranger model objects fitted via the parsnip package.

library(parsnip)

parsnip_model <- rand_forest(mode = "classification") %>%
  set_engine("ranger") %>%
  fit(Species ~ ., data = iris)

tidypredict_fit(parsnip_model)[[1]]
#> case_when(Petal.Width < 0.8 ~ "setosa", Petal.Length >= 5.05 & 
#>     Petal.Width >= 0.8 ~ "virginica", Petal.Width < 1.65 & Petal.Length < 
#>     4.75 & Petal.Length < 5.05 & Petal.Width >= 0.8 ~ "versicolor", 
#>     Petal.Width >= 1.65 & Petal.Length < 4.75 & Petal.Length < 
#>         5.05 & Petal.Width >= 0.8 ~ "virginica", Sepal.Length >= 
#>         6.5 & Petal.Length >= 4.75 & Petal.Length < 5.05 & Petal.Width >= 
#>         0.8 ~ "versicolor", Sepal.Width < 3.1 & Sepal.Length < 
#>         6.5 & Petal.Length >= 4.75 & Petal.Length < 5.05 & Petal.Width >= 
#>         0.8 ~ "virginica", Sepal.Width >= 3.1 & Sepal.Length < 
#>         6.5 & Petal.Length >= 4.75 & Petal.Length < 5.05 & Petal.Width >= 
#>         0.8 ~ "versicolor")