ranger() model

Highlights & Limitations

How it works

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

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

The SQL translations returns a single SQL CASE WHEN operation. Each decision path is a WHEN statement.

library(tidypredict)

tidypredict_sql(model, dbplyr::simulate_mssql())
## <SQL> CASE
## WHEN ((((`Petal.Length`) < 2.45) AND ((`Petal.Width`) < 1.75))) THEN ('setosa')
## WHEN ((((`Petal.Width`) >= 1.75) AND ((`Petal.Length`) < 4.85))) THEN ('virginica')
## WHEN ((((`Petal.Length`) >= 4.85) AND ((`Petal.Width`) >= 1.75))) THEN ('virginica')
## WHEN (((((`Petal.Length`) >= 2.45) AND ((`Petal.Length`) < 5.4)) AND ((`Petal.Width`) < 1.75))) THEN ('versicolor')
## WHEN (((((`Petal.Length`) >= 5.4) AND ((`Petal.Length`) >= 2.45)) AND ((`Petal.Width`) < 1.75))) THEN ('virginica')
## END

Alternatively, use tidypredict_to_column() if the results are the be used or previewed in dplyr.

iris %>%
  tidypredict_to_column(model) %>%
  head(10)
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species    fit
## 1           5.1         3.5          1.4         0.2  setosa setosa
## 2           4.9         3.0          1.4         0.2  setosa setosa
## 3           4.7         3.2          1.3         0.2  setosa setosa
## 4           4.6         3.1          1.5         0.2  setosa setosa
## 5           5.0         3.6          1.4         0.2  setosa setosa
## 6           5.4         3.9          1.7         0.4  setosa setosa
## 7           4.6         3.4          1.4         0.3  setosa setosa
## 8           5.0         3.4          1.5         0.2  setosa setosa
## 9           4.4         2.9          1.4         0.2  setosa setosa
## 10          4.9         3.1          1.5         0.1  setosa setosa

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)
##   nodeID leftChild rightChild splitvarID splitvarName splitval terminal
## 1      0         1          2          4  Petal.Width     1.75    FALSE
## 2      1         3          4          3 Petal.Length     2.45    FALSE
## 3      2         5          6          3 Petal.Length     4.85    FALSE
## 4      3        NA         NA         NA         <NA>       NA     TRUE
## 5      4         7          8          3 Petal.Length     5.40    FALSE
## 6      5        NA         NA         NA         <NA>       NA     TRUE
## 7      6        NA         NA         NA         <NA>       NA     TRUE
## 8      7        NA         NA         NA         <NA>       NA     TRUE
## 9      8        NA         NA         NA         <NA>       NA     TRUE
##   prediction
## 1       <NA>
## 2       <NA>
## 3       <NA>
## 4     setosa
## 5       <NA>
## 6  virginica
## 7  virginica
## 8 versicolor
## 9  virginica

The parsed model contains one row for each path. The field, operator and split_point field list every step in a concatenated character variable.

parse_model(model)
## # A tibble: 6 x 7
##   labels vals       type     estimate field            operator   split_p…
##   <chr>  <fctr>     <chr>       <dbl> <chr>            <chr>      <chr>   
## 1 path-1 setosa     path            0 Petal.Length{:}… left{:}le… 2.45{:}…
## 2 path-2 virginica  path            0 Petal.Length{:}… left{:}ri… 4.85{:}…
## 3 path-3 virginica  path            0 Petal.Length{:}… right{:}r… 4.85{:}…
## 4 path-4 versicolor path            0 Petal.Length{:}… left{:}ri… 5.4{:}2…
## 5 path-5 virginica  path            0 Petal.Length{:}… right{:}r… 5.4{:}2…
## 6 model  ranger     variable       NA <NA>             <NA>       <NA>

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)
## case_when((((Petal.Length) < 2.45) & ((Petal.Width) < 1.75)) ~ 
##     "setosa", (((Petal.Width) >= 1.75) & ((Petal.Length) < 4.85)) ~ 
##     "virginica", (((Petal.Length) >= 4.85) & ((Petal.Width) >= 
##     1.75)) ~ "virginica", ((((Petal.Length) >= 2.45) & ((Petal.Length) < 
##     5.4)) & ((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:

How it performs

Currently, the formula matches 146 out of 150 prediction of the test model. The threshold in tidypredict_test() is a integer indicating the number of records are OK to be different than the baseline prediction that the predict() function returns.

test <- tidypredict_test(model, iris, threshold = 4)

test
## tidypredict test results
## 
## Success, test is under the set threshold of: 4
## Predictions that did not match predict(): 4
test$raw_results %>%
  filter(predict != tidypredict)
## # A tibble: 4 x 7
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    pred… tidy…
##          <dbl>       <dbl>        <dbl>       <dbl> <fctr>     <chr> <chr>
## 1         5.90        3.20         4.80        1.80 versicolor vers… virg…
## 2         4.90        2.50         4.50        1.70 virginica  virg… vers…
## 3         6.00        2.20         5.00        1.50 virginica  virg… vers…
## 4         6.30        2.80         5.10        1.50 virginica  virg… vers…