randomForest::getTree()
to parse each decision path.wt ~ mpg + am
mutate(mtcars, newam = paste0(am))
and then wt ~ mpg + newam
wt ~ mpg + as.factor(am)
wt ~ mpg + as.character(am)
tidypredict_interval()
& tidypredict_sql_interval()
Here is a simple randomForest()
model using the iris
dataset:
The SQL translations returns a single SQL CASE WHEN
operation. Each decision path is a WHEN
statement.
## <SQL> CASE
## WHEN ((`Petal.Length` <= 2.5)) THEN ('setosa')
## WHEN ((`Petal.Length` > 5.05 AND `Petal.Length` > 2.5)) THEN ('virginica')
## WHEN ((`Petal.Width` > 1.9 AND `Petal.Length` > 2.5 AND `Petal.Length` <= 5.05)) THEN ('virginica')
## WHEN ((`Petal.Length` > 2.5 AND `Sepal.Length` <= 4.95 AND `Petal.Width` <= 1.9 AND `Petal.Length` <= 5.05)) THEN ('virginica')
## WHEN ((`Sepal.Length` > 4.95 AND `Petal.Length` > 2.5 AND `Petal.Width` <= 1.75 AND `Petal.Width` <= 1.9 AND `Petal.Length` <= 5.05)) THEN ('versicolor')
## WHEN ((`Petal.Width` > 1.75 AND `Sepal.Length` > 4.95 AND `Petal.Length` > 2.5 AND `Sepal.Width` <= 3.0 AND `Petal.Width` <= 1.9 AND `Petal.Length` <= 5.05)) THEN ('virginica')
## WHEN ((`Sepal.Width` > 3.0 AND `Petal.Width` > 1.75 AND `Sepal.Length` > 4.95 AND `Petal.Length` > 2.5 AND `Petal.Width` <= 1.9 AND `Petal.Length` <= 5.05)) THEN ('versicolor')
## END
Alternatively, use tidypredict_to_column()
if the results are the be used or previewed in dplyr
.
## 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
The parser is based on the output from the randomForest::getTree()
function. It will return as many decision paths as there are non-NA rows in the prediction
field.
## left daughter right daughter split var split point status prediction
## 1 2 3 Petal.Length 2.50 1 <NA>
## 2 0 0 <NA> 0.00 -1 setosa
## 3 4 5 Petal.Length 5.05 1 <NA>
## 4 6 7 Petal.Width 1.90 1 <NA>
## 5 0 0 <NA> 0.00 -1 virginica
## 6 8 9 Sepal.Length 4.95 1 <NA>
The parsed model contains one row for each path. The field
, operator
and split_point
field list every step in a concatenated character variable.
## # A tibble: 8 x 7
## labels vals type estimate field operator split_point
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 path-1 setosa path 0 Petal.Len… left 2.5
## 2 path-2 virginica path 0 Petal.Len… right{:}r… 5.05{:}2.5
## 3 path-3 virginica path 0 Petal.Wid… right{:}l… 1.9{:}5.05{…
## 4 path-4 virginica path 0 Sepal.Len… left{:}le… 4.95{:}1.9{…
## 5 path-5 versicolor path 0 Petal.Wid… left{:}ri… 1.75{:}4.95…
## 6 path-6 virginica path 0 Sepal.Wid… left{:}ri… 3{:}1.75{:}…
## 7 path-7 versicolor path 0 Sepal.Wid… right{:}r… 3{:}1.75{:}…
## 8 model randomForest 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
## case_when((Petal.Length <= 2.5) ~ "setosa", (Petal.Length > 5.05 &
## Petal.Length > 2.5) ~ "virginica", (Petal.Width > 1.9 & Petal.Length >
## 2.5 & Petal.Length <= 5.05) ~ "virginica", (Petal.Length >
## 2.5 & Sepal.Length <= 4.95 & Petal.Width <= 1.9 & Petal.Length <=
## 5.05) ~ "virginica", (Sepal.Length > 4.95 & Petal.Length >
## 2.5 & Petal.Width <= 1.75 & Petal.Width <= 1.9 & Petal.Length <=
## 5.05) ~ "versicolor", (Petal.Width > 1.75 & Sepal.Length >
## 4.95 & Petal.Length > 2.5 & Sepal.Width <= 3 & Petal.Width <=
## 1.9 & Petal.Length <= 5.05) ~ "virginica", (Sepal.Width >
## 3 & Petal.Width > 1.75 & Sepal.Length > 4.95 & Petal.Length >
## 2.5 & Petal.Width <= 1.9 & Petal.Length <= 5.05) ~ "versicolor")
From there, the Tidy Eval formula can be used anywhere where it can be operated. tidypredict
provides three paths:
dplyr
, mutate(iris, !! tidypredict_fit(model))
tidypredict_to_column(model)
to a piped command settidypredict_to_sql(model)
to retrieve the SQL statementCurrently, the formula matches 147 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.
## tidypredict test results
##
## Success, test is under the set threshold of: 5
## Predictions that did not match predict(): 3
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species predict
## 1 4.9 2.4 3.3 1.0 versicolor versicolor
## 2 6.0 2.7 5.1 1.6 versicolor versicolor
## 3 6.0 2.2 5.0 1.5 virginica virginica
## tidypredict
## 1 virginica
## 2 virginica
## 3 versicolor