ranger::treeInfo()
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 ranger()
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` < 2.45 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
.
## 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 ranger::treeInfo()
function. It will return as many decision paths as there are non-NA rows in the prediction
field.
## 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.
## # A tibble: 6 x 7
## labels vals type estimate field operator split_point
## <chr> <fct> <chr> <dbl> <chr> <chr> <chr>
## 1 path-1 setosa path 0 Petal.Lengt… left{:}le… 2.45{:}1.75
## 2 path-2 virginica path 0 Petal.Lengt… left{:}ri… 4.85{:}1.75
## 3 path-3 virginica path 0 Petal.Lengt… right{:}r… 4.85{:}1.75
## 4 path-4 versicolor path 0 Petal.Lengt… left{:}ri… 5.4{:}2.45{…
## 5 path-5 virginica path 0 Petal.Lengt… right{:}r… 5.4{:}2.45{…
## 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
## 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:
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 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.
## tidypredict test results
##
## Success, test is under the set threshold of: 4
## Predictions that did not match predict(): 4
## # A tibble: 4 x 7
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species predict
## <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 5.9 3.2 4.8 1.8 versicolor versicolor
## 2 4.9 2.5 4.5 1.7 virginica virginica
## 3 6 2.2 5 1.5 virginica virginica
## 4 6.3 2.8 5.1 1.5 virginica virginica
## # ... with 1 more variable: tidypredict <chr>