shapper is an R package which ports the
shap python library in R. For details and examples see shapper repository on github and shapper website.
SHAP (SHapley Additive exPlanations) is a method to explain predictions of any machine learning model. For more details about this method see shap repository on github.
To run shapper python library shap is required. It can be installed both by python or R. To install it throught R, you an use function
install_shap from the
The example usage is presented on the
titanic dataset form the R package
library("DALEX") titanic_train <- titanic[,c("survived", "class", "gender", "age", "sibsp", "parch", "fare", "embarked")] titanic_train$survived <- factor(titanic_train$survived) titanic_train$gender <- factor(titanic_train$gender) titanic_train$embarked <- factor(titanic_train$embarked) titanic_train <- na.omit(titanic_train) head(titanic_train)
library("randomForest") set.seed(123) model_rf <- randomForest(survived ~ . , data = titanic_train) model_rf
Let's assume that we want to explain the prediction of a particular observation (male, 8 years old, traveling 1-st class embarked at C, without parents and siblings.
new_passanger <- data.frame( class = factor("1st", levels = c("1st", "2nd", "3rd", "deck crew", "engineering crew", "restaurant staff", "victualling crew")), gender = factor("male", levels = c("female", "male")), age = 8, sibsp = 0, parch = 0, fare = 72, embarked = factor("Cherbourg", levels = c("Belfast", "Cherbourg", "Queenstown", "Southampton")) )
To use the function
shap() function (alias for
individual_variable_effect()) we need four elements
shap() function can be used directly with these four arguments, but for the simplicity here we are using the DALEX package with preimplemented predict functions.
library("DALEX") exp_rf <- explain(model_rf, data = titanic_train[,-1], y = as.numeric(titanic_train[,1])-1)
The explainer is an object that wraps up a model and meta-data. Meta data consists of, at least, the data set used to fit model and observations to explain.
And now it's enough to generate SHAP attributions with explainer for RF model.
library("shapper") ive_rf <- shap(exp_rf, new_observation = new_passanger) ive_rf