Introduction

The 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.

Install shaper and shap

R package shapper

library("shapper")

Python library shap

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 shapper package.

shapper::install_shap()

Load data sets

The example usage is presented on the titanic dataset form the R package DALEX.

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)

Let’s build a model

library("randomForest")
set.seed(123)
model_rf <- randomForest(survived ~ . , data = titanic_train)
model_rf

Prediction to be explained

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"))
)

Here shapper starts

To use the function shap() function (alias for individual_variable_effect()) we need four elements

a model,
a data set,
a function that calculated scores (predict function),
an instance (or instances) to be explained.

The 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

Plotting results

plot(ive_rf)

How to use shapper for regression