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 titanic.

library("titanic")
titanic <- titanic_train[,c("Survived", "Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked")]
titanic$Survived <- factor(titanic$Survived)
titanic$Sex <- factor(titanic$Sex)
titanic$Embarked <- factor(titanic$Embarked)
titanic <- na.omit(titanic)
head(titanic)

  Survived Pclass    Sex Age SibSp Parch    Fare Embarked
1        0      3   male  22     1     0  7.2500        S
2        1      1 female  38     1     0 71.2833        C
3        1      3 female  26     0     0  7.9250        S
4        1      1 female  35     1     0 53.1000        S
5        0      3   male  35     0     0  8.0500        S
7        0      1   male  54     0     0 51.8625        S

Let's build a model

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

Call:
 randomForest(formula = Survived ~ ., data = titanic) 
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 2

        OOB estimate of  error rate: 18.63%
Confusion matrix:
    0   1 class.error
0 384  40  0.09433962
1  93 197  0.32068966

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(
            Pclass = 1,
            Sex = factor("male", levels = c("female", "male")),
            Age = 8,
            SibSp = 0,
            Parch = 0,
            Fare = 72,
            Embarked = factor("C", levels = c("","C","Q","S"))
)

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[,-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

    Pclass  Sex Age SibSp Parch Fare Embarked _id_ _ylevel_ _yhat_ _yhat_mean_ _vname_ _attribution_ _sign_      _label_
1        1 male   8     0     0   72        C    1        0  0.442   0.6327059  Pclass  -0.070047752      - randomForest
1.2      1 male   8     0     0   72        C    1        0  0.442   0.6327059     Sex   0.154519708      + randomForest
1.3      1 male   8     0     0   72        C    1        0  0.442   0.6327059     Age  -0.143046212      - randomForest
1.4      1 male   8     0     0   72        C    1        0  0.442   0.6327059   SibSp  -0.003154522      - randomForest
1.5      1 male   8     0     0   72        C    1        0  0.442   0.6327059   Parch   0.018111585      + randomForest
1.6      1 male   8     0     0   72        C    1        0  0.442   0.6327059    Fare  -0.086728705      - randomForest

Plotting results

plot(ive_rf)