# 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)