---
title: "Basic Workflow"
description: >
  This vignette describes the basic workflow of SHAPforxgboost.
bibliography: "biblio.bib"
link-citations: true
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Basic Workflow}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  fig.width = 5,
  fig.height = 4
)
```

## Introduction

This vignette shows the basic workflow of using `SHAPforxgboost` for interpretation of models trained with `XGBoost`, a hightly efficient gradient boosting implementation [@chen2016].

```{r setup}
library("ggplot2")
library("SHAPforxgboost")
library("xgboost")

set.seed(9375)
```

## Training the model

Let's train a small model to predict the first column in the iris data set, namely `Sepal.Length`.

```{r}
head(iris)

X <- data.matrix(iris[, -1])
dtrain <- xgb.DMatrix(X, label = iris[[1]])

fit <- xgb.train(
  params = list(
    objective = "reg:squarederror",
    learning_rate = 0.1
  ), 
  data = dtrain,
  nrounds = 50
)

```

## SHAP analysis

Now, we can prepare the SHAP values and analyze the results. All this in just very few lines of code!

```{r}
# Crunch SHAP values
shap <- shap.prep(fit, X_train = X)

# SHAP importance plot
shap.plot.summary(shap)

# Alternatively, mean absolute SHAP values
shap.plot.summary(shap, kind = "bar")

# Dependence plots in decreasing order of importance
# (colored by strongest interacting variable)
for (x in shap.importance(shap, names_only = TRUE)) {
  p <- shap.plot.dependence(
    shap, 
    x = x, 
    color_feature = "auto", 
    smooth = FALSE, 
    jitter_width = 0.01, 
    alpha = 0.4
    ) +
  ggtitle(x)
  print(p)
}

```

*Note: `print` is required only in the context of using `ggplot` in `rmarkdown` and for loop.*

This is just a teaser: `SHAPforxgboost` can do much more! Check out the README for much more information.

## References


.
