shap.prep: Prepare SHAP values into long format for plotting

Description Usage Arguments Details Value Examples

View source: R/SHAP_funcs.R

Description

Produce a dataset of 6 columns: ID of each observation, variable name, SHAP value, variable values (feature value), deviation of the feature value for each observation (for coloring the point), and the mean SHAP values for each variable. You can view this example dataset included in the package: shap_long_iris

Usage

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shap.prep(
  xgb_model = NULL,
  shap_contrib = NULL,
  X_train,
  top_n = NULL,
  var_cat = NULL
)

Arguments

xgb_model

an XGBoost (or LightGBM) model object, will derive the SHAP values from it

shap_contrib

optional to directly supply a SHAP values dataset. If supplied, it will overwrite the xgb_model if xgb_model is also supplied

X_train

the dataset of predictors used to calculate SHAP values, it provides feature values to the plot, must be supplied

top_n

to choose top_n variables ranked by mean|SHAP| if needed

var_cat

if supplied, will provide long format data, grouped by this categorical variable

Details

The ID variable is added for each observation in the shap_contrib dataset for better tracking, it is created as 1:nrow(shap_contrib) before melting shap_contrib into long format.

Value

a long-format data.table, named as shap_long

Examples

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data("iris")
X1 = as.matrix(iris[,-5])
mod1 = xgboost::xgboost(
  data = X1, label = iris$Species, gamma = 0, eta = 1,
  lambda = 0, nrounds = 1, verbose = FALSE)

# shap.values(model, X_dataset) returns the SHAP
# data matrix and ranked features by mean|SHAP|
shap_values <- shap.values(xgb_model = mod1, X_train = X1)
shap_values$mean_shap_score
shap_values_iris <- shap_values$shap_score

# shap.prep() returns the long-format SHAP data from either model or
shap_long_iris <- shap.prep(xgb_model = mod1, X_train = X1)
# is the same as: using given shap_contrib
shap_long_iris <- shap.prep(shap_contrib = shap_values_iris, X_train = X1)

# **SHAP summary plot**
shap.plot.summary(shap_long_iris, scientific = TRUE)
shap.plot.summary(shap_long_iris, x_bound  = 1.5, dilute = 10)

# Alternatives options to make the same plot:
# option 1: from the xgboost model
shap.plot.summary.wrap1(mod1, X = as.matrix(iris[,-5]), top_n = 3)

# option 2: supply a self-made SHAP values dataset
# (e.g. sometimes as output from cross-validation)
shap.plot.summary.wrap2(shap_score = shap_values_iris, X = X1, top_n = 3)
####
#
# use `var_cat` to add a categorical variable, output the long-format data differently:
library("data.table")
data("iris")
set.seed(123)
iris$Group <- 0
iris[sample(1:nrow(iris), nrow(iris)/2), "Group"] <- 1

data.table::setDT(iris)
X_train = as.matrix(iris[,c(colnames(iris)[1:4], "Group"), with = FALSE])
mod1 = xgboost::xgboost(
  data = X_train, label = iris$Species, gamma = 0, eta = 1,
  lambda = 0, nrounds = 1, verbose = FALSE)

shap_long2 <- shap.prep(xgb_model = mod1, X_train = X_train, var_cat = "Group")
# **SHAP summary plot**
shap.plot.summary(shap_long2, scientific = TRUE) +
  ggplot2::facet_wrap(~ Group)

Example output

Petal.Length  Petal.Width Sepal.Length  Sepal.Width 
  0.62935975   0.21664035   0.02910357   0.00000000 

SHAPforxgboost documentation built on March 28, 2021, 9:06 a.m.