shap.plot.summary: SHAP summary plot core function using the long format SHAP...

Description Usage Arguments Value Examples

View source: R/SHAP_funcs.R

Description

The summary plot (a sina plot) uses a long format data of SHAP values. The SHAP values could be obtained from either a XGBoost/LightGBM model or a SHAP value matrix using shap.values. So this summary plot function normally follows the long format dataset obtained using shap.values. If you want to start with a model and data_X, use shap.plot.summary.wrap1. If you want to use a self-derived dataset of SHAP values, use shap.plot.summary.wrap2. If a list named new_labels is provided in the global environment (new_labels is pre-loaded by the package as NULL), the plots will use that list to label the variables, here is an example of such a list (the default labels): labels_within_package.

Usage

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shap.plot.summary(
  data_long,
  x_bound = NULL,
  dilute = FALSE,
  scientific = FALSE,
  my_format = NULL
)

Arguments

data_long

a long format data of SHAP values from shap.prep

x_bound

use to set horizontal axis limit in the plot

dilute

being numeric or logical (TRUE/FALSE), it aims to help make the test plot for large amount of data faster. If dilute = 5 will plot 1/5 of the data. If dilute = TRUE or a number, will plot at most half points per feature, so the plotting won't be too slow. If you put dilute too high, at least 10 points per feature would be kept. If the dataset is too small after dilution, will just plot all the data

scientific

show the mean|SHAP| in scientific format. If TRUE, label format is 0.0E-0, default to FALSE, and the format will be 0.000

my_format

supply your own number format if you really want

Value

returns a ggplot2 object, could add further layers.

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)

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.