Skip to contents

A wrapped function to make summary plot from model object and predictors

Source: R/SHAP_funcs.R
shap.plot.summary.wrap1.Rd

shap.plot.summary.wrap1 wraps up function shap.prep and shap.plot.summary

Usage

shap.plot.summary.wrap1(model, X, top_n, dilute = FALSE)

Arguments

model

the model

X

the dataset of predictors used for calculating SHAP

top_n

how many predictors you want to show in the plot (ranked)

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

Examples

# Example: Basic workflow for SHAP summary plot
# Note: For xgboost 3.x, use xgb.DMatrix + xgb.train, and convert factor labels to numeric

data("iris")
X1 = as.matrix(iris[,1:4])
y1 = as.numeric(iris[[5]]) - 1  # Convert factor to numeric
dtrain = xgboost::xgb.DMatrix(data = X1, label = y1)
params = list(learning_rate = 1, min_split_loss = 0, reg_lambda = 0,
              objective = 'reg:squarederror', nthread = 1)
mod1 = xgboost::xgb.train(params = params, data = dtrain,
                          nrounds = 1, verbose = 0)

# Get SHAP values and feature importance
shap_values <- shap.values(xgb_model = mod1, X_train = X1)
shap_values$mean_shap_score  # Ranked features by mean|SHAP|
#> Petal.Length  Petal.Width Sepal.Length  Sepal.Width 
#>    0.6307042    0.2135736    0.0300757    0.0000000 
shap_values_iris <- shap_values$shap_score

# Prepare long-format data for plotting
shap_long_iris <- shap.prep(xgb_model = mod1, X_train = X1)
# Alternative: use pre-computed SHAP values
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)


# Alternative options:
# Option 1: directly from xgboost model
shap.plot.summary.wrap1(mod1, X = as.matrix(iris[,1:4]), top_n = 3)


# Option 2: from pre-computed SHAP values (useful for cross-validation)
shap.plot.summary.wrap2(shap_score = shap_values_iris, X = X1, top_n = 3)