shapley (version 0.5)

shapley.plot: Plot weighted SHAP contributions

Description

This function applies different criteria to visualize SHAP contributions

Usage

shapley.plot(
  shapley,
  plot = "bar",
  method = "mean",
  cutoff = 0.01,
  top_n_features = NULL,
  features = NULL,
  legendstyle = "continuous",
  scale_colour_gradient = NULL
)

Value

ggplot object

Arguments

shapley

object of class 'shapley', as returned by the 'shapley' function

plot

character, specifying the type of the plot, which can be either 'bar', 'waffle', or 'shap'. The default is 'bar'.

method

Character. The column name in summaryShaps used for feature selection. Default is "mean", which selects important features which have weighted mean shap ratio (WMSHAP) higher than the specified cutoff. Other alternative is "lowerCI", which selects features which their lower bound of confidence interval is higher than the cutoff.

cutoff

numeric, specifying the cutoff for the method used for selecting the top features.

top_n_features

Integer. If specified, the top n features with the highest weighted SHAP values will be selected, overrullung the 'cutoff' and 'method' arguments.

features

character vector, specifying the feature to be plotted.

legendstyle

character, specifying the style of the plot legend, which can be either 'continuous' (default) or 'discrete'. the continuous legend is only applicable to 'shap' plots and other plots only use 'discrete' legend.

scale_colour_gradient

character vector for specifying the color gradients for the plot.

Author

E. F. Haghish

Examples

if (FALSE) {
# load the required libraries for building the base-learners and the ensemble models
library(h2o)            #shapley supports h2o models
library(shapley)

# initiate the h2o server
h2o.init(ignore_config = TRUE, nthreads = 2, bind_to_localhost = FALSE, insecure = TRUE)

# upload data to h2o cloud
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.importFile(path = prostate_path, header = TRUE)

### H2O provides 2 types of grid search for tuning the models, which are
### AutoML and Grid. Below, I demonstrate how weighted mean shapley values
### can be computed for both types.

set.seed(10)

#######################################################
### PREPARE AutoML Grid (takes a couple of minutes)
#######################################################
# run AutoML to tune various models (GBM) for 60 seconds
y <- "CAPSULE"
prostate[,y] <- as.factor(prostate[,y])  #convert to factor for classification
aml <- h2o.automl(y = y, training_frame = prostate, max_runtime_secs = 120,
                 include_algos=c("GBM"),

                 # this setting ensures the models are comparable for building a meta learner
                 seed = 2023, nfolds = 10,
                 keep_cross_validation_predictions = TRUE)

### call 'shapley' function to compute the weighted mean and weighted confidence intervals
### of SHAP values across all trained models.
### Note that the 'newdata' should be the testing dataset!
result <- shapley(models = aml, newdata = prostate, plot = TRUE)

#######################################################
### PLOT THE WEIGHTED MEAN SHAP VALUES
#######################################################

shapley.plot(result, plot = "bar")
shapley.plot(result, plot = "waffle")
}