shapley (version 0.6.0)

shapley.row.plot: WMSHAP row-level plot for a single observation (participant or data row)

Description

Computes and visualizes Weighted Mean SHAP contributions (WMSHAP) for a single row (subject/observation) across multiple models in a shapley object. For each feature, the function computes a weighted mean of row-level SHAP contributions across models using shapley$weights and reports an approximate 95 interval summarizing variability across models.

Usage

shapley.row.plot(
  shapley,
  row_index,
  features = NULL,
  plot = TRUE,
  print = FALSE
)

Value

a list including the GGPLOT2 object and the data frame of WMSHAP summary values.

Arguments

shapley

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

row_index

Integer (length 1). The row/subject identifier to visualize. This is matched against the index column in shapley$results.

features

Optional character vector of feature names to plot. If NULL, all available features in shapley$results are used.

plot

Logical. If TRUE, prints the plot.

print

Logical. If TRUE, prints the computed summary table for the row.

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)

set.seed(10)

### 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.

#######################################################
### EXAMPLE 1: 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"),

                 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,
                  performance_metric = "aucpr", plot = TRUE)

shapley.row.plot(result, row_index = 11)

#######################################################
### EXAMPLE 2: PREPARE H2O Grid (takes a couple of minutes)
#######################################################
# make sure equal number of "nfolds" is specified for different grids
grid <- h2o.grid(algorithm = "gbm", y = y, training_frame = prostate,
                 hyper_params = list(ntrees = seq(1,50,1)),
                 grid_id = "ensemble_grid",

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

result2 <- shapley(models = grid, newdata = prostate,
                   performance_metric = "aucpr", plot = TRUE)

shapley.row.plot(result2, row_index = 9)

#######################################################
### EXAMPLE 3: PREPARE autoEnsemble STACKED ENSEMBLE MODEL
#######################################################

### get the models' IDs from the AutoML and grid searches.
### this is all that is needed before building the ensemble,
### i.e., to specify the model IDs that should be evaluated.
library(autoEnsemble)
ids    <- c(h2o.get_ids(aml), h2o.get_ids(grid))
autoSearch <- ensemble(models = ids, training_frame = prostate, strategy = "search")
result3 <- shapley(models = autoSearch, newdata = prostate,
                   performance_metric = "aucpr", plot = TRUE)

#plot all important features
shapley.row.plot(result3, row_index = 13)

#plot only the given features
shapPlot <- shapley.row.plot(result3, row_index = 13, features = c("PSA", "AGE"))

# inspect the computed data for the row 13
ptint(shapPlot$summary)
}