hmda.domain: compute and plot weighted mean SHAP contributions at group...

In HMDA: Holistic Multimodel Domain Analysis for Exploratory Machine Learning

compute and plot weighted mean SHAP contributions at group level (factors or domains)

Description

This function applies different criteria to visualize SHAP contributions

Usage

hmda.domain(
  shapley,
  domains,
  plot = "bar",
  legendstyle = "continuous",
  scale_colour_gradient = NULL,
  print = FALSE
)

Arguments

shapley	object of class 'shapley', as returned by the 'shapley' function
domains	character list, specifying the domains for grouping the features' contributions. Domains are clusters of features' names, that can be used to compute WMSHAP at higher level, along with their 95 better understand how a cluster of features influence the outcome. Note that either of 'features' or 'domains' arguments can be specified at the time.
plot	character, specifying the type of the plot, which can be either 'bar', 'waffle', or 'shap'. The default is 'bar'.
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.
print	logical. if TRUE, the WMSHAP summary table for the given row is printed

Value

ggplot object

Author(s)

E. F. Haghish

Examples

## Not run: 
  library(HMDA)
  library(h2o)
  hmda.init()

  # Import a sample binary outcome dataset into H2O
  train <- h2o.importFile(
  "https://s3.amazonaws.com/h2o-public-test-data/smalldata/higgs/higgs_train_10k.csv")
  test <- h2o.importFile(
  "https://s3.amazonaws.com/h2o-public-test-data/smalldata/higgs/higgs_test_5k.csv")

  # Identify predictors and response
  y <- "response"
  x <- setdiff(names(train), y)

  # For binary classification, response should be a factor
  train[, y] <- as.factor(train[, y])
  test[, y] <- as.factor(test[, y])

  params <- list(learn_rate = c(0.01, 0.1),
                 max_depth = c(3, 5, 9),
                 sample_rate = c(0.8, 1.0)
  )

  # Train and validate a cartesian grid of GBMs
  hmda_grid1 <- hmda.grid(algorithm = "gbm", x = x, y = y,
                          grid_id = "hmda_grid1",
                          training_frame = train,
                          nfolds = 10,
                          ntrees = 100,
                          seed = 1,
                          hyper_params = params)

  # Assess the performances of the models
  grid_performance <- hmda.grid.analysis(hmda_grid1)

  # Return the best 2 models according to each metric
  hmda.best.models(grid_performance, n_models = 2)

  # build an autoEnsemble model & test it with the testing dataset
  meta <- hmda.autoEnsemble(models = hmda_grid1, training_frame = train)
  print(h2o.performance(model = meta$model, newdata = test))

  # compute weighted mean shap values
  wmshap <- hmda.wmshap(models = hmda_grid1,
                        newdata = test,
                        performance_metric = "aucpr",
                        standardize_performance_metric = FALSE,
                        performance_type = "xval",
                        minimum_performance = 0,
                        method = "mean",
                        cutoff = 0.01,
                        plot = TRUE)

  # define domains to combine their WMSHAP values
  # =============================================
  #
  # There are different ways to specify a cluster of features or even
  # a group of factors that touch on a broader domain. HMDA includes
  # exploratory factor analysis procedure to help with this process
  # (see ?hmda.efa function). Here, "assuming" that we have good reasons
  # to combine some of the features under some clusters:

  domains = list(Group1 = c("x22", "x18", "x14", "x1", "x10", "x4"),
                 Group2 = c("x25", "x23", "x6", "x27"),
                 Group3 = c("x28", "x26"))

  hmda.domain(shapley = wmshap,
              plot = "bar",
              domains = domains,
              print = TRUE)

## End(Not run)

HMDA documentation built on April 4, 2025, 6:06 a.m.