HMDA (version 0.2.0)

hmda.plot: Plot WMSHAP contributions

Description

This function applies different criteria to visualize WMSHAP contributions

Usage

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

Value

ggplot object

Arguments

wmshap

object of class 'shapley', as returned by the 'shapley' function or hmda.wmshap function

plot

Character. Plot type passed to shapley.plot(). Common options are "bar" and "wmshap".

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 'wmshap' plots and other plots only use 'discrete' legend.

scale_colour_gradient

character vector for specifying the color gradients for the plot.

labels

Optional named character vector mapping feature names to display labels, e.g., c(feature1 = "Label 1", feature2 = "Label 2").

Author

E. F. Haghish

Examples

if (FALSE) {
  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)

  # 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)

#######################################################
### PLOT THE WEIGHTED MEAN SHAP VALUES
#######################################################
hmda.plot(wmshap, plot = "bar")
}