decisionImportance: Compute the importance of decisions

In aruaud/endoR: Interpret and Visualize Stable Tree Ensemble Models

Compute the importance of decisions

Description

This function computes the importance of decisions.

Usage

decisionImportance(
  rules,
  data,
  target,
  classPos = NULL,
  in_parallel = FALSE,
  n_cores = detectCores() - 1,
  cluster = NULL
)

Arguments

rules	a data.frame with a column "condition" or a vector with name "condition".
data	data from which to get the decision support.
target	response variable.
classPos	if classification, the positive class.
in_parallel	if TRUE, the function is run in parallel.
n_cores	if in_parallel = TRUE, and no cluster has been passed: number of cores to use.
cluster	the cluster to use to run the function in parallel.

Value

the data.frame passed in rules with the gain and importance of the each decision.

Examples

library(randomForest)
library(caret)
library(data.table)

# import data and fit model
data(iris)
mod <- randomForest(Species ~ ., data = iris)

# Let's get the decision ensemble. One could use the wrapping function
# model2DE() but, we will run each function separately.

# Get the raw decision ensemble
de <- preCluster(model = mod, model_type = "rf", data = iris[, -5]
        , target = iris$Species, classPos = "setosa"
        , times = 1 # number of bootstraps, here just one
        , discretize = FALSE) # we will discretize outside for the example
summary(de)
# exec = the decision ensemble
# partitions = list of sample indexes for boostrapping
# if we had done discretization, the new data would be in data_ctg
de <- de$exec

# Discretize variables in 3 categories - optional
de <- discretizeDecisions(rules = de, data = iris[, -5], target = iris$Species
        , K = 3, classPos = "setosa", mode = "data")
data_ctg <- de$data_ctg
de <- de$rules_ctg

# Homogenize the decision ensemble
de <- de[, condition := sapply(condition, function(x) {
  paste(sort(unlist(strsplit(x, split = " & "))), collapse = " & ")
})]
de <- unique(
          as.data.table(de)[, n := as.numeric(n)][, n := sum(n), by = condition]
          )

# Calculate decision metrics, we don't need the importances yet since we will
# do pruning. Otherwise, set importances = TRUE and skip the next 2 steps.
de_met <- getDecisionsMetrics(de, data = data_ctg, target = iris$Species
            , classPos = "setosa", importances = FALSE)
de <- de[de_met, on = "condition"]

# Pruning - optional
de <- pruneDecisions(rules = de, data = data_ctg, target = iris$Species
        , classPos = "setosa")

# Decision importances
de <- decisionImportance(rules = de, data = data_ctg, target = iris$Species
        , classPos = "setosa")

# Filter out decisions with the lowest importance: min_imp = the minimal
# importance in the decision ensemble compared to the maximal one.
# E.g., if min_imp = 0.5, then at least all decisions with an
# importance > 0.5*max(importance) will be kept.
# This ensures that we don't throw out too much.
# Since the decision ensemble is quite small, we don't need to filter much...
de <- filterDecisionsImportances(rules = de, min_imp = 0.1)

# Get the network
de_net <- getNetwork(rules = de, data = data_ctg, target = iris$Species
            , classPos = "setosa")

# Plot the feature importance/influence and the network
plotFeatures(de_net, levels_order = c("Low", "Medium", "High"))
plotNetwork(de_net, hide_isolated_nodes = FALSE, layout = "fr")

aruaud/endoR documentation built on Jan. 25, 2025, 2:20 a.m.