module_importance: Calculate the importance of particular modules in the graph.

In pievos101/DFNET: Network-guided greedy decision forest for feature subset selection

Calculate the importance of particular modules in the graph.

Description

Calculate the importance of particular modules in the graph.

Usage

module_importance(
  graph,
  modules,
  edge_importances,
  tree_importances,
  mc.cores = 1
)

Arguments

graph	the graph used for training
modules	the modules obtained by training
edge_importances	the importance scores assigned to each edge in the graph, e.g. by edge_importance.
tree_importances	the importance scores assigned to each decision tree in the forest
mc.cores	how many cores to use in parallel

Value

the accumulated edge importance and total importance of each module.

See Also

Other metrics: edge_importance(), feature_importance(), unique_module_importance()

pievos101/DFNET documentation built on Dec. 1, 2022, 3:44 p.m.