Detecting Influence Paths with Information Theory
Extract all branches of the Vistla tree
Collapse the vistla tree into a pairwise graph
Construct the value for the ensemble argument
Construct the value for the flow argument
Extract the vertex hierarchy from the vistla tree
Extract mutual information score matrix
Basic discretisation of numerical features
Extract a single path
List all paths
Overview plot of the vistla tree
Print vistla objects
Prune the vistla tree
Influence path identification with the Vistla algorithm
Export tree to a Graphviz DOT format
Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs. In particular, 'vistla' can be used to better understand the results of high-throughput biomedical experiments, by organising the effects of the investigated intervention in a tree-like hierarchy from direct to indirect ones, following the plausible information relay circuits. Due to its higher-order nature, 'vistla' can handle multi-modality and assign multiple roles to a single feature.