ennemi: Non-linear correlation detection with mutual information
Petri Laarne, Martha Arbayani Zaidan, Tuomo Nieminen
Abstract
We present ennemi, a Python package for correlation analysis based on mutual information (MI). MI is a measure of relationship between variables. Unlike Pearson correlation it is valid also for non-linear relationships, yet in the linear case the two are equivalent. The effect of other variables can be removed like with partial correlation, with the same equivalence. These features make MI a better correlation measure for exploratory analysis of many variable pairs. Our package provides methods for common correlation analysis tasks using MI. It is scalable, integrated with the Python data science ecosystem, and requires minimal configuration.
Topics & Concepts
Computer scienceCorrelationMutual informationLinear correlationArtificial intelligenceMathematicsStatisticsGeometryNeural Networks and ApplicationsDistributed Sensor Networks and Detection AlgorithmsBlind Source Separation Techniques