Sirius: Visualization of Mixed Features as a Mutual Information Network Graph
This tool aids data scientists in exploratory analysis for mixed-feature datasets, but it is incremental as it applies existing mutual information methods to visualization.
The authors tackled the problem of exploring feature relationships in datasets with mixed data types by introducing Sirius, a visualization package that uses mutual information to create network graphs, enabling tasks like hypothesis confirmation and feature identification.
Data scientists across disciplines are increasingly in need of exploratory analysis tools for data sets with a high volume of features of mixed data type (quantitative continuous and discrete categorical). We introduce Sirius, a novel visualization package for researchers to explore feature relationships among mixed data types using mutual information. The visualization of feature relationships aids data scientists in finding meaningful dependence among features prior to the development of predictive modeling pipelines, which can inform downstream analysis such as feature selection, feature extraction, and early detection of potential proxy variables. Using an information theoretic approach, Sirius supports network visualization of heterogeneous data sets (consisting of continuous and discrete data types), and provides a user interface for exploring feature pairs with locally significant mutual information scores. Mutual information algorithm and bivariate chart types are assigned on a data type pairing basis (continuous-continuous, discrete-discrete, and discrete-continuous). We show how this tool can be used for tasks such as hypothesis confirmation, identification of predictive features, suggestions for feature extraction, or early warning of data abnormalities. The accompanying website for this paper can be accessed at https://sirius.universalities.com/. All code and supplemental materials can be accessed at https://osf.io/pdm9r/.