PartitionedLS.jl

Linear least squares is one of the most widely used regression methods among scientists in many fields. The simplicity of the model allows this method to be used when data is scarce and it is usually appealing to practitioners that need to gather some insight into the problem by inspecting the values of the learnt parameters. PartitionedLS is a variant of the linear least squares model allowing practitioners to partition the input features into groups of variables that they require to contribute with the same sign to the final result. For instance, when analyzing complex chemical compounds, it is possible to group together fine-grained features to obtain a partition which refers to high-level properties of the compound (such as structural, interactive and bond-forming among others), and knowing how much each high-level property contributes to the result of the analysis is often of great practical value. The PartitionedLS squares problem allows a practitioner to specify how to group the variables together.