Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR): a Python module for representing multidimensional functions with machine-learned lower-dimensional terms

We present a Python implementation for RS-HDMR-GPR (Random Sampling High Dimensional Model Representation Gaussian Process Regression). The method builds representations of multivariate functions with lower-dimensional terms, either as an expansion over orders of coupling or using terms of only a given dimensionality. This facilitates, in particular, recovering functional dependence from sparse data. The code also allows imputation of missing values of the variables. The capabilities of this regression tool are demonstrated on test cases involving synthetic analytic functions, the potential energy surface of the water molecule, and financial market data.