pyANOVAapprox

This package provides a framework for the method ANOVAapprox to approximate high-dimensional functions with a low superposition dimension or a sparse ANOVA decomposition from scattered data. The method has been dicussed and applied in the following articles/preprints:

D. Potts and M. Schmischke
Interpretable transformed ANOVA approximation on the example of the prevention of forest fires
arXiv, PDF
F. Bartel, D. Potts und M. Schmischke
Grouped transformations and Regularization in high-dimensional explainable ANOVA approximation
SIAM Journal on Scientific Computing (accepted)
arXiv, PDF
D. Potts and M. Schmischke
Interpretable approximation of high-dimensional data
SIAM Journal on Mathematics of Data Science (accepted)
arXiv, PDF, Software
D. Potts and M. Schmischke
Learning multivariate functions with low-dimensional structures using polynomial bases
Journal of Computational and Applied Mathematics 403, 113821, 2021
DOI, arXiv, PDF
D. Potts and M. Schmischke
Approximation of high-dimensional periodic functions with Fourier-based methods
SIAM Journal on Numerical Analysis 59 (5), 2393-2429, 2021
DOI, arXiv, PDF
L. Lippert, D. Potts and T. Ullrich
Fast Hyperbolic Wavelet Regression meets ANOVA
ArXiv: 2108.13197
arXiv, PDF

pyANOVAapprox provides the following functionality:

approximation of high-dimensional periodic and nonperiodic functions with a sparse ANOVA decomposition
analysis tools for interpretability (global sensitvitiy indices, attribute ranking, shapley values)

Getting started

The pyANOVAapprox package can be installed via pip:

pip install -i https://test.pypi.org/simple/ pyANOVAapprox

Read the documentation for specific usage information.

Requirements

Python 3.8 or greater
pyGroupedTransforms 0.1.0 or greater
NumPy 2.0.0 or greater
SciPy 1.16.0 or greater
Matplotlib 3.5 or greater

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