pyANOVAapprox 2.0.0

Approximation Package for High-Dimensional Functions

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