The otshapley library is a fork of shapley-effects, updated to be compatible with Python 3.9+. Surrogate model-related functionalities are removed to focus on the actual computation of Shapley effects.
Its purpose is to estimate the Shapley effects for Sensitivity Analysis of Model Output [1]. Several features are available in the library. For a given probabilistic model and numerical function, it is possible to:
- compute the Shapley effects,
- compute the Sobol' indices for dependent and independent inputs.
The library is mainly built on top of NumPy and OpenTURNS. It is also validated and compared to the sensitivity package from the R software.
- Example notebooks are available in the example directory.
>>> pip install git+https://github.com/josephmure/otshapley
Various dependencies are necessary in this library and we strongly recommend the use of Miniconda for the installation. The dependencies are:
- Numpy,
- Scipy,
- Pandas,
- OpenTURNS.
Optional dependencies are also necessary for various task like plotting or tuning the model:
- Matplotlib,
- Seaborn.
The library has been developed at the CEMRACS 2017 with the help of Bertrand Iooss, Roman Sueur, Veronique Maume-Deschamps and Clementine Prieur.
[1] Owen, A. B., & Prieur, C. (2017). On Shapley value for measuring importance of dependent inputs. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 986-1002.
[2] Song, E., Nelson, B. L., & Staum, J. (2016). Shapley effects for global sensitivity analysis: Theory and computation. SIAM/ASA Journal on Uncertainty Quantification, 4(1), 1060-1083.