Estimation of Shapley effects for Sensitivity Analysis of Model Output.
[![build status](https://gitlab.com/CEMRACS17/shapley-effects/badges/master/build.svg)](https://gitlab.com/CEMRACS17/shapley-effects/commits/master) [![coverage report](https://gitlab.com/CEMRACS17/shapley-effects/badges/master/coverage.svg)](https://gitlab.com/CEMRACS17/shapley-effects/commits/master) # Shapley effects
Shapley-effects, or shapley, is a Python library that estimates the Shapley effects for the field of Sensitivity Analysis of Model Output [](http://epubs.siam.org/doi/pdf/10.1137/16M1097717). 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,
- build a surrogate model to substitute the numerical function.
The library is mainly built on top of NumPy, OpenTURNS and other libraries. It is also validated and compared to the [sensitivity](https://github.com/cran/sensitivity/) package from the R software.
## Important links
- Example notebooks are available in the [example directory](https://gitlab.com/CEMRACS17/shapley-effects/tree/dev/examples).
- Issues: [https://gitlab.com/CEMRACS17/shapley-effects/issues](https://gitlab.com/CEMRACS17/shapley-effects/issues)
Various dependencies are necessary in this library and we strongly recommend the use of [Anaconda](https://anaconda.org/) for the installation. The dependencies are:
Scikit-learn is used to build kriging and random-forest models. OpenTURNS is a very convenient tool to define probabilistic distributions. GPflow which generates kriging models from GPy using Tensorflow.
Optional dependencies are also necessary for various task like plotting or tuning the model:
These libraries can easily be installed using Anaconda and pip. Execute the following commands:
` conda install numpy pandas scikit-learn tensorflow matplotlib seaborn scikit-optimize conda install -c conda-forge openturns gpy `
The package GPflow is not available on Anaconda or PyPi. Thus it must be installed from the source. First clone the GitHub repository:
` git clone https://github.com/GPflow/GPflow.git `
Then, inside the GPflow folder, execute the command:
` pip install . `
The library has been developed at the [CEMRACS 2017](http://smai.emath.fr/cemracs/cemracs17/) with the help of Bertrand Iooss, Roman Sueur, Veronique Maume-Deschamps and Clementine Prieur.
 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.
 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.
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