{
    "info": {
        "author": "Nazih BENOUMECHIARA & Kevin ELIE-DIT-COSAQUE",
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        "description": "[![build status](https://gitlab.com/CEMRACS17/shapley-effects/badges/master/build.svg)](https://gitlab.com/CEMRACS17/shapley-effects/commits/master)\n[![coverage report](https://gitlab.com/CEMRACS17/shapley-effects/badges/master/coverage.svg)](https://gitlab.com/CEMRACS17/shapley-effects/commits/master)\n# Shapley effects\n\nShapley-effects, or `shapley`, is a Python library that estimates the Shapley effects for the field of Sensitivity Analysis of Model Output [[1]](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:\n\n- compute the Shapley effects,\n- compute the Sobol' indices for dependent and independent inputs,\n- build a surrogate model to substitute the numerical function.\n\nThe 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. \n\n## Important links\n\n- Example notebooks are available in the [example directory](https://gitlab.com/CEMRACS17/shapley-effects/tree/dev/examples).\n- Issues: [https://gitlab.com/CEMRACS17/shapley-effects/issues](https://gitlab.com/CEMRACS17/shapley-effects/issues)\n\n## Installation\n\nVarious dependencies are necessary in this library and we strongly recommend the use of [Anaconda](https://anaconda.org/) for the installation. The dependencies are:\n\n- Numpy,\n- Scipy,\n- Pandas,\n- OpenTURNS,\n- Scikit-Learn,\n- GPflow.\n\nScikit-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.\n\nOptional dependencies are also necessary for various task like plotting or tuning the model:\n\n- Matplotlib,\n- Seaborn,\n- Scikit-Optimize.\n\nThese libraries can easily be installed using Anaconda and pip. Execute the following commands:\n\n```\nconda install numpy pandas scikit-learn tensorflow matplotlib seaborn scikit-optimize\nconda install -c conda-forge openturns gpy\n```\n\nThe package GPflow is not available on Anaconda or PyPi. Thus it must be installed from the source. First clone the GitHub repository:\n\n```\ngit clone https://github.com/GPflow/GPflow.git\n```\n\nThen, inside the GPflow folder, execute the command:\n\n```\npip install .\n```\n\n## Acknowledgements\n\nThe 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.\n\n## References\n\n[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.\n\n[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.",
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