Metadata-Version: 1.0
Name: shapley-effects
Version: 0.1
Summary: Estimation of Shapley effects for Sensitivity Analysis of Model Output.
Home-page: https://gitlab.com/CEMRACS17/shapley-effects
Author: Nazih BENOUMECHIARA & Kevin ELIE-DIT-COSAQUE
Author-email: nazih.benoumechiara@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: [![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 [[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:
        
        - 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)
        
        ## Installation
        
        Various dependencies are necessary in this library and we strongly recommend the use of [Anaconda](https://anaconda.org/) for the installation. The dependencies are:
        
        - Numpy,
        - Scipy,
        - Pandas,
        - OpenTURNS,
        - Scikit-Learn,
        - GPflow.
        
        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:
        
        - Matplotlib,
        - Seaborn,
        - Scikit-Optimize.
        
        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 .
        ```
        
        ## Acknowledgements
        
        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.
        
        ## References
        
        [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.
Keywords: sensitivity analysis,shapley,effects,depedencies
Platform: UNKNOWN
