Welcome to gsa_framework’s documentation!
Python package gsa_framework is aimed at providing interface for Global Sensitivity Analysis (GSA) - the study of how uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model input [SRA+08]. For each GSA method, it combines its typical components in a consistent way, while preserving modularity. The components include sampling, model runs and computation of sensitivity indices, as well as optional modules that support reliable and efficient GSA by means of convergence, robustness and validation analyses.
Sampling methods
random
latin hypercube
Sobol’ quasi-random sequences
Saltelli design [SAA+10]
custom inputs for the case when input data is independent from sampling and is obtained from measurements
Sensitivity methods
Pearson and Spearman correlation coefficients
Sobol firt and total order with Saltelli estimators [SAA+10]
Extended FAST [STC99]
Borgonovo delta moment-independent indices [Bor07]
Feature importances from gradient boosted trees with XGBoost [CG16]
Models
test functions, such as Morris, borehole, wingweight, OTLcircuit, piston, Moon, Sobol-Levitan, Sobol G and G star functions
life cycle assessment model
custom models
Additional components to support reliability of GSA
GSA results validation
Convergence of sensitivity indices
Robustness with bootstrapping
This package is part of the doctoral work of Aleksandra Kim at Paul Scherrer Institute and ETH Zurich.