##Sensitivity Analysis Library (SALib)

Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the effects of model inputs or exogenous factors on outputs of interest.

Requirements: NumPy, SciPy, Scikit-learn

Installation: pip install SALib or python setup.py install

Build Status: Test Coverage:

Methods included:

• Sobol Sensitivity Analysis (Sobol 2001, Saltelli 2002, Saltelli et al. 2010)
• Method of Morris, including groups and optimal trajectories (Morris 1991, Campolongo et al. 2007)
• Fourier Amplitude Sensitivity Test (FAST) (Cukier et al. 1973, Saltelli et al. 1999)
• Delta Moment-Independent Measure (Borgonovo 2007, Plischke et al. 2013)
• Derivative-based Global Sensitivity Measure (DGSM) (Sobol and Kucherenko 2009)
• Metamodel-based Sobol Analysis (experimental). Uses RBF support vector regression from scikit-learn.

Contributors: Jon Herman, Chris Mutel, Will Usher, Matt Woodruff, Fernando Rios, Dan Hyams, xantares

To get started, create a file describing the sampling ranges for the parameters in the model. Parameter files should be created with 3 columns: [name, lower bound, upper bound]:

P1 0.0 1.0
P2 0.0 5.0
...etc.

Lines beginning with # will be treated as comments and ignored.

Create a file describing the grouping of parameters in the model. Group files should be created with row equal to the number of groups desired.

Group 1,P1,P2,P3
Group 2,P4,P5
Group 3,P6
...etc.

From the command line:

python -m SALib.sample.saltelli \
     -n 1000 \
     -p ./SALib/test_functions/params/Ishigami.txt \
     -o model_input.txt \

Other methods include SALib.sample.morris and SALib.sample.fast_sampler. For an explanation of all command line options, see the examples here.

Or, generate samples from Python:

from SALib.sample import saltelli
import numpy as np

param_file = '../../SALib/test_functions/params/Ishigami.txt'
param_values = saltelli.sample(1000, param_file, calc_second_order = True)
np.savetxt('model_input.txt', param_values, delimiter=' ')

Either way, this will create a file of sampled input values in model_input.txt.

Here's an example of running a test function in Python, but this will usually be a user-defined model, maybe even in another language. Just save the outputs.

from SALib.test_functions import Ishigami
Y = Ishigami.evaluate(param_values)
np.savetxt('model_output.txt', Y, delimiter=' ')

From the command line:

python -m SALib.analyze.sobol \
     -p ./SALib/test_functions/params/Ishigami.txt \
     -Y model_output.txt \
     -c 0 \

This will print indices and confidence intervals to the command line. You can redirect to a file using the > operator.

Or, from Python:

from SALib.analyze import sobol
import numpy as np
Si = sobol.analyze(param_file, 'model_output.txt', column = 0, print_to_console=False)
# Returns a dictionary with keys 'S1', 'S1_conf', 'ST', and 'ST_conf'
# e.g. Si['S1'] contains the first-order index for each parameter, in the same order as the parameter file

Check out the examples for a full description of command line and keyword options for all of the methods.

Copyright (C) 2013-2014 Jon Herman and others. Licensed under the GNU Lesser General Public License.

The Sensitivity Analysis Library is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

The Sensitivity Analysis Library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with the Sensitivity Analysis Library. If not, see http://www.gnu.org/licenses/.