SALib.analyze.ff module#

Created on 30 Jun 2015

@author: will2

SALib.analyze.ff.analyze(problem, X, Y, second_order=False, print_to_console=False, seed=None)[source]#

Perform a fractional factorial analysis

Returns a dictionary with keys ‘ME’ (main effect) and ‘IE’ (interaction effect). The techniques bulks out the number of parameters with dummy parameters to the nearest 2**n. Any results involving dummy parameters could indicate a problem with the model runs.

Notes

Compatible with:

ff : SALib.sample.ff.sample()

Examples

>>> X = sample(problem)
>>> Y = X[:, 0] + (0.1 * X[:, 1]) + ((1.2 * X[:, 2]) * (0.2 + X[:, 0]))
>>> analyze(problem, X, Y, second_order=True, print_to_console=True)
Parameters:

  • problem (dict) – The problem definition

  • X (numpy.matrix) – The NumPy matrix containing the model inputs

  • Y (numpy.array) – The NumPy array containing the model outputs

  • second_order (bool, default=False) – Include interaction effects

  • print_to_console (bool, default=False) – Print results directly to console

  • seed (int) – Seed to generate a random number

Returns:

Si – A dictionary of sensitivity indices, including main effects ME, and interaction effects IE (if second_order is True)

Return type:

dict

References

  1. Saltelli, A., Ratto, M., Andres, T., Campolongo, F.,

    Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., 2008. Global Sensitivity Analysis: The Primer. Wiley, West Sussex, U.K. http://doi.org/10.1002/9780470725184

SALib.analyze.ff.cli_action(args)[source]#
SALib.analyze.ff.cli_parse(parser)[source]#
SALib.analyze.ff.interactions(problem, Y)[source]#

Computes the second order effects

Computes the second order effects (interactions) between all combinations of pairs of input factors

Parameters:

  • problem (dict) – The problem definition

  • Y (numpy.array) – The NumPy array containing the model outputs

Returns:

  • ie_names (list) – The names of the interaction pairs

  • IE (list) – The sensitivity indices for the pairwise interactions

SALib.analyze.ff.to_df(self)[source]#

Conversion method to Pandas DataFrame. To be attached to ResultDict.

Returns:

main_effect, inter_effect – A tuple of DataFrames for main effects and interaction effects. The second element (for interactions) will be None if not available.

Return type:

tuple