SALib.analyze.hdmr module#

SALib.analyze.hdmr.analyze(problem: Dict, X: ndarray, Y: ndarray, maxorder: int = 2, maxiter: int = 100, m: int = 2, K: int = 20, R: int = None, alpha: float = 0.95, lambdax: float = 0.01, print_to_console: bool = False, seed: int | bool | None = None) → Dict[source]#

Compute global sensitivity indices using the meta-modeling technique known as High-Dimensional Model Representation (HDMR).

HDMR itself is not a sensitivity analysis method but a surrogate modeling approach. It constructs a map of relationship between sets of high dimensional inputs and output system variables [1]. This I/O relation can be constructed using different basis functions (orthonormal polynomials, splines, etc.). The model decomposition can be expressed as

\[\widehat{y} = \sum_{u \subseteq \{1, 2, ..., d \}} f_u\]

where \(u\) represents any subset including an empty set.

HDMR becomes extremely useful when the computational cost of obtaining sufficient Monte Carlo samples are prohibitive, as may be the case with Sobol’s method. It uses least-square regression to reduce the required number of samples and thus the number of function (model) evaluations. Another advantage of this method is that it can account for correlation among the model input. Unlike other variance-based methods, the main effects are the combination of structural (uncorrelated) and correlated contributions.

This method uses as input

  • a N x d matrix of N different d-vectors of model inputs (factors/parameters)

  • a N x 1 vector of corresponding model outputs

Notes

Compatible with:

all samplers

Sets an emulate method allowing re-use of the emulator.

Examples

 1sp = ProblemSpec({
 2    'names': ['X1', 'X2', 'X3'],
 3    'bounds': [[-np.pi, np.pi]] * 3,
 4    # 'groups': ['A', 'B', 'A'],
 5    'outputs': ['Y']
 6})
 7
 8(sp.sample_saltelli(2048)
 9    .evaluate(Ishigami.evaluate)
10    .analyze_hdmr()
11)
12
13sp.emulate()
Parameters:

  • problem (dict) – The problem definition

  • X (numpy.matrix) – The NumPy matrix containing the model inputs, N rows by d columns

  • Y (numpy.array) – The NumPy array containing the model outputs for each row of X

  • maxorder (int (1-3, default: 2)) – Maximum HDMR expansion order

  • maxiter (int (1-1000, default: 100)) – Max iterations backfitting

  • m (int (2-10, default: 2)) – Number of B-spline intervals

  • K (int (1-100, default: 20)) – Number of bootstrap iterations

  • R (int (100-N/2, default: N/2)) – Number of bootstrap samples. Will be set to length of Y if K is set to 1.

  • alpha (float (0.5-1)) – Confidence interval F-test

  • lambdax (float (0-10, default: 0.01)) – Regularization term

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

  • seed ({int, bool, None}) – Seed to generate a random number

Returns:

Si – Sa : Uncorrelated contribution of a term

Sa_conf : Confidence interval of Sa

Sb : Correlated contribution of a term

Sb_conf : Confidence interval of Sb

STotal contribution of a particular term

Sum of Sa and Sb, representing first/second/third order sensitivity indices

S_conf : Confidence interval of S

ST : Total contribution of a particular dimension/parameter

ST_conf : Confidence interval of ST

select : Number of selection (F-Test)

EmEmulator result set

C1: First order coefficient C2: Second order coefficient C3: Third Order coefficient

Return type:

ResultDict,

References

  1. Rabitz, H. and Aliş, Ö.F., “General foundations of high dimensional model representations”, Journal of Mathematical Chemistry 25, 197-233 (1999) https://doi.org/10.1023/A:1019188517934

  2. Genyuan Li, H. Rabitz, P.E. Yelvington, O.O. Oluwole, F. Bacon, C.E. Kolb, and J. Schoendorf, “Global Sensitivity Analysis for Systems with Independent and/or Correlated Inputs”, Journal of Physical Chemistry A, Vol. 114 (19), pp. 6022 - 6032, 2010, https://doi.org/10.1021/jp9096919

SALib.analyze.hdmr.cli_action(args)[source]#
SALib.analyze.hdmr.cli_parse(parser)[source]#