GlobalSensitivity.jl

GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia - Published in JOSS (2022)

https://github.com/sciml/globalsensitivity.jl

Science Score: 100.0%

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Published in Journal of Open Source Software

Keywords

differential-equations efast global-sensitivity-analysis gsa julia julia-language julialang morris-method ode ordinary-differential-equations scientific-machine-learning sciml sensitivity-analysis sobol-method

Keywords from Contributors

pde meshing fluxes standardization unconstrained-optimization unconstrained-optimisation optimisation ode-solver probabilistic-models probabilistic-numerics

Scientific Fields

Engineering Computer Science - 60% confidence

Last synced: 4 months ago · JSON representation ·

Repository

Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia

Basic Info

Host: GitHub
Owner: SciML
License: mit
Language: Julia
Default Branch: master
Homepage: https://docs.sciml.ai/GlobalSensitivity/stable/
Size: 23.2 MB

Statistics

Stars: 58
Watchers: 6
Forks: 23
Open Issues: 29
Releases: 30

Topics

Created about 5 years ago · Last pushed 5 months ago

Metadata Files

Readme License Citation

README.md

GlobalSensitivity.jl

GlobalSensitivity.jl package contains implementation of some the most popular GSA methods. Currently it supports Delta Moment-Independent, DGSM, EASI, eFAST, Morris, Mutual Information, Fractional Factorial, RBD-FAST, RSA, Sobol and Regression based sensitivity methods.

Tutorials and Documentation

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation, which contains the unreleased features.

Installation

The GlobalSensitivity.jl package can be installed with julia's package manager as shown below:

julia using Pkg Pkg.add("GlobalSensitivity")

General Interface

The general interface for performing global sensitivity analysis using this package is:

julia res = gsa(f, method, param_range; samples, batch = false)

Example

Sobol method on the Ishigami function.

Serial execution

```julia function ishi(X) A = 7 B = 0.1 sin(X[1]) + A * sin(X[2])^2 + B * X[3]^4 * sin(X[1]) end

n = 600000 lb = -ones(4) * π ub = ones(4) * π sampler = SobolSample() A, B = QuasiMonteCarlo.generatedesignmatrices(n, lb, ub, sampler)

res1 = gsa(ishi, Sobol(order = [0, 1, 2]), A, B) ```

Using batching interface

```julia function ishi_batch(X) A = 7 B = 0.1 @. sin(X[1, :]) + A * sin(X[2, :])^2 + B * X[3, :]^4 * sin(X[1, :]) end

res2 = gsa(ishi_batch, Sobol(), A, B, batch = true) ```

Regression based and Morris method sensitivity analysis of Lotka Volterra model.

```julia using GlobalSensitivity, QuasiMonteCarlo, OrdinaryDiffEq, Statistics, CairoMakie

function f(du, u, p, t) du[1] = p[1] * u[1] - p[2] * u[1] * u[2] #prey du[2] = -p[3] * u[2] + p[4] * u[1] * u[2] #predator end

u0 = [1.0; 1.0] tspan = (0.0, 10.0) p = [1.5, 1.0, 3.0, 1.0] prob = ODEProblem(f, u0, tspan, p) t = collect(range(0, stop = 10, length = 200))

f1 = function (p) prob1 = remake(prob; p = p) sol = solve(prob1, Tsit5(); saveat = t) return [mean(sol[1, :]), maximum(sol[2, :])] end

bounds = [[1, 5], [1, 5], [1, 5], [1, 5]]

regsens = gsa(f1, RegressionGSA(true), bounds) fig = Figure(resolution = (600, 400)) ax, hm = CairoMakie.heatmap(fig[1, 1], regsens.partialcorrelation, figure = (resolution = (300, 200),), axis = (xticksvisible = false, yticksvisible = false, yticklabelsvisible = false, xticklabelsvisible = false, title = "Partial correlation")) Colorbar(fig[1, 2], hm) ax, hm = CairoMakie.heatmap(fig[2, 1], regsens.standard_regression, figure = (resolution = (300, 200),), axis = (xticksvisible = false, yticksvisible = false, yticklabelsvisible = false, xticklabelsvisible = false, title = "Standard regression")) Colorbar(fig[2, 2], hm) fig ```

heatmapreg

julia using StableRNGs _rng = StableRNG(1234) morris_sens = gsa(f1, Morris(), bounds, rng = _rng) fig = Figure(resolution = (300, 200)) scatter(fig[1, 1], [1, 2, 3, 4], morris_sens.means_star[1, :], color = :green, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Prey (Morris)")) scatter(fig[1, 2], [1, 2, 3, 4], morris_sens.means_star[2, :], color = :red, axis = (xticksvisible = false, xticklabelsvisible = false, title = "Predator (Morris)")) fig

morrisscat

Citing

If you use this software in your work, please cite:

bib @article{dixit2022globalsensitivity, title={GlobalSensitivity. jl: Performant and Parallel Global Sensitivity Analysis with Julia}, author={Dixit, Vaibhav Kumar and Rackauckas, Christopher}, journal={Journal of Open Source Software}, volume={7}, number={76}, pages={4561}, year={2022} }

Owner

Name: SciML Open Source Scientific Machine Learning
Login: SciML
Kind: organization
Email: contact@chrisrackauckas.com

Website: https://sciml.ai
Twitter: SciML_Org
Repositories: 170
Profile: https://github.com/SciML

Open source software for scientific machine learning

JOSS Publication

GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia

Published

August 17, 2022

DOI

10.21105/joss.04561

Volume 7, Issue 76, Page 4561

Authors

Vaibhav Kumar Dixit

Julia Computing

Christopher Rackauckas

Julia Computing, Massachusetts Institute of Technology, Pumas-AI

Editor

Mehmet Hakan Satman

Citation (CITATION.bib)

@article{dixit2022globalsensitivity,
  title={GlobalSensitivity. jl: Performant and Parallel Global Sensitivity Analysis with Julia},
  author={Dixit, Vaibhav Kumar and Rackauckas, Christopher},
  journal={Journal of Open Source Software},
  volume={7},
  number={76},
  pages={4561},
  year={2022}
}

GitHub Events

Total

Create event: 12
Release event: 1
Issues event: 4
Watch event: 7
Delete event: 11
Issue comment event: 11
Push event: 52
Pull request review event: 1
Pull request event: 22
Fork event: 4

Last Year

Create event: 12
Release event: 1
Issues event: 4
Watch event: 7
Delete event: 11
Issue comment event: 11
Push event: 52
Pull request review event: 1
Pull request event: 22
Fork event: 4

Committers

Last synced: 5 months ago

All Time

Total Commits: 317
Total Committers: 24
Avg Commits per committer: 13.208
Development Distribution Score (DDS): 0.527

Past Year

Commits: 18
Committers: 6
Avg Commits per committer: 3.0
Development Distribution Score (DDS): 0.556

Top Committers

Name	Email	Commits
Vaibhav Dixit	v**t@g**m	150
Christopher Rackauckas	a**s@c**m	49
CompatHelper Julia	c**y@j**g	18
Arno Strouwen	a**n@t**e	18
github-actions[bot]	4****]	16
dependabot[bot]	4****]	12
Anurag Vaidya	a**2@b**u	7
David Widmann	d****n	7
Mehmet Hakan Satman	m**n@g**m	5
Sathvik Bhagavan	s**n@j**m	5
Anant Thazhemadam	4****m	4
Max de Rooij	m**j@t**l	4
Pawel Latawiec	p**c@h**m	4
Oskar Laverny	o**y@u**r	4
Chris Elrod	e**c@g**m	3
Krishna Bhogaonker	c**q@g**m	2
Harry Saxton	c**9@m**k	2
Andreas Scheidegger	a**r@e**h	1
Chris de Graaf	me@c****v	1
Frank Schaefer	f**r@u**h	1
Hendrik Ranocha	m**l@r**e	1
Pepijn de Vos	p**s@j**m	1
Claude	n**y@a**m	1
maja.k.gwozdz@gmail.com	m**z@g**m	1

Committer Domains (Top 20 + Academic)

anthropic.com: 1 juliacomputing.com: 1 ranocha.de: 1 unibas.ch: 1 cdg.dev: 1 eawag.ch: 1 my.shu.ac.uk: 1 univ-amu.fr: 1 tue.nl: 1 juliahub.com: 1 bucknell.edu: 1 telenet.be: 1 julialang.org: 1 chrisrackauckas.com: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 50
Total pull requests: 140
Average time to close issues: 6 months
Average time to close pull requests: 14 days
Total issue authors: 23
Total pull request authors: 19
Average comments per issue: 3.74
Average comments per pull request: 0.86
Merged pull requests: 124
Bot issues: 0
Bot pull requests: 51

Past Year

Issues: 4
Pull requests: 17
Average time to close issues: N/A
Average time to close pull requests: 20 days
Issue authors: 4
Pull request authors: 4
Average comments per issue: 0.0
Average comments per pull request: 0.0
Merged pull requests: 13
Bot issues: 0
Bot pull requests: 12

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Top Authors

Issue Authors

Vaibhavdixit02 (14)
ChrisRackauckas (8)
Ge0rges (3)
zhenwu0728 (2)
NikosMemmos (2)
max-de-rooij (2)
devmotion (2)
moesphere (1)
JuliaTagBot (1)
xushanthu-2014 (1)
natema (1)
duodenum96 (1)
ArnoStrouwen (1)
platawiec (1)
TorkelE (1)

Pull Request Authors

github-actions[bot] (45)
Vaibhavdixit02 (39)
ArnoStrouwen (21)
dependabot[bot] (18)
devmotion (12)
max-de-rooij (10)
thazhemadam (8)
ChrisRackauckas (5)
sathvikbhagavan (4)
jbytecode (3)
scheidan (2)
00krishna (2)
lrnv (2)
ranocha (1)
christopher-dG (1)

Top Labels

Issue Labels

bug (4)

Pull Request Labels

dependencies (18) hacktoberfest-accepted (1)

Packages

Total packages: 1
Total downloads:
- julia 251 total

Total dependent packages: 5
Total dependent repositories: 0
Total versions: 31

juliahub.com: GlobalSensitivity

Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia

Homepage: https://docs.sciml.ai/GlobalSensitivity/stable/
Documentation: https://docs.juliahub.com/General/GlobalSensitivity/stable/
License: MIT
Latest release: 2.8.0
published 5 months ago

Versions: 31
Dependent Packages: 5
Dependent Repositories: 0
Downloads: 251 Total

Rankings

Dependent repos count: 9.9%

Dependent packages count: 11.0%

Average: 13.4%

Forks count: 14.9%

Stargazers count: 17.9%

Last synced: 4 months ago

Dependencies

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.github/workflows/Tests.yml actions

GlobalSensitivity.jl

Science Score: 100.0%

Keywords

Keywords from Contributors

Scientific Fields

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

GlobalSensitivity.jl

Tutorials and Documentation

Installation

General Interface

Example

Sobol method on the Ishigami function.

Regression based and Morris method sensitivity analysis of Lotka Volterra model.

Citing

Owner

JOSS Publication

GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia

Authors

Editor

Tags

Citation (CITATION.bib)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

juliahub.com: GlobalSensitivity

Rankings

Dependencies