Package: trata
Trata Sampling Methods PackageTrata offers a large number of general sampling strategies that can be used to explore parameter spaces or improve a model's predictive ability.Trata contains 3 modules:composite_samplessampleradaptive_samplescomposite_samplesThecomposite_samplesmodule enables a user to parse a tab or csv file and create a "variable", or parameter, class object that represents discrete discrete-ordered, or continuous samples. Theparse_filefunction returns aSamplesobject containing the points from the file. Other file types would need to be parsed with a custom function.samplerThesamplermodule enables a user to select the type of sampling method they would like to perform across a design parameter space.  The available options include:CartesianCrossCenteredCornerDakotaDefaultValueFaceLatinHyperCubeMonteCarloMultiNormalOneAtATimeProbabilityDensityFunctionQuasiRandomNumberRejectionSamplePointUniformUserValueadaptive_samplesThe number of samples required to build an accurate surrogate model isa posterioriknowledge determined by the complexity of the approximated input-output relation. Therefore enriching the training dataset as training progresses is performed and is known as active learning.Theadaptive_samplermodule allows a user to specify learning functions to help identify the next sample with the highest information value. Those learning functions are designed to allocate samples to regions where the surrogate model is thought to be inaccurate or uncertain, or the regions where particularly interesting combinations of design parameters lie, such as the region that possibly contains the globally optimum values of the design parameters. The available options include:ScoredWeightedActiveLearningDeltaExpectedImprovementLearningExpectedImprovementGetting StartedTo get the latest public version:pipinstalltrataTo get the latest stable from a cloned repo, simply run:pipinstall.Alternatively, add the path to this repo to your PYTHONPATH environment variable or in your code with:importsys
sys.path.append(path_to_trata_repo)DocumentationThe documentation can be built from thedocsdirectory using:makehtmlRead the Docs coming soon.Contact InfoTrata maintainer can be reached at:olson59@llnl.govContributingContributing to Trata is relatively easy. Just send us a pull request. When you send your request, make develop the destination branch on the Trata repository.Your PR must pass Trata's unit tests and documentation tests, and must be PEP 8 compliant. We enforce these guidelines with our CI process. To run these tests locally, and for helpful tips on git, see ourContribution Guide.Trata'sdevelopbranch has the latest contributions. Pull requests should targetdevelop, and users who want the latest package versions, features, etc. can usedevelop.Contributions should be submitted as a pull request pointing to thedevelopbranch, and must pass Trata's CI process; to run the same checks locally, use:pytesttests/test_*.pyReleasesSee ourchange logfor more details.Code of ConductPlease note that Trata has aCode of Conduct. By participating in the Trata community, you agree to abide by its rules.LicenseTrata is distributed under the terms of the MIT license. All new contributions must be made under the MIT license. See LICENSE and NOTICE for details.LLNL-CODE-838977