Adds Monte Carlo Samplers#340
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+ Coverage 98.76% 98.79% +0.03%
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BradyPlanden
commented
Jul 4, 2024
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This is ready for a first review. There is a bit more work to do on integration testing for the slower convergence samplers, as well as integrating #357. I believe this PR is in a good state to be integrated into develop, with subsequent PRs improving the design structure and robustness all around. |
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# Conflicts: # CHANGELOG.md
# Conflicts: # CHANGELOG.md
…dfS1, suggestions from review
…potential thread deadlock in optimisation_options test
… now optional arg with default=1
# Conflicts: # CHANGELOG.md
NicolaCourtier
self-requested a review
August 27, 2024 13:16
brosaplanella
approved these changes
Aug 28, 2024
brosaplanella
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Looks good, just some small comments
# Conflicts: # CHANGELOG.md
…imensions bugfix for single parameter inference
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Description
Adds Pints-based Monte Carlo samplers. The full list of composed samplers can be found here.
This PR includes a base sampling class
BaseSampler, of whichPintsBaseSampleris a child. The Pints MCMC samplers are composed of PyBOP classes, which are constructed withinPintsBaseSampler. This follows a similar structure to theBasePintsOptimiserclass. A genericMCMCSamplerclass is included for use as a default, which is conceptually aligned with theOptimisationclass.Additions to the Priors class include the
JointLogPrior.Two additional PR's will be chained around this one. The order of merge should be:
Issue reference
Fixes #6
Review
Before you mark your PR as ready for review, please ensure that you've considered the following:
Type of change
Key checklist:
$ pre-commit run(or$ nox -s pre-commit) (see CONTRIBUTING.md for how to set this up to run automatically when committing locally, in just two lines of code)$ nox -s tests$ nox -s doctestYou can run integration tests, unit tests, and doctests together at once, using
$ nox -s quick.Further checks:
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