Package: smcpy
SMCPy -SequentialMonteCarlo withPythonDescriptionSMCPy is an open-source package for performing uncertainty quantification using
a parallelized sequential Monte Carlo sampler.Key FeaturesAlternative to Markov chain Monte Carlo for Bayesian inference problemsUnbiased estimation of marginal likelihood for Bayesian model selectionParallelization through either numpy vectorization or mpi4pyQuick StartInstallationTo install SMCPy, use pip.pipinstallsmcpyOverviewTo operate the code, the user supplies a computational model built in Python
3.6+, defines prior distributions for each of the model parameters to be
estimated, and provides data to be used for probabilistic model calibration. SMC
sampling of the parameter posterior distribution can then be conducted with ease
through instantiation of a sampler class and a call to the sample() method.The two primary sampling algorithms implemented in this package are MPI-enabled
versions of those presented in the following articles, respectively:Nguyen, Thi Le Thu, et al. "Efficient sequential Monte-Carlo samplers for Bayesian
inference." IEEE Transactions on Signal Processing 64.5 (2015): 1305-1319.Link to Article|BibTeX ReferenceBuchholz, Alexander, Nicolas Chopin, and Pierre E. Jacob. "Adaptive tuning of
hamiltonian monte carlo within sequential monte carlo." Bayesian Analysis
1.1 (2021): 1-27.Link to Article|BibTeX ReferenceThe first is a simple likelihood tempering approach in which the tempering
sequence is fixed and user-specified
(FixedSampler).
The second is an adaptive approach that chooses the tempering steps based on a
target effective sample size (AdaptiveSampler).This software was funded by and developed under the High Performance Computing
Incubator (HPCI) at NASA Langley Research Center.Example Usageimportnumpyasnpfromscipy.statsimportuniformfromspring_mass_modelimportSpringMassModelfromsmcpy.utils.plotterimportplot_pairwisefromsmcpyimportAdaptiveSampler,VectorMCMC,VectorMCMCKernel# Load datastd_dev=0.5displacement_data=np.genfromtxt('noisy_data.txt')# Define prior distributions & MCMC kernelpriors=[uniform(0,10),uniform(0,10)]vector_mcmc=VectorMCMC(model.evaluate,displacement_data,priors,std_dev)mcmc_kernel=VectorMCMCKernel(vector_mcmc,param_order=('K','g'))# SMC samplingsmc=AdaptiveSampler(mcmc_kernel)step_list,mll_list=smc.sample(num_particles=500,num_mcmc_samples=5,target_ess=0.8)# Display resultsprint(f'parameter means ={step_list[-1].compute_mean()}')plot_pairwise(step_list[-1].params,step_list[-1].weights,save=True,param_labels=['K','g'])The above code produces probabilistic estimates of K, the spring stiffness
divided by mass, and g, the gravitational constant on some unknown planet.
These estimates are in the form of weighted particles and can be visualized by
plotting the pairwise weights as shown below. The mean of each parameter is
marked by the dashed red line. The true values for this example were K = 1.67
and g = 4.62. More details can be found in thespring mass
example. To run this model in
parallel using MPI, the MCMC kernel just needs to be built with the
ParallelMCMC class in place of VectorMCMC. More details can be found in theMPI example.To run this model in parallel using MPI, the MCMC kernel just needs to be built
with the ParallelMCMC class in place of VectorMCMC. More details can be found
in the MPI example (smcpy/examples/mpi_example/).TestsClone the repo and move into the package directory:gitclonehttps://github.com/nasa/SMCPy.gitcdSMCPyInstall requirements necessary to use SMCPy:pipinstall-rrequirements.txtOptionally, if you'd like to use the MPI-enabled parallel sampler, install the
associated requirements:pipinstall-rrequirements_optional.txtAdd SMCPy to your Python path. For example:exportPYTHONPATH="$PYTHONPATH:/path/to/smcpy"Run the tests to ensure proper installation:pytesttestsContributingFork (https://github.com/nasa/SMCPy/fork)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a Pull RequestDevelopmentNASA Langley Research CenterHampton, VirginiaThis software was funded by and developed under the High Performance Computing Incubator (HPCI) at NASA Langley Research Center.AuthorsPatrick LeserMichael WangLicenseNotices:
Copyright 2018 United States Government as represented by the Administrator of
the National Aeronautics and Space Administration. No copyright is claimed in
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