smcpy 0.1.13

A package for performing uncertainty quantification using a parallelized sequential Monte Carlo sampler.

SMCPy - Sequential Monte Carlo with Python

Description

SMCPy is an open-source package for performing uncertainty quantification using a parallelized sequential Monte Carlo sampler.

Key Features

• Alternative to Markov chain Monte Carlo for Bayesian inference problems
• Unbiased estimation of marginal likelihood for Bayesian model selection
• Parallelization through either numpy vectorization or mpi4py

Quick Start

Installation

To install SMCPy, use pip.

pip install smcpy

Overview

To 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 Reference

Buchholz, 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 Reference

The first is a simple likelihood tempering approach in which the tempering sequence is fixed and user-specified (FixedPhiSampler). 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 Usage

We'll set up a toy example: estimating the slope and intercept of a line given only noisy observations of points along that line.

import numpy as np
import seaborn as sns
import pandas as pd

from scipy.stats import uniform

from smcpy import AdaptiveSampler, VectorMCMC, VectorMCMCKernel
from smcpy.utils.noise_generator import generate_noisy_data


def model(params):
    return params[:, [0]] * np.linspace(0.5, 2.5, 100) + params[:, [1]]


if __name__ == "__main__":

    std_dev = 0.5
    true_params = np.array([[2, 3.5]]) # true but unknown (to be estimated)
    noisy_data = generate_noisy_data(model(true_params), std_dev)
    priors = [uniform(-6, 12.0), uniform(-6, 12.0)]

    vector_mcmc = VectorMCMC(model, noisy_data, priors, std_dev)
    mcmc_kernel = VectorMCMCKernel(vector_mcmc, ("slope", "intercept"))

    smc = AdaptiveSampler(mcmc_kernel)
    step_list, mll_list = smc.sample(num_particles=500, num_mcmc_samples=10)

    print(f"marginal log likelihood = {mll_list[-1]}")
    print(f"parameter means = {step_list[-1].compute_mean()}")

    sns.pairplot(pd.DataFrame(step_list[-1].param_dict))
    sns.mpl.pyplot.savefig("pairwise.png")

Output

[ mutation ratio: 1.0: : 100.00%|███████████████████████████████████████| phi: 1.00000/1.0 [00:00<00:00
marginal log likelihood = -76.27987428009173
parameter means = {'slope': np.float64(2.0148875448007013), 'intercept': np.float64(3.523915137965013)}

Plotting is easy with seaborn:

To run this example in parallel using MPI, the MCMC kernel just needs to be built with the ParallelVectorMCMC class in place of VectorMCMC. More details can be found in the MPI example.

Tests

Clone the repo and move into the package directory:

git clone https://github.com/nasa/SMCPy.git
cd SMCPy

Install requirements necessary to use SMCPy:

pip install -r requirements.txt

Optionally, if you'd like to use the MPI-enabled parallel sampler, install the associated requirements:

pip install -r requirements_optional.txt

Add SMCPy to your Python path. For example:

export PYTHONPATH="$PYTHONPATH:/path/to/smcpy"

Run the tests to ensure proper installation:

pytest tests

Contributing

1. Fork (https://github.com/nasa/SMCPy/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a Pull Request

Development

NASA Langley Research Center
Hampton, Virginia

This software was funded by and developed under the High Performance Computing Incubator (HPCI) at NASA Langley Research Center.

Authors

• Patrick Leser
• Julia Truong
• Michael Wang

License

Notices: Copyright 2018 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. No copyright is claimed in the United States under Title 17, U.S. Code. All Other Rights Reserved.

Disclaimers No Warranty: THE SUBJECT SOFTWARE IS PROVIDED "AS IS" WITHOUT ANY WARRANTY OF ANY KIND, EITHER EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR FREEDOM FROM INFRINGEMENT, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL BE ERROR FREE, OR ANY WARRANTY THAT DOCUMENTATION, IF PROVIDED, WILL CONFORM TO THE SUBJECT SOFTWARE. THIS AGREEMENT DOES NOT, IN ANY MANNER, CONSTITUTE AN ENDORSEMENT BY GOVERNMENT AGENCY OR ANY PRIOR RECIPIENT OF ANY RESULTS, RESULTING DESIGNS, HARDWARE, SOFTWARE PRODUCTS OR ANY OTHER APPLICATIONS RESULTING FROM USE OF THE SUBJECT SOFTWARE. FURTHER, GOVERNMENT AGENCY DISCLAIMS ALL WARRANTIES AND LIABILITIES REGARDING THIRD-PARTY SOFTWARE, IF PRESENT IN THE ORIGINAL SOFTWARE, AND DISTRIBUTES IT "AS IS."

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