Skip to content

Latest commit

 

History

History
117 lines (90 loc) · 3.1 KB

README.md

File metadata and controls

117 lines (90 loc) · 3.1 KB
Raw

Overview

Approximate Bayesian computing (ABC) with sequential Monte Carlo (SMC) sampling is an increasingly popular and principled approach for "tuning" slow-running stochastic models with a large number of parameters to multiple datasets. This process of choosing intervals of parameter values to match the datasets is also sometimes referred to as "model calibration".

Existing higher-level language implementations of ABC-SMC samplers lack a few desirable features:

  1. Debugging crashes of samplers written in higher-level languages that occur several days of walltime is more challenging than using rich coredumps from compiled languages like C and C++.
  2. Checkpointing and resuming calibrations is generally not supported by higher-level language ABC-SMC samplers.
  3. Parallel logging facilities are also not supported.
  4. Multivariate parameters like Dirichlet distributions e.g. of observed infection outcomes.

Such limitations jusfify the additional time and complexity involved with using lower-level languages to support these and other desirable features with more intricate control over implementation.

Configure dependencies

Polaris cluster

Load the modules for the MPI-ABC dependencies:\n

module use /soft/modulefiles
module load cray-python spack-pe-base ninja math_libs/gsl doxygen

Local

If not using Polaris, fallback to a spack environment called "mpi-abc":\n

git clone -c feature.manyFiles=true https://github.com/spack/spack.git
cd spack
source share/spack/setup-env.sh
spack compiler find
spack env create mpi-abc
spack env activate mpi-abc
spack add mpich
spack add gsl+external-cblas
spack add doxygen
spack add ninja
# For tests
spack add py-gcovr
spack concretize
spack install

Then to use this spack environment in the future:\n

source spack/share/spack/setup-env.sh
spack env activate mpi-abc

Build

Run meson to build both the executable infer:\n

BUILDDIR=../build-mpiabc
meson setup -Db_coverage=true $BUILDDIR
meson compile -C $BUILDDIR
# Optional locations of headers for editing source files with Emacs flycheck:
cp -a $BUILDDIR/.dir-locals.el .

Documentation

Generate the Doxygen PDF at $BUILDDIR/latex/refman.pdf:\n

BUILDDIR=../build-mpiabc
meson compile -C $BUILDDIR docs

Test

BUILDDIR=../build-mpiabc
meson test --suite mpiabc -C $BUILDDIR

Check test coverage with:\n

BUILDDIR=../build-mpiabc
meson setup -Db_coverage=true --reconfigure $BUILDDIR
meson test --suite mpiabc -C $BUILDDIR
ninja coverage-text -C $BUILDDIR &&
	cat $BUILDDIR/meson-logs/coverage.txt

Check for memory leaks with:\n

BUILDDIR=../build-mpiabc
CK_FORK=no valgrind --leak-check=full $BUILDDIR/test_abc

API

The included example Lotka-Volterra model illustrates the 4 steps necessary to integrate a model into the MPI-ABC framework:

  1. Fit priors matching parameters in the model to the calibrated.
  2. Fix the remaining parameters.
  3. Check that the model produces outcome variables.
  4. Convert model outcomes into one-or-more distances; the distances are also called summary statistics.