├── .gitignore
├── .project
├── .pydevproject
├── .travis.yml
├── AUTHORS.rst
├── CONTRIBUTING.rst
├── HISTORY.rst
├── LICENSE
├── MANIFEST.in
├── Makefile
├── README.rst
├── abcpmc
├── __init__.py
├── mpi_util.py
├── sampler.py
└── threshold.py
├── docs
├── Makefile
├── abcpmc.png
├── abcpmc.rst
├── authors.rst
├── check_sphinx.py
├── conf.py
├── contributing.rst
├── history.rst
├── index.rst
├── installation.rst
├── modules.rst
└── usage.rst
├── notebooks
├── 2d_gauss.ipynb
├── README.rst
├── dual_abc_pmc.ipynb
└── toy_model.ipynb
├── requirements.readthedocs.txt
├── requirements.txt
├── setup.py
├── test
├── __init__.py
├── test_MpiUtil.py
├── test_sampler.py
└── test_threshold.py
└── tox.ini
/.gitignore:
--------------------------------------------------------------------------------
1 | *.py[cod]
2 |
3 | # C extensions
4 | *.so
5 |
6 | # Packages
7 | *.egg
8 | *.egg-info
9 | dist
10 | build
11 | eggs
12 | parts
13 | bin
14 | var
15 | sdist
16 | develop-eggs
17 | .installed.cfg
18 | lib
19 | lib64
20 |
21 | # Installer logs
22 | pip-log.txt
23 |
24 | # Unit test / coverage reports
25 | .coverage
26 | .tox
27 | nosetests.xml
28 | coverage.xml
29 | junit*.xml
30 |
31 | # Translations
32 | *.mo
33 |
34 | # Mr Developer
35 | .mr.developer.cfg
36 | .project
37 | .pydevproject
38 |
39 | # Sphinx
40 | docs/_build
41 |
42 | # Coverage
43 | htmlcov/
44 |
45 | # TextMate
46 | .tm_properties
47 |
48 | # Local env
49 | .envs
50 | .hope
51 |
--------------------------------------------------------------------------------
/.project:
--------------------------------------------------------------------------------
1 |
2 |
3 | abcpmc
4 |
5 |
6 |
7 |
8 |
9 | org.python.pydev.PyDevBuilder
10 |
11 |
12 |
13 |
14 |
15 | org.python.pydev.pythonNature
16 |
17 |
18 |
--------------------------------------------------------------------------------
/.pydevproject:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 | /${PROJECT_DIR_NAME}
5 |
6 | python 2.7
7 | Default
8 |
9 |
--------------------------------------------------------------------------------
/.travis.yml:
--------------------------------------------------------------------------------
1 | language: python
2 | python:
3 | - "2.7"
4 | - "3.3"
5 |
6 | before_install:
7 | - sudo apt-get update
8 | # We do this conditionally because it saves us some downloading if the
9 | # version is the same.
10 | - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
11 | wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh;
12 | else
13 | wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
14 | fi
15 | - bash miniconda.sh -b -p $HOME/miniconda
16 | - export PATH="$HOME/miniconda/bin:$PATH"
17 | - conda update --yes conda
18 | - travis_retry conda install --yes python=$TRAVIS_PYTHON_VERSION pip numpy scipy
19 |
20 | install:
21 | - travis_retry pip install coveralls
22 | - travis_retry pip install -r requirements.txt
23 |
24 | # command to run tests, e.g. python setup.py test
25 | script: coverage run --source abcpmc setup.py test
26 | after_success:
27 | coveralls
28 |
--------------------------------------------------------------------------------
/AUTHORS.rst:
--------------------------------------------------------------------------------
1 | =======
2 | Credits
3 | =======
4 |
5 | Development Lead
6 | ----------------
7 |
8 | * Joel Akeret
9 |
10 | Contributors
11 | ------------
12 |
13 | None yet. Why not be the first?
14 |
15 | Citations
16 | ---------
17 |
18 | As you use **abcpmc** for your exciting discoveries, please cite the paper that describes the package:
19 |
20 | `Akeret, J., Refregier, A., Amara, A, Seehars, S., and Hasner, C., Journal of Cosmology and Astroparticle Physics (2015) `_
21 |
--------------------------------------------------------------------------------
/CONTRIBUTING.rst:
--------------------------------------------------------------------------------
1 | ============
2 | Contributing
3 | ============
4 |
5 | Contributions are welcome, and they are greatly appreciated! Every
6 | little bit helps, and credit will always be given.
7 |
8 | You can contribute in many ways:
9 |
10 | Types of Contributions
11 | ----------------------
12 |
13 | Report Bugs
14 | ~~~~~~~~~~~
15 |
16 | If you are reporting a bug, please include:
17 |
18 | * Your operating system name and version.
19 | * Any details about your local setup that might be helpful in troubleshooting.
20 | * Detailed steps to reproduce the bug.
21 |
22 | Fix Bugs
23 | ~~~~~~~~
24 |
25 | Implement Features
26 | ~~~~~~~~~~~~~~~~~~
27 |
28 | Write Documentation
29 | ~~~~~~~~~~~~~~~~~~~
30 |
31 | abcpmc could always use more documentation, whether as part of the
32 | official abcpmc docs, in docstrings, or even on the web in blog posts,
33 | articles, and such.
34 |
35 | Submit Feedback
36 | ~~~~~~~~~~~~~~~
37 |
38 | If you are proposing a feature:
39 |
40 | * Explain in detail how it would work.
41 | * Keep the scope as narrow as possible, to make it easier to implement.
42 | * Remember that this is a volunteer-driven project, and that contributions
43 | are welcome :)
44 |
45 | Pull Request Guidelines
46 | -----------------------
47 |
48 | Before you submit a pull request, check that it meets these guidelines:
49 |
50 | 1. The pull request should include tests.
51 | 2. If the pull request adds functionality, the docs should be updated. Put
52 | your new functionality into a function with a docstring, and add the
53 | feature to the list in README.rst.
54 | 3. The pull request should work for Python 2.6, 2.7, and 3.3, and for PyPy.
55 | make sure that the tests pass for all supported Python versions.
56 |
57 |
58 | Tips
59 | ----
60 |
61 | To run a subset of tests::
62 |
63 | $ py.test test/test_abcpmc.py
--------------------------------------------------------------------------------
/HISTORY.rst:
--------------------------------------------------------------------------------
1 | .. :changelog:
2 |
3 | History
4 | -------
5 | 0.x.x (2016-0x-xx)
6 | ++++++++++++++++++
7 |
8 | * Fixed map issue under Py3
9 | * Replaced numpy.random with a RandomState
10 | * Restart sampling feature
11 |
12 | 0.1.2 (2016-01-27)
13 | ++++++++++++++++++
14 |
15 | * Added support for sampling with multiple distance simultaneously
16 | * Clean setup.py
17 | * Simplifying the code
18 | * Improved documentation
19 |
20 |
21 | 0.1.1 (2015-05-03)
22 | ++++++++++++++++++
23 |
24 | * Python 3 support
25 | * Minor fixes
26 | * Improved documentation
27 |
28 | 0.1.0 (2015-04-28)
29 | ++++++++++++++++++
30 |
31 | * First release
--------------------------------------------------------------------------------
/LICENSE:
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--------------------------------------------------------------------------------
/MANIFEST.in:
--------------------------------------------------------------------------------
1 | include AUTHORS.rst
2 | include CONTRIBUTING.rst
3 | include HISTORY.rst
4 | include LICENSE
5 | include README.rst
6 | include requirements.txt
7 | include Makefile
8 | include docs/*
9 | include test/*
10 | include notebooks/*
11 | include example/*
--------------------------------------------------------------------------------
/Makefile:
--------------------------------------------------------------------------------
1 | .PHONY: help clean clean-pyc clean-build list test test-all coverage docs release sdist
2 |
3 | help:
4 | @echo "clean-build - remove build artifacts"
5 | @echo "clean-pyc - remove Python file artifacts"
6 | @echo "lint - check style with flake8"
7 | @echo "test - run tests quickly with the default Python"
8 | @echo "test-all - run tests on every Python version with tox"
9 | @echo "coverage - check code coverage quickly with the default Python"
10 | @echo "docs - generate Sphinx HTML documentation, including API docs"
11 | @echo "sdist - package"
12 |
13 | clean: clean-build clean-pyc
14 |
15 | clean-build:
16 | rm -fr build/
17 | rm -fr dist/
18 | rm -fr *.egg-info
19 |
20 | clean-pyc:
21 | find . -name '*.pyc' -exec rm -f {} +
22 | find . -name '*.pyo' -exec rm -f {} +
23 | find . -name '*~' -exec rm -f {} +
24 | find . -name '__pycache__' -exec rm -rf {} +
25 |
26 | lint:
27 | flake8 abcpmc test
28 |
29 | test:
30 | py.test
31 |
32 | test-all:
33 | tox
34 |
35 | coverage:
36 | coverage run --source abcpmc setup.py test
37 | coverage report -m
38 | coverage html
39 | open htmlcov/index.html
40 |
41 | docs:
42 | #rm -f docs/abcpmc.rst
43 | #rm -f docs/modules.rst
44 | sphinx-apidoc -o docs/ abcpmc
45 | $(MAKE) -C docs clean
46 | $(MAKE) -C docs html
47 | open docs/_build/html/index.html
48 |
49 | sdist: clean
50 | #pip freeze > requirements.rst
51 | python setup.py sdist
52 | ls -l dist
--------------------------------------------------------------------------------
/README.rst:
--------------------------------------------------------------------------------
1 | =============================
2 | abcpmc
3 | =============================
4 |
5 | .. image:: https://badge.fury.io/py/abcpmc.svg
6 | :target: http://badge.fury.io/py/abcpmc
7 |
8 | .. image:: https://travis-ci.org/jakeret/abcpmc.svg?branch=master
9 | :target: https://travis-ci.org/jakeret/abcpmc
10 |
11 | .. image:: https://coveralls.io/repos/jakeret/abcpmc/badge.svg?branch=master
12 | :target: https://coveralls.io/r/jakeret/abcpmc?branch=master
13 |
14 | .. image:: https://img.shields.io/badge/docs-latest-blue.svg?style=flat
15 | :target: http://abcpmc.readthedocs.org/en/latest
16 |
17 | .. image:: http://img.shields.io/badge/arXiv-1504.07245-orange.svg?style=flat
18 | :target: http://arxiv.org/abs/1504.07245
19 |
20 |
21 |
22 | A Python Approximate Bayesian Computing (ABC) Population Monte Carlo (PMC) implementation based on Sequential Monte Carlo (SMC) with Particle Filtering techniques.
23 |
24 | .. image:: https://raw.githubusercontent.com/jakeret/abcpmc/master/docs/abcpmc.png
25 | :alt: approximated 2d posterior (created with triangle.py).
26 | :align: center
27 |
28 | The **abcpmc** package has been developed at ETH Zurich in the `Software Lab of the Cosmology Research Group `_ of the `ETH Institute of Astronomy `_.
29 |
30 | The development is coordinated on `GitHub `_ and contributions are welcome. The documentation of **abcpmc** is available at `readthedocs.org `_ and the package is distributed over `PyPI `_.
31 |
32 | Features
33 | --------
34 |
35 | * Entirely implemented in Python and easy to extend
36 |
37 | * Follows Beaumont et al. 2009 PMC algorithm
38 |
39 | * Parallelized with muliprocessing or message passing interface (MPI)
40 |
41 | * Extendable with k-nearest neighbour (KNN) or optimal local covariance matrix (OLCM) pertubation kernels (Fillipi et al. 2012)
42 |
43 | * Detailed examples in IPython notebooks
44 |
45 | * A `2D gauss `_ case study
46 |
47 | * A `Multi distance `_ case study
48 |
49 | * A `toy model `_ including a comparison to theoretical predictions
50 |
51 |
52 |
--------------------------------------------------------------------------------
/abcpmc/__init__.py:
--------------------------------------------------------------------------------
1 | # abcpmc is free software: you can redistribute it and/or modify
2 | # it under the terms of the GNU General Public License as published by
3 | # the Free Software Foundation, either version 3 of the License, or
4 | # (at your option) any later version.
5 | #
6 | # abcpmc is distributed in the hope that it will be useful,
7 | # but WITHOUT ANY WARRANTY; without even the implied warranty of
8 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
9 | # GNU General Public License for more details.
10 | #
11 | # You should have received a copy of the GNU General Public License
12 | # along with abcpmc. If not, see .
13 |
14 | __author__ = 'Joel Akeret'
15 | __email__ = 'jakeret@phys.ethz.ch'
16 | __version__ = '0.1.2'
17 | __credits__ = 'ETH Zurich, Institute for Astronomy'
18 |
19 | from abcpmc.sampler import *
20 | from abcpmc.threshold import *
--------------------------------------------------------------------------------
/abcpmc/mpi_util.py:
--------------------------------------------------------------------------------
1 | # abcpmc is free software: you can redistribute it and/or modify
2 | # it under the terms of the GNU General Public License as published by
3 | # the Free Software Foundation, either version 3 of the License, or
4 | # (at your option) any later version.
5 | #
6 | # abcpmc is distributed in the hope that it will be useful,
7 | # but WITHOUT ANY WARRANTY; without even the implied warranty of
8 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
9 | # GNU General Public License for more details.
10 | #
11 | # You should have received a copy of the GNU General Public License
12 | # along with abcpmc. If not, see .
13 |
14 | import itertools
15 |
16 | try:
17 | from mpi4py import MPI
18 | MPI = MPI
19 | except ImportError:
20 | MPI = None
21 |
22 | __all__ = ["MpiPool", "mpiBCast"]
23 |
24 | class MpiPool(object):
25 | def __init__(self, mapFunction=map):
26 | self.rank = MPI.COMM_WORLD.Get_rank()
27 | self.size = MPI.COMM_WORLD.Get_size()
28 | self.mapFunction = mapFunction
29 |
30 | def map(self, function, sequence):
31 | (rank,size) = (MPI.COMM_WORLD.Get_rank(),MPI.COMM_WORLD.Get_size())
32 | sequence = mpiBCast(sequence)
33 | mergedList = _mergeList(MPI.COMM_WORLD.allgather(
34 | self.mapFunction(function, _splitList(sequence,size)[rank])))
35 | return mergedList
36 |
37 | def isMaster(self):
38 | """
39 | Returns true if the rank is 0
40 | """
41 | return (self.rank==0)
42 |
43 | def mpiBCast(value):
44 | """
45 | Mpi bcasts the value and returns the value from the master (rank = 0).
46 | """
47 | return MPI.COMM_WORLD.bcast(value)
48 |
49 | def _splitList(list, n):
50 | blockLen = len(list) / float(n)
51 | return [list[int(round(blockLen * i)): int(round(blockLen * (i + 1)))] for i in range(n)]
52 |
53 | def _mergeList(lists):
54 | return list(itertools.chain(*lists))
55 |
56 |
57 |
58 |
--------------------------------------------------------------------------------
/abcpmc/sampler.py:
--------------------------------------------------------------------------------
1 | # abcpmc is free software: you can redistribute it and/or modify
2 | # it under the terms of the GNU General Public License as published by
3 | # the Free Software Foundation, either version 3 of the License, or
4 | # (at your option) any later version.
5 | #
6 | # abcpmc is distributed in the hope that it will be useful,
7 | # but WITHOUT ANY WARRANTY; without even the implied warranty of
8 | # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
9 | # GNU General Public License for more details.
10 | #
11 | # You should have received a copy of the GNU General Public License
12 | # along with abcpmc. If not, see .
13 |
14 |
15 | '''
16 | Created on Oct 9, 2014
17 |
18 | author: jakeret
19 | '''
20 | from __future__ import print_function, division, absolute_import, unicode_literals
21 |
22 | from multiprocessing.pool import Pool
23 | from collections import namedtuple
24 |
25 | import numpy as np
26 | from scipy import stats
27 | from scipy import spatial
28 |
29 | __all__ = ["GaussianPrior",
30 | "TophatPrior",
31 | "ParticleProposal",
32 | "KNNParticleProposal",
33 | "OLCMParticleProposal",
34 | "Sampler",
35 | "PoolSpec",
36 | "weighted_cov",
37 | "weighted_avg_and_std"
38 | ]
39 |
40 | class GaussianPrior(object):
41 | """
42 | Normal gaussian prior
43 |
44 | :param mu: scalar or vector of means
45 | :param sigma: scalar variance or covariance matrix
46 | """
47 |
48 | def __init__(self, mu, sigma):
49 | self.mu = mu
50 | self.sigma = sigma
51 | self._random = np.random.mtrand.RandomState()
52 |
53 | def __call__(self, theta=None):
54 | if theta is None:
55 | return self._random.multivariate_normal(self.mu, self.sigma)
56 | else:
57 | return stats.multivariate_normal.pdf(theta, self.mu, self.sigma)
58 |
59 |
60 | class TophatPrior(object):
61 | """
62 | Tophat prior
63 |
64 | :param min: scalar or array of min values
65 | :param max: scalar or array of max values
66 | """
67 |
68 | def __init__(self, min, max):
69 | self.min = np.atleast_1d(min)
70 | self.max = np.atleast_1d(max)
71 | self._random = np.random.mtrand.RandomState()
72 | assert self.min.shape == self.max.shape
73 | assert np.all(self.min < self.max)
74 |
75 | def __call__(self, theta=None):
76 | if theta is None:
77 | return np.array([self._random.uniform(mi, ma) for (mi, ma) in zip(self.min, self.max)])
78 | else:
79 | return 1 if np.all(theta < self.max) and np.all(theta >= self.min) else 0
80 |
81 | class ParticleProposal(object):
82 | """
83 | Creates new particles using twice the weighted covariance matrix (Beaumont et al. 2009)
84 | """
85 | def __init__(self, sampler, eps, pool, kwargs):
86 | self.postfn = sampler.postfn
87 | self.distfn = sampler.dist
88 | self._random = sampler._random
89 | self.Y = sampler.Y
90 | self.N = sampler.N
91 | self.eps = np.asanyarray(eps)
92 | self.pool = pool
93 | self.kwargs = kwargs
94 |
95 | self.sigma = 2 * weighted_cov(pool.thetas, pool.ws)
96 |
97 | def __call__(self, i):
98 | # setting seed to prevent problem with multiprocessing
99 | self._random.seed(i)
100 | cnt = 1
101 | while True:
102 | idx = self._random.choice(range(self.N), 1, p= self.pool.ws/np.sum(self.pool.ws))[0]
103 | theta = self.pool.thetas[idx]
104 | sigma = self._get_sigma(theta, **self.kwargs)
105 | sigma = np.atleast_2d(sigma)
106 | thetap = self._random.multivariate_normal(theta, sigma)
107 | X = self.postfn(thetap)
108 | p = np.asarray(self.distfn(X, self.Y))
109 |
110 | if np.all(p <= self.eps):
111 | break
112 | cnt+=1
113 | return thetap, p, cnt
114 |
115 | def _get_sigma(self, theta):
116 | return self.sigma
117 |
118 | class KNNParticleProposal(ParticleProposal):
119 | """
120 | Creates new particles using a covariance matrix from the K-nearest neighbours (Fillipi et al. 2012)
121 | Set `k` as key-word arguement in `abcpmc.Sampler.particle_proposal_kwargs`
122 | """
123 |
124 | def _get_sigma(self, theta, k):
125 | tree = spatial.cKDTree(self.pool.thetas)
126 | _, idxs = tree.query(theta, k, p=2)
127 | sigma = np.cov(self.pool.thetas[idxs].T)
128 | return sigma
129 |
130 | class OLCMParticleProposal(ParticleProposal):
131 | """
132 | Creates new particles using an optimal loacl covariance matrix (Fillipi et al. 2012)
133 | """
134 |
135 | def _get_sigma(self, theta):
136 | if len(self.eps.shape) == 0:
137 | idx = self.pool.dists < self.eps
138 | else:
139 | idx = np.all(self.pool.dists < self.eps, axis=1)
140 | thetas = self.pool.thetas[idx]
141 | weights = self.pool.ws[idx]
142 | weights = weights/np.sum(weights)
143 |
144 | m = np.sum((weights * thetas.T).T, axis=0)
145 | n = thetas.shape[1]
146 |
147 | sigma = np.empty((n, n))
148 | for i in range(n):
149 | for j in range(n):
150 | sigma[i, j] = np.sum(weights * (thetas[:, i] - m[i]) * (thetas[:, j] - m[j]).T) + (m[i] - theta[i]) * (m[j] - theta[j])
151 | return sigma
152 |
153 |
154 | """Namedtuple representing a pool of one sampling iteration"""
155 | PoolSpec = namedtuple("PoolSpec", ["t", "eps", "ratio", "thetas", "dists", "ws"])
156 |
157 | class Sampler(object):
158 | """
159 | ABC population monte carlo sampler
160 |
161 | :param N: number of particles
162 | :param Y: observed data set
163 | :param postfn: model function (a callable), which creates a new dataset x for a given theta
164 | :param dist: distance function rho(X, Y) (a callable)
165 | :param threads: (optional) number of threads. If >1 and no pool is given multiprocesses will be started
166 | :param pool: (optional) a pool instance which has a