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Repository: minaskar/zeus
Branch: main
Commit: 1abdf08252a9
Files: 49
Total size: 89.1 MB
Directory structure:
gitextract_8ypbapnn/
├── .github/
│ └── workflows/
│ ├── release_to_pypi.yml
│ └── setup_and_run_tests.yml
├── .gitignore
├── .readthedocs.yaml
├── LICENSE
├── MANIFEST.in
├── README.md
├── docs/
│ ├── Makefile
│ ├── _static/
│ │ ├── copybutton.js
│ │ └── default.css
│ ├── api/
│ │ ├── autocorr.rst
│ │ ├── callbacks.rst
│ │ ├── moves.rst
│ │ ├── parallel.rst
│ │ ├── plotting.rst
│ │ └── sampler.rst
│ ├── api.rst
│ ├── conf.py
│ ├── cookbook.rst
│ ├── faq.rst
│ ├── index.rst
│ ├── make.bat
│ ├── notebooks/
│ │ ├── GR.ipynb
│ │ ├── MPI.ipynb
│ │ ├── blobs.ipynb
│ │ ├── convergence.ipynb
│ │ ├── datafit.ipynb
│ │ ├── multimodal.ipynb
│ │ ├── multiprocessing.ipynb
│ │ ├── normal_distribution.ipynb
│ │ └── progress.ipynb
│ └── requirements.txt
├── requirements.txt
├── setup.cfg
├── setup.py
├── tests/
│ ├── test_autocorr.py
│ ├── test_fwrapper.py
│ ├── test_sampler.py
│ └── test_samples.py
└── zeus/
├── __init__.py
├── _version.py
├── autocorr.py
├── callbacks.py
├── ensemble.py
├── fwrapper.py
├── moves.py
├── parallel.py
├── plotting.py
└── samples.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/workflows/release_to_pypi.yml
================================================
name: Publish zeus to PyPI / GitHub
on:
push:
branches:
- main
paths:
- 'zeus/_version.py' # Only run workflow on pushes where _version.py was changed
jobs:
build-n-publish:
name: Build and publish to PyPI
runs-on: ubuntu-latest
steps:
- name: Checkout source
uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: "3.x"
- name: Build source and wheel distributions
run: |
python -m pip install --upgrade build twine
python -m build
twine check --strict dist/*
- name: Install zeus from the wheel file
run: |
pip install dist/*.whl
- name: List the installed packages
run: |
pip freeze
- name: Run zeus unittests
run: |
python -m unittest discover tests
- name: Publish distribution to PyPI
uses: pypa/gh-action-pypi-publish@master
with:
user: __token__
password: ${{ secrets.PYPI_API_TOKEN }}
- name: Get the version
id: get_version
run: echo ::set-output name=VERSION::$(cat zeus/_version.py | grep version | cut -d'"' -f 2)
- name: Create GitHub Release
id: create_release
uses: actions/create-release@v1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, no need to create our own
with:
tag_name: ${{ steps.get_version.outputs.VERSION }}
release_name: ${{ steps.get_version.outputs.VERSION }}
draft: false
prerelease: false
- name: Get Asset name
run: |
export PKG=$(ls dist/ | grep tar)
set -- $PKG
echo "name=$1" >> $GITHUB_ENV
- name: Upload Release Asset (sdist) to GitHub
id: upload-release-asset
uses: actions/upload-release-asset@v1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
upload_url: ${{ steps.create_release.outputs.upload_url }}
asset_path: dist/${{ env.name }}
asset_name: ${{ env.name }}
asset_content_type: application/zip
================================================
FILE: .github/workflows/setup_and_run_tests.yml
================================================
# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
name: Setup zeus and run tests
on:
push:
branches: [ "main"]
pull_request:
branches: [ "main"]
workflow_dispatch:
jobs:
build:
name: Setup and Run Tests
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.7", "3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v3
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install flake8
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Lint with flake8
run: |
# stop the build if there are Python syntax errors or undefined names
flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
# exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- name: Run tests
run: |
python -m unittest discover tests
================================================
FILE: .gitignore
================================================
__pycache__/
zeus/_pycache__/
*.py[cod]
.ipynb_checkpoints/
examples/.ipynb_checkpoints/
zeus_mcmc.egg-info/
build/
dist/
================================================
FILE: .readthedocs.yaml
================================================
# .readthedocs.yaml
# Read the Docs configuration file
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
# Required
version: 2
# Set the version of Python and other tools you might need
build:
os: ubuntu-20.04
tools:
python: "3.9"
# Build documentation in the docs/ directory with Sphinx
sphinx:
configuration: docs/conf.py
# If using Sphinx, optionally build your docs in additional formats such as PDF
# formats:
# - pdf
# Optionally declare the Python requirements required to build your docs
python:
install:
- requirements: docs/requirements.txt
- requirements: requirements.txt
system_packages: true
================================================
FILE: LICENSE
================================================
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================================================
FILE: MANIFEST.in
================================================
include LICENSE README.md requirements.txt
================================================
FILE: README.md
================================================
**zeus is a Python implementation of the Ensemble Slice Sampling method.**
- Fast & Robust *Bayesian Inference*,
- Efficient *Markov Chain Monte Carlo (MCMC)*,
- Black-box inference, no hand-tuning,
- Excellent performance in terms of autocorrelation time and convergence rate,
- Scale to multiple CPUs without any extra effort,
- Automated Convergence diagnostics.
[](https://github.com/minaskar/zeus)
[](https://arxiv.org/abs/2002.06212)
[](https://arxiv.org/abs/2105.03468)
[](https://ascl.net/2008.010)
[](https://travis-ci.com/minaskar/zeus)
[](https://github.com/minaskar/zeus/blob/master/LICENSE)
[](https://zeus-mcmc.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/zeus-mcmc)
## Example
For instance, if you wanted to draw samples from a 10-dimensional Gaussian, you would do something like:
```python
import zeus
import numpy as np
def log_prob(x, ivar):
return - 0.5 * np.sum(ivar * x**2.0)
nsteps, nwalkers, ndim = 1000, 100, 10
ivar = 1.0 / np.random.rand(ndim)
start = np.random.randn(nwalkers,ndim)
sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar])
sampler.run_mcmc(start, nsteps)
chain = sampler.get_chain(flat=True)
```
## Documentation
Read the docs at [zeus-mcmc.readthedocs.io](https://zeus-mcmc.readthedocs.io)
## Installation
To install ``zeus`` using ``pip`` run:
```bash
pip install zeus-mcmc
```
To install ``zeus`` in a [[Ana]Conda](https://conda.io/projects/conda/en/latest/index.html) environment use:
```bash
conda install -c conda-forge zeus-mcmc
```
## Attribution
Please cite the following papers if you found this code useful in your research:
```bash
@article{karamanis2021zeus,
title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference},
author={Karamanis, Minas and Beutler, Florian and Peacock, John A},
journal={arXiv preprint arXiv:2105.03468},
year={2021}
}
@article{karamanis2020ensemble,
title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions},
author = {Karamanis, Minas and Beutler, Florian},
journal = {arXiv preprint arXiv: 2002.06212},
year = {2020}
}
```
## Licence
Copyright 2019-2021 Minas Karamanis and contributors.
zeus is free software made available under the GPL-3.0 License. For details see the `LICENSE` file.
================================================
FILE: docs/Makefile
================================================
# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = .
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
================================================
FILE: docs/_static/copybutton.js
================================================
// originally taken from scikit-learn's Sphinx theme
$(document).ready(function() {
/* Add a [>>>] button on the top-right corner of code samples to hide
* the >>> and ... prompts and the output and thus make the code
* copyable.
* Note: This JS snippet was taken from the official python.org
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var pre = div.find('pre');
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'font-family': 'monospace', 'padding-left': '0.2em', 'padding-right': '0.2em'
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button.attr('title', hide_text);
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// wrapped in a span to work with .nextUntil() (see later)
jthis.find('pre:has(.gt)').contents().filter(function() {
return ((this.nodeType == 3) && (this.data.trim().length > 0));
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});
// define the behavior of the button when it's clicked
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function() {
var button = $(this);
button.parent().find('.go, .gp, .gt').hide();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
button.css('text-decoration', 'line-through');
button.attr('title', show_text);
},
function() {
var button = $(this);
button.parent().find('.go, .gp, .gt').show();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
button.css('text-decoration', 'none');
button.attr('title', hide_text);
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================================================
FILE: docs/_static/default.css
================================================
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*[id]:before {
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margin-top: -60px;
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================================================
FILE: docs/api/autocorr.rst
================================================
===============================
Autocorrelation Time Estimation
===============================
.. autofunction:: zeus.AutoCorrTime
================================================
FILE: docs/api/callbacks.rst
================================================
=============
The Callbacks
=============
Starting from version 2.4.0, ``zeus`` supports callback functions. Those are functions that are
called in every iteration of a run. Among other things, these can be used to monitor useful quantities,
assess convergence, and save the chains to disk. Custom callback functions can also be used. Sampling
terminates if a callback function returns ``True`` and continues running while ``False`` or ``None`` is
returned.
Autocorrelation Callback
========================
.. autoclass:: zeus.callbacks.AutocorrelationCallback
:members:
Split-R Callback
================
.. autoclass:: zeus.callbacks.SplitRCallback
:members:
Parallel Split-R Callback
=========================
.. autoclass:: zeus.callbacks.ParallelSplitRCallback
:members:
Minimum Iterations Callback
===========================
.. autoclass:: zeus.callbacks.MinIterCallback
:members:
Save Progress Callback
======================
.. autoclass:: zeus.callbacks.SaveProgressCallback
:members:
================================================
FILE: docs/api/moves.rst
================================================
==================
The Ensemble Moves
==================
``zeus`` was originally built on the ``Differential`` and ``Gaussian`` moves. Starting from version
2.0.0, ``zeus`` supports a mixture of different moves/proposals. Moves are recipes that the walkers
follow to cross the parameter space. The ``Differential Move`` remains the default choice but we also
provide a suite of additional moves, such as the ``Global Move`` that can be used when sampling from
challenging target distributions (e.g. highly dimensional multimodal distributions).
Differential Move
=================
.. autoclass:: zeus.moves.DifferentialMove
:members:
Gaussian Move
=============
.. autoclass:: zeus.moves.GaussianMove
:members:
Global Move
===========
.. autoclass:: zeus.moves.GlobalMove
:members:
KDE Move
========
.. autoclass:: zeus.moves.KDEMove
:members:
Random Move
===========
.. autoclass:: zeus.moves.RandomMove
:members:
================================================
FILE: docs/api/parallel.rst
================================================
=============================
The Chain Manager & MPI Tools
=============================
The ``Chain Manager`` can be used to parallelize ``zeus``. The benefits of this appoach is that
the ``Chain Manager`` can parallelize many chains and walkers simultaneously. See the Cookbook
for more information.
.. autoclass:: zeus.ChainManager
:members:
================================================
FILE: docs/api/plotting.rst
================================================
================
Plotting Results
================
Cornerplot
==========
.. currentmodule:: zeus
.. autofunction:: zeus.cornerplot
================================================
FILE: docs/api/sampler.rst
================================================
==========================
The Ensemble Slice Sampler
==========================
.. autoclass:: zeus.EnsembleSampler
:members:
================================================
FILE: docs/api.rst
================================================
=============
API Reference
=============
**zeus** consists mainly of six parts:
.. toctree::
:maxdepth: 2
api/sampler
api/callbacks
api/moves
api/autocorr
api/parallel
api/plotting
================================================
FILE: docs/conf.py
================================================
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
sys.path.insert(0, os.path.abspath('../../zeus/'))
import zeus
import sphinx_bootstrap_theme
# -- Project information -----------------------------------------------------
project = 'zeus'
copyright = '2019-2022, Minas Karamanis'
author = 'Minas Karamanis'
# The full version, including alpha/beta/rc tags
release = zeus.__version__
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.intersphinx',
'sphinx.ext.mathjax',
'sphinx.ext.napoleon',
#'numpydoc',
'nbsphinx',
'sphinx.ext.coverage',
'IPython.sphinxext.ipython_console_highlighting',
]
# Generate the API documentation when building
autosummary_generate = True
numpydoc_show_class_members = False
napoleon_google_docstring = True
napoleon_numpy_docstring = True
napoleon_include_init_with_doc = False
napoleon_include_private_with_doc = False
napoleon_include_special_with_doc = False
napoleon_use_admonition_for_examples = False
napoleon_use_admonition_for_notes = False
napoleon_use_admonition_for_references = False
napoleon_use_ivar = False
napoleon_use_param = True
napoleon_use_rtype = True
napoleon_use_keyword = True
napoleon_custom_sections = None
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
source_suffix = ".rst"
master_doc = "index"
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']
#exclude_patterns = ['_build']
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'bootstrap'
html_favicon = "_static/favicon.png"
# (Optional) Logo. Should be small enough to fit the navbar (ideally 24x24).
# Path should be relative to the ``_static`` files directory.
#html_logo = "my_logo.png"
# Theme options are theme-specific and customize the look and feel of a
# theme further.
html_theme_options = {
'navbar_title': "zeus",
'navbar_site_name': "Contents",
'navbar_links': [
("Cookbook", "cookbook"),
("FAQ", "faq"),
("API", "api"),
],
'navbar_sidebarrel': False, # Render the next and previous page links in navbar. (Default: true)
'navbar_pagenav': False, # Render the current pages TOC in the navbar. (Default: true)
'navbar_pagenav_name': "Page", # Tab name for the current pages TOC. (Default: "Page")
# Global TOC depth for "site" navbar tab. (Default: 1)
# Switching to -1 shows all levels.
'globaltoc_depth': 2,
# Include hidden TOCs in Site navbar?
#
# Note: If this is "false", you cannot have mixed ``:hidden:`` and
# non-hidden ``toctree`` directives in the same page, or else the build
# will break.
#
# Values: "true" (default) or "false"
'globaltoc_includehidden': "true",
# HTML navbar class (Default: "navbar") to attach to <div> element.
# For black navbar, do "navbar navbar-inverse"
#'navbar_class': "navbar navbar-inverse",
# Fix navigation bar to top of page?
# Values: "true" (default) or "false"
'navbar_fixed_top': "true",
#'bootswatch_theme': "united",
#'bootswatch_theme': "paper",
#'bootswatch_theme': "cosmo",
'bootswatch_theme': "readable",
#'bootswatch_theme': "flatly",
#'bootswatch_theme': "Yeti",
'bootstrap_version': "3",
'body_max_width' : '100%',
#'body_min_width' : '70%',
}
html_theme_path = sphinx_bootstrap_theme.get_html_theme_path()
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If true, links to the reST sources are added to the pages.
html_show_sourcelink = False
# Add the 'copybutton' javascript, to hide/show the prompt in code
# examples, originally taken from scikit-learn's doc/conf.py
#def setup(app):
# app.add_javascript('copybutton.js')
# app.add_stylesheet('default.css')
#html_css_files = ['_static',]
#html_context = {'css_files': ['_static/default.css', # override wide tables in RTD theme
#],}
#autodoc_default_options = {
# 'exclude-members': '__init__'
#}
#autoclass_content = ["class"]
================================================
FILE: docs/cookbook.rst
================================================
========
Cookbook
========
MCMC Sampling recipes
=====================
- `Sampling from a multivariate Normal distribution`_
Demonstrates how to sample from a correlated multivariate Gaussian distribution and how to perform
the post-processing of the samples.
- `Fitting a model to data`_
In this recipe we are going to produce some mock data and use them to illustrate how *zeus* works in
realistic scenarios.
- `Sampling from multimodal distributions`_
In this recipe we will demonstrate how one can use ``zeus`` with the ``Moves`` interface to sample
efficiently from challenging high-dimensional multimodal distributions.
.. _Sampling from a multivariate Normal distribution: notebooks/normal_distribution.ipynb
.. _Fitting a model to data: notebooks/datafit.ipynb
.. _Sampling from multimodal distributions: notebooks/multimodal.ipynb
Parallelisation recipes
=======================
- `Multiprocessing`_
Use many CPUs to sample from an expensive-to-evaluate probability distribution even faster.
- `MPI and ChainManager`_
Distribute calculation to huge computer clusters.
.. _Multiprocessing: notebooks/multiprocessing.ipynb
.. _MPI and ChainManager: notebooks/MPI.ipynb
.. raw:: html
<style>
.red {color:red; font-weight:bold;}
.b {color:#0000FF; background-color:white;}
</style>
.. role:: red
Convergence Diagnostics and Saving Progress recipes :red:`NEW`
==============================================================
- `Automated Convergence Diagnostics using the callback interface`_ :red:`NEW`
In this recipe we are going to use the callback interface to monitor convergence and stop sampling automatically.
- `Saving progress to disk using h5py`_ :red:`NEW`
In this recipe we are going to use the callback interface to save the samples and their corresponding log-probability values in a ``.h5`` file.
- `Parallel sampling using MPI and Gelman-Rubin convergence diagnostics`_ :red:`NEW`
In this recipe we are going to use the ChainManager to run zeus in parallel using MPI and terminate sampling automatically using Gelman-Rubin diagnostics.
- `Tracking metadata using the blobs interface`_
We introduce the blobs interface. An easy way for the user to track arbitrary metadata for every sample of the chain.
.. _Automated Convergence Diagnostics using the callback interface: notebooks/convergence.ipynb
.. _Saving progress to disk using h5py: notebooks/progress.ipynb
.. _Parallel sampling using MPI and Gelman-Rubin convergence diagnostics: notebooks/GR.ipynb
.. _Tracking metadata using the blobs interface: notebooks/blobs.ipynb
.. toctree::
:maxdepth: 2
:hidden:
notebooks/normal_distribution.ipynb
notebooks/datafit.ipynb
notebooks/multimodal.ipynb
notebooks/multiprocessing.ipynb
notebooks/MPI.ipynb
notebooks/blobs.ipynb
notebooks/progress.ipynb
notebooks/convergence.ipynb
notebooks/GR.ipynb
================================================
FILE: docs/faq.rst
================================================
==========================
Frequently Asked Questions
==========================
What is the acceptance rate of ``zeus``?
========================================
Unlike most MCMC methods, ``zeus`` acceptance rate isn't varying during a run. As a matter of fact,
its acceptance rate is identically 1, always. This is because of the Slice Sampler at its core.
Why should I use zeus instead of other MCMC samplers?
=====================================================
The first reason you should think of using ``zeus`` is due to the fact that it doesn't require
any hand tuning at all. There is no need to adjust any hyperparameters or provide a proposal
distribution.
Moreover, unlike other black-box MCMC methods ``zeus`` is more robust to the curse of
dimensionality and handle challenging distributions better.
What are the walkers?
=====================
Walkers are the members of the ensemble. They are interacting parallel chains which collectively explore
the posterior mass.
How many walkers should I use?
==============================
At least twice the number of parameters of your problem. A good rule of thump is to use between 2 and 4
times the number of parameters. If your distribution has multiple modes/peaks you may want to increase
the number of walkers.
How should I initialize the positions of the walkers?
=====================================================
A good practice seems to be to initialize the walkers from a small ball close to the *Maximum a Posteriori*
estimate. After a few autocorrelation times the walkers would have explored the rest of the usefull regions
of the parameter space (i.e. the typical set), producing a great number of independent samples.
How long should I run ``zeus``?
===============================
You don't have to run ``zeus`` for very long. If your goal is to produce 2D/1D contours and/or 1-sigma/2-sigma
constraints for your parameters, running ``zeus`` for a few autocorrelation times (e.g. 10) is more than enough.
You can also use the implemented callback functions (see Cookbook and API) to automate the termination of a run.
What can I do if the first few iterations take too long to complete?
====================================================================
This usually occurs when the walkers are initialised closed to each other. During the first ``10-100`` iterations
``zeus`` is tuning its proposal scale ``mu``. During that time ``zeus`` may do more model evaluations than usual.
Tuning of ``mu`` is faster if initialised from a large value. We thus recommend to set ``mu`` to an large value
(e.g. ``mu=1e3``) initially in the ``EnsembleSampler``.
Is there any way to reduce the computational cost per iteration?
================================================================
``zeus``'s power originates in its flexibility. During each iteration, the walkers move along straight lines (i.e. slices)
that cross the posterior mass. The construction of a slice involves two steps, an initial expanding/stepping-out and a subsequent
shrinking procedure. One can decrease the computational cost per iteration by forcing ``zeus`` to conduct no expansions. This is
achieved by setting ``light_mode=True`` in the ``EnsembleSampler`` at the cost of reduced flexibility. If the target distribution
is close to normal/Gaussian one then this procedure can cut the cost to half.
What are the ``Moves`` and which one should I use?
==================================================
``zeus`` was originally built on the ``Differential`` and ``Gaussian`` moves. Starting from version
2.0.0, ``zeus`` supports a mixture of different moves/proposals. Moves are recipes that the walkers
follow to cross the parameter space. The ``Differential Move`` remains the default choice but we also
provide a suite of additional moves, such as the ``Global Move`` that can be used when sampling from
challenging target distributions (e.g. highly dimensional multimodal distributions).
The move(s) you should use depends on the particular target distribution. The ``Differential Move``
seems to be a good choice for most distributions and 50-50 mixture of the ``Global Move`` and
``Local Move`` seem to perform very well in highly dimensional multimodal distributions when used
after the burnin period is over.
================================================
FILE: docs/index.rst
================================================
.. title:: zeus documentation
.. figure:: ./../logo.png
:scale: 30 %
:align: center
.. raw:: html
<style>
.red {color:red; font-weight:bold;}
.b {color:#0000FF; background-color:white;}
</style>
.. role:: red
**zeus is a Python implementation of the Ensemble Slice Sampling method.**
- Fast & Robust *Bayesian Inference*,
- Efficient *Markov Chain Monte Carlo (MCMC)*,
- Black-box inference, no hand-tuning,
- Excellent performance in terms of autocorrelation time and convergence rate,
- Scale to multiple CPUs without any extra effort,
- Automated Convergence diagnostics. :red:`NEW`
.. image:: https://img.shields.io/badge/GitHub-minaskar%2Fzeus-blue
:target: https://github.com/minaskar/zeus
.. image:: https://img.shields.io/badge/arXiv-2002.06212-red
:target: https://arxiv.org/abs/2002.06212
.. image:: https://img.shields.io/badge/arXiv-2105.03468-brightgreen
:target: https://arxiv.org/abs/2105.03468
.. image:: https://img.shields.io/badge/ascl-2008.010-blue.svg?colorB=262255
:target: https://ascl.net/2008.010
.. image:: https://travis-ci.com/minaskar/zeus.svg?token=xnVWRZ3TFg1zxQYQyLs4&branch=master
:target: https://travis-ci.com/minaskar/zeus
.. image:: https://img.shields.io/badge/License-GPLv3-blue.svg
:target: https://github.com/minaskar/zeus/blob/master/LICENSE
.. image:: https://readthedocs.org/projects/zeus-mcmc/badge/?version=latest&token=4455dbf495c5a4eaba52de26ac56628aad85eb3eadc90badfd1703d0a819a0f9
:target: https://zeus-mcmc.readthedocs.io/en/latest/?badge=latest
.. image:: https://pepy.tech/badge/zeus-mcmc
:target: https://pepy.tech/project/zeus-mcmc
Basic use
=========
For instance, if you wanted to draw samples from a *10-dimensional Normal distribution*, you would do something like:
.. code:: Python
import zeus
import numpy as np
def log_prob(x, ivar):
return - 0.5 * np.sum(ivar * x**2.0)
nsteps, nwalkers, ndim = 1000, 100, 10
ivar = 1.0 / np.random.rand(ndim)
start = np.random.randn(nwalkers, ndim)
sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar])
sampler.run_mcmc(start, nsteps)
chain = sampler.get_chain(flat=True)
Installation
============
To install ``zeus`` using ``pip`` run:
.. code:: bash
pip install zeus-mcmc
To install ``zeus`` in a `[Ana]Conda <https://conda.io/projects/conda/en/latest/index.html>`_ environment use:
.. code:: bash
conda install -c conda-forge zeus-mcmc
Getting Started
===============
- See the :doc:`cookbook` page to learn how to perform Bayesian Inference using ``zeus``.
- See the :doc:`faq` page for frequently asked questions about ``zeus``' operation.
- See the :doc:`api` page for detailed API documentation.
Citation
========
Please cite the following papers if you found this code useful in your
research::
@article{karamanis2021zeus,
title={zeus: A Python implementation of Ensemble Slice Sampling for efficient Bayesian parameter inference},
author={Karamanis, Minas and Beutler, Florian and Peacock, John A},
journal={arXiv preprint arXiv:2105.03468},
year={2021}
}
@article{karamanis2020ensemble,
title = {Ensemble slice sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions},
author = {Karamanis, Minas and Beutler, Florian},
journal = {arXiv preprint arXiv: 2002.06212},
year = {2020}
}
Licence
=======
Copyright 2019-2021 Minas Karamanis and contributors.
``zeus`` is free software made available under the ``GPL-3.0 License``.
Changelog
=========
**2.4.1 (17/11/21)**
- Introduced ``ParallelSplitRCallback`` callback function for checking Gelman-Rubin statistics during ``MPI`` runs.
**2.4.0 (01/11/21)**
- Introduced callback interface.
- Added convergence diagnostics.
- Added ``H5DF`` support.
**2.3.1 (03/08/21)**
- Raise exception if model fails.
**2.3.0 (25/02/21)**
- Added ``sample`` method which advances the chain as a generator.
- Added ``light_mode``. When used, ``light_mode`` can significantly reduce the number of log likelihood evaluations
and increase the general efficiency of the algorithm. ``light_mode`` works by performing no expansions after the
end of the tuning phase. The scale factor is set to its opttimal value. This works best for approximately Gaussian
distributions.
- Added ``start=None`` support for ``run_mcmc``. When used, the sampler proceeds from the last known position of the walkers.
- Added support for both ``thin`` and ``thin_by`` arguments.
**2.2.2 (21/02/21)**
- Added ``log_prob0`` and ``blobs0`` arguments in ``run``.
- Added ``get_last_sample()``, ``get_last_log_prob()`` and ``get_last_blobs()`` methods.
**2.2.0 (03/11/20)**
- Improved vectorization.
**2.1.1 (29/10/20)**
- Added ``blobs`` interface to track arbitrary metadata.
- Updated ``GlobalMove`` and multimodal example.
- Fixed minor bugs.
**2.0.0 (05/10/20)**
- Added new ``Moves`` interface (e.g. ``DifferentialMove``, ``GlobalMove``, etc).
- Plotting capabilities (i.e. ``cornerplot``).
- Updated docs.
- Fixed minor bugs.
**1.2.2 (19/09/20)**
- ``Sampler`` class is deprecated. New ``EnsembleSampler`` class in now available.
- New estimator for the Integrated Autocorrelation Time. It's accurate even with short chains.
- Updated ``ChainManager`` to handle thousands of CPUs.
**1.2.1 (04/08/20)**
- Changed to Flat-not-nested philosophy for diagnostics and ``ChainManager``.
**1.2.0 (03/08/20)**
- Extended ``ChainManager`` with ``gather``, ``scatter``, and ``bcast`` tools.
**1.1.0 (02/08/20)**
- Added ``ChainManager`` to deploy into supercomputing clusters, parallelizing both chains and walkers.
- Added Convergence diagnostic tools (Gelman-Rubin, Geweke).
**1.0.7 (11/05/20)**
- Improved parallel distribution of tasks
.. toctree::
:maxdepth: 1
:caption: Cookbook Recipes
:hidden:
Overview <cookbook>
notebooks/normal_distribution.ipynb
notebooks/datafit.ipynb
notebooks/multiprocessing.ipynb
notebooks/MPI.ipynb
.. toctree::
:maxdepth: 3
:caption: Help & Reference
:hidden:
faq
api
================================================
FILE: docs/make.bat
================================================
@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=.
set BUILDDIR=_build
if "%1" == "" goto help
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd
================================================
FILE: docs/notebooks/GR.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parallel sampling using MPI and Gelman-Rubin convergence diagnostics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To take advantage of modern high performance computing facilities such as clusters with hundreds of CPUs we recommend to use ``MPI`` instead of ``multiprocessing``.\n",
"\n",
"To do this we will use the ``ChainManager`` included in ``zeus``. We will also use the ``ParallelSplitRCallback`` function to check the Gelman-Rubing convergence diagnostic during the run and terminate sampling automatically.\n",
"\n",
"In order to run this example, copy and paste the following script into a file called 'test_mpi.py' and run the following command in the terminal:\n",
"\n",
"```\n",
"mpiexec -n 8 python3 test_mpi_gr.py\n",
"```\n",
"\n",
"This will spawn 8 ``MPI`` processes and divide them into 2 independent chains of 10 walkers each. Unfortunately ``MPI`` is not compatible with ``Jupyter`` notebooks."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save this as 'test_mpi_gr.py'\n",
"\n",
"```python\n",
"\n",
"import numpy as np\n",
"import zeus\n",
"from zeus import ChainManager\n",
"\n",
"ndim = 20\n",
"nwalkers = 2 * ndim\n",
"nsteps = 10000\n",
"nchains = 2\n",
"\n",
"def log_prob(x):\n",
" return -0.5 * np.sum(x**2.0)\n",
"\n",
"start = 1e-2 * np.random.randn(nwalkers, ndim) + 20.0\n",
"\n",
"\n",
"with ChainManager(nchains) as cm:\n",
" rank = cm.get_rank\n",
"\n",
" cb = zeus.callbacks.ParallelSplitRCallback(epsilon=0.01, chainmanager=cm)\n",
" sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, pool=cm.get_pool)\n",
" sampler.run_mcmc(start, nsteps, callbacks=cb)\n",
" chain = sampler.get_chain(flat=True, discard=0.5)\n",
" \n",
" if rank == 0:\n",
" print('R =', cb.estimates, flush=True)\n",
" np.save('chain_'+str(rank)+'.npy', chain)\n",
"\n",
"```"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "288px"
},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: docs/notebooks/MPI.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parallelizing sampling using MPI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To take advantage of modern high performance computing facilities such as clusters with hundreds of CPUs we recommend to use ``MPI`` instead of ``multiprocessing``.\n",
"\n",
"To do this we will use the ``ChainManager`` included in ``zeus``.\n",
"\n",
"In order to run this example, copy and paste the following script into a file called 'test_mpi.py' and run the following command in the terminal:\n",
"\n",
"```\n",
"mpiexec -n 8 python3 test_mpi.py\n",
"```\n",
"\n",
"This will spawn 8 ``MPI`` processes and divide them into 2 independent chains of 10 walkers each. Unfortunately ``MPI`` is not compatible with ``Jupyter`` notebooks."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save this as 'test_mpi.py'\n",
"\n",
"```python\n",
"\n",
"import numpy as np\n",
"import zeus\n",
"from zeus import ChainManager\n",
"\n",
"ndim = 5\n",
"nwalkers = 2 * ndim\n",
"nsteps = 100\n",
"nchains = 2\n",
"\n",
"def log_prob(x):\n",
" return -0.5 * np.sum(x**2.0)\n",
"\n",
"start = np.random.randn(nwalkers, ndim)\n",
"\n",
"\n",
"with ChainManager(nchains) as cm:\n",
" rank = cm.get_rank\n",
"\n",
" sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, pool=cm.get_pool)\n",
" sampler.run_mcmc(start, nsteps)\n",
" chain = sampler.get_chain(flat=True, discard=0.5)\n",
" \n",
" np.save('chain_'+str(rank)+'.npy', chain)\n",
"\n",
"```"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {
"height": "calc(100% - 180px)",
"left": "10px",
"top": "150px",
"width": "288px"
},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
================================================
FILE: docs/notebooks/blobs.ipynb
================================================
{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "Python 3.7.6 64-bit ('nbodykit-env': conda)",
"display_name": "Python 3.7.6 64-bit ('nbodykit-env': conda)",
"metadata": {
"interpreter": {
"hash": "92a13e2fbe78b004d0fb7131bfe04f8cf6342cb1d93c749c200bfd2478bfd7dd"
}
}
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"source": [
"# Blobs and Metadata"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"We introduce the blobs interface. An easy way for the user to track arbitrary metadata for every sample of the chain."
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"## Tracking the value of the log-prior\n",
"\n",
"We can easily use blobs to store the value of the log-prior at each step in the chain by doing something like:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Initialising ensemble of 32 walkers...\n",
"Sampling progress : 100%|██████████| 100/100 [00:00<00:00, 160.45it/s](100, 32)\n",
"(3200,)\n",
"\n"
]
}
],
"source": [
"import zeus\n",
"\n",
"import numpy as np\n",
"\n",
"def log_prior(x):\n",
" return -0.5 * np.dot(x,x)\n",
"\n",
"def log_like(x):\n",
" return -0.5 * np.dot(x,x) / 0.1**2.0\n",
"\n",
"def log_prob(x):\n",
" lp = log_prior(x)\n",
" if not np.isfinite(lp):\n",
" return -np.inf, -np.inf\n",
" ll = log_like(x)\n",
" if not np.isfinite(ll):\n",
" return lp, -np.inf\n",
" return lp + ll, lp\n",
"\n",
"nwalkers, ndim = 32, 3\n",
"start = np.random.randn(nwalkers, ndim)\n",
"sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\n",
"sampler.run_mcmc(start, 100)\n",
"\n",
"log_prior_samps = sampler.get_blobs()\n",
"flat_log_prior_samps = sampler.get_blobs(flat=True)\n",
"\n",
"print(log_prior_samps.shape) # (100, 32)\n",
"print(flat_log_prior_samps.shape) # (3200,)"
]
},
{
"source": [
"Once this is done running, the “blobs” stored by the sampler will be a ``(nsteps, nwalkers)`` numpy array with the value of the log prior at every sample."
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"## Tracking multiple species of metadata\n",
"\n",
"When handling multiple species of metadata, it can be useful to name them. This can be done using the ``blobs_dtype`` argument of the ``EnsembleSampler``. For instance, to save the mean of the parameters as well as the log-prior we could do something like:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Initialising ensemble of 32 walkers...\n",
"Sampling progress : 100%|██████████| 100/100 [00:00<00:00, 137.06it/s](100, 32)\n",
"(100, 32)\n",
"(3200,)\n",
"(3200,)\n",
"\n"
]
}
],
"source": [
"def log_prob(params):\n",
" lp = log_prior(params)\n",
" if not np.isfinite(lp):\n",
" return -np.inf, -np.inf\n",
" ll = log_like(params)\n",
" if not np.isfinite(ll):\n",
" return lp, -np.inf\n",
" return lp + ll, lp, np.mean(params)\n",
"\n",
"nwalkers, ndim = 32, 3\n",
"start = np.random.randn(nwalkers, ndim)\n",
"\n",
"# Here are the important lines\n",
"dtype = [(\"log_prior\", float), (\"mean\", float)]\n",
"sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob, blobs_dtype=dtype)\n",
"\n",
"sampler.run_mcmc(start, 100)\n",
"\n",
"blobs = sampler.get_blobs()\n",
"log_prior_samps = blobs[\"log_prior\"]\n",
"mean_samps = blobs[\"mean\"]\n",
"print(log_prior_samps.shape)\n",
"print(mean_samps.shape)\n",
"\n",
"flat_blobs = sampler.get_blobs(flat=True)\n",
"flat_log_prior_samps = flat_blobs[\"log_prior\"]\n",
"flat_mean_samps = flat_blobs[\"mean\"]\n",
"print(flat_log_prior_samps.shape)\n",
"print(flat_mean_samps.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
}
================================================
FILE: docs/notebooks/convergence.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automated Convergence Diagnostics using the callback interface"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Knowing when to stop sampling can be very useful when running expensive MCMC procedures. Ideally, if we want unbiased results, we want the sampler to stop after it has converged to the stationary phase (i.e. after the burn-in/warm-up period is over). To do this we can combine different ``Convergence Diagnostics`` offered as ``callback functions`` by zeus.\n",
"\n",
"We will start by setting the simple problem of sampling from a bimodal Gaussian mixture distribution:"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"import zeus\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"nsteps, nwalkers, ndim = 100000, 50, 5\n",
"\n",
"def log_prob(x):\n",
" return np.logaddexp(-0.5 * np.sum(x ** 2), -0.5 * np.sum((x - 4.0) ** 2))\n",
"\n",
"x0 = 1e-3*np.random.randn(nwalkers,ndim) + 5.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where ``nsteps`` would be the maximum number of steps/iterations, ``ivar`` would be the inverse variance (precision) of the normal target distribution that we are going to sample from, and ``x0`` is the starting position of the walkers.\n",
"\n",
"We will then define all the convergence diagnostics that we will use as ``callback functions``.\n",
"\n",
"First of all, we would like check the integrated autocorrelation time (IAT) of the chain every ``ncheck=100`` steps and make sure that we don't stop running unless the length of the chain is longer than ``nact=50`` times the IAT and that the rate of change of IAT drops bellow 1 percent (i.e. ``dact=0.01``). We would also discard the first half of the chain (i.e. ``discard=0.5``) before computing the IAT."
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"cb0 = zeus.callbacks.AutocorrelationCallback(ncheck=100, dact=0.01, nact=50, discard=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will then use the **Split-R Gelman-Rubin statistic** computed using different segments (i.e. split into ``nsplits=2`` parts) of the same chain and decide that the sampler has converged if its value drops bellow ``(1+epsilon)=1.01``."
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"cb1 = zeus.callbacks.SplitRCallback(ncheck=100, epsilon=0.01, nsplits=2, discard=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, just to make sure that the sampler doesn't stop too early, we will set the minimum number of iterations to ``nmin=500``."
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [],
"source": [
"cb2 = zeus.callbacks.MinIterCallback(nmin=500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now ready to start sampling and require that all three of the aforementioned criteria are satisfied before sampling terminates."
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Initialising ensemble of 50 walkers...\n",
"Sampling progress : 2%|▏ | 1898/100000 [00:13<13:42, 119.22it/s]"
]
}
],
"source": [
"sampler = zeus.EnsembleSampler(nwalkers, ndim, log_prob)\n",
"sampler.run_mcmc(x0, nsteps, callbacks=[cb0, cb1, cb2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We noticed that the sampler automatically stopped running after approximately ``1900`` iterations. We can now have a look at the ``split-R`` statistics and the IAT estimate."
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Sampling progress : 2%|▏ | 1900/100000 [00:13<11:50, 138.00it/s]\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 864x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tau = cb0.estimates\n",
"R = cb1.estimates\n",
"\n",
"N = np.arange(len(tau)) * 100\n",
"\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"plt.subplot(121)\n",
"\n",
"plt.plot(N, tau, lw=2.5)\n",
"plt.title('Integrated Autocorrelation Time', fontsize=14)\n",
"plt.xlabel('Iterations', fontsize=14)\n",
"plt.ylabel(r'$\\tau$', fontsize=14)\n",
"\n",
"\n",
"plt.subplot(122)\n",
"\n",
"plt.plot(N, R, lw=2.5)\n",
"plt.title('Split-R Gelman-Rubin Statistic', fontsize=14)\n",
"plt.xlabel('Iterations', fontsize=14)\n",
"plt.ylabel(r'$R$', fontsize=14)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also have a look at the traces of the walkers."
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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
gitextract_8ypbapnn/
├── .github/
│ └── workflows/
│ ├── release_to_pypi.yml
│ └── setup_and_run_tests.yml
├── .gitignore
├── .readthedocs.yaml
├── LICENSE
├── MANIFEST.in
├── README.md
├── docs/
│ ├── Makefile
│ ├── _static/
│ │ ├── copybutton.js
│ │ └── default.css
│ ├── api/
│ │ ├── autocorr.rst
│ │ ├── callbacks.rst
│ │ ├── moves.rst
│ │ ├── parallel.rst
│ │ ├── plotting.rst
│ │ └── sampler.rst
│ ├── api.rst
│ ├── conf.py
│ ├── cookbook.rst
│ ├── faq.rst
│ ├── index.rst
│ ├── make.bat
│ ├── notebooks/
│ │ ├── GR.ipynb
│ │ ├── MPI.ipynb
│ │ ├── blobs.ipynb
│ │ ├── convergence.ipynb
│ │ ├── datafit.ipynb
│ │ ├── multimodal.ipynb
│ │ ├── multiprocessing.ipynb
│ │ ├── normal_distribution.ipynb
│ │ └── progress.ipynb
│ └── requirements.txt
├── requirements.txt
├── setup.cfg
├── setup.py
├── tests/
│ ├── test_autocorr.py
│ ├── test_fwrapper.py
│ ├── test_sampler.py
│ └── test_samples.py
└── zeus/
├── __init__.py
├── _version.py
├── autocorr.py
├── callbacks.py
├── ensemble.py
├── fwrapper.py
├── moves.py
├── parallel.py
├── plotting.py
└── samples.py
SYMBOL INDEX (114 symbols across 12 files)
FILE: tests/test_autocorr.py
function get_chain (line 6) | def get_chain(seed=42, ndim=5, N=100000):
function test_1d (line 15) | def test_1d(seed=42):
FILE: tests/test_fwrapper.py
function func0 (line 7) | def func0(x):
function func1 (line 11) | def func1(x, mu):
function func2 (line 15) | def func2(x, mu, ivar):
function test_none (line 19) | def test_none(func=func0,seed=42):
function test_args1 (line 29) | def test_args1(func=func1,seed=42):
function test_args2 (line 40) | def test_args2(func=func2,seed=42):
function test_kwargs1 (line 52) | def test_kwargs1(func=func1,seed=42):
function test_kwargs2 (line 63) | def test_kwargs2(func=func2,seed=42):
function test_argskwargs (line 75) | def test_argskwargs(func=func2,seed=42):
FILE: tests/test_sampler.py
function logp (line 6) | def logp(x):
function test_mean (line 10) | def test_mean(logp=logp,seed=42):
function test_std (line 23) | def test_std(logp=logp,seed=42):
function test_ncall (line 34) | def test_ncall(seed=42):
FILE: tests/test_samples.py
function test_chain (line 7) | def test_chain(seed=42):
function test_flatten (line 22) | def test_flatten(seed=42):
function test_multiple (line 40) | def test_multiple():
FILE: zeus/autocorr.py
function _autocorr_func_1d (line 4) | def _autocorr_func_1d(x, norm=True):
function _autocorr_time_1d (line 38) | def _autocorr_time_1d(y, c=5.0, method='mk'):
function AutoCorrTime (line 80) | def AutoCorrTime(samples, c=5.0, method='mk'):
FILE: zeus/callbacks.py
class AutocorrelationCallback (line 10) | class AutocorrelationCallback:
method __init__ (line 28) | def __init__(self, ncheck=100, dact=0.01, nact=10, discard=0.5, trigge...
method __call__ (line 40) | def __call__(self, i, x, y):
class SplitRCallback (line 73) | class SplitRCallback:
method __init__ (line 87) | def __init__(self, ncheck=100, epsilon=0.05, nsplits=2, discard=0.5, t...
method __call__ (line 98) | def __call__(self, i, x, y):
method estimate_Rhat (line 137) | def estimate_Rhat(self, means, vars, N):
class MinIterCallback (line 157) | class MinIterCallback:
method __init__ (line 165) | def __init__(self, nmin=1000):
method __call__ (line 168) | def __call__(self, i, x, y):
class ParallelSplitRCallback (line 188) | class ParallelSplitRCallback:
method __init__ (line 204) | def __init__(self, ncheck=100, epsilon=0.01, nsplits=2, discard=0.5, t...
method __call__ (line 220) | def __call__(self, i, x, y):
method estimate_Rhat (line 265) | def estimate_Rhat(self, means, vars, N):
class SaveProgressCallback (line 285) | class SaveProgressCallback:
method __init__ (line 294) | def __init__(self, filename='./chains.h5', ncheck=100):
method __call__ (line 304) | def __call__(self, i, x, y):
method __save (line 327) | def __save(self, x, y):
method __initialize_and_save (line 335) | def __initialize_and_save(self, x, y):
FILE: zeus/ensemble.py
class EnsembleSampler (line 17) | class EnsembleSampler:
method __init__ (line 45) | def __init__(self,
method run (line 142) | def run(self, *args, **kwargs):
method reset (line 147) | def reset(self):
method get_chain (line 154) | def get_chain(self, flat=False, thin=1, discard=0):
method get_log_prob (line 177) | def get_log_prob(self, flat=False, thin=1, discard=0):
method get_blobs (line 199) | def get_blobs(self, flat=False, thin=1, discard=0):
method chain (line 221) | def chain(self):
method act (line 232) | def act(self):
method ess (line 243) | def ess(self):
method ncall (line 254) | def ncall(self):
method efficiency (line 265) | def efficiency(self):
method scale_factor (line 276) | def scale_factor(self):
method get_last_sample (line 286) | def get_last_sample(self):
method get_last_sample (line 292) | def get_last_sample(self):
method get_last_log_prob (line 299) | def get_last_log_prob(self):
method get_last_blobs (line 306) | def get_last_blobs(self):
method summary (line 314) | def summary(self):
method compute_log_prob (line 333) | def compute_log_prob(self, coords):
method run_mcmc (line 388) | def run_mcmc(self,
method sample (line 445) | def sample(self,
class sampler (line 724) | class sampler(EnsembleSampler):
method __init__ (line 725) | def __init__(self, *args, **kwargs):
FILE: zeus/fwrapper.py
class _FunctionWrapper (line 3) | class _FunctionWrapper(object):
method __init__ (line 17) | def __init__(self, f, args, kwargs):
method __call__ (line 22) | def __call__(self, x):
FILE: zeus/moves.py
class DifferentialMove (line 17) | class DifferentialMove:
method __init__ (line 33) | def __init__(self, tune=True, mu0=1.0):
method get_direction (line 38) | def get_direction(self, X, mu):
class GaussianMove (line 66) | class GaussianMove:
method __init__ (line 81) | def __init__(self, tune=False, mu0=1.0, cov=None):
method get_direction (line 87) | def get_direction(self, X, mu):
class GlobalMove (line 118) | class GlobalMove:
method __init__ (line 140) | def __init__(self, tune=True, mu0=1.0, rescale_cov=0.001, n_components...
method get_direction (line 151) | def get_direction(self, X, mu):
class KDEMove (line 192) | class KDEMove:
method __init__ (line 210) | def __init__(self, tune=False, mu0=1.0, bw_method=None):
method get_direction (line 220) | def get_direction(self, X, mu):
class RandomMove (line 250) | class RandomMove:
method __init__ (line 266) | def __init__(self, tune=True, mu0=1.0):
method get_direction (line 271) | def get_direction(self, X, mu):
FILE: zeus/parallel.py
function _import_mpi (line 7) | def _import_mpi(use_dill=False):
class MPIPool (line 21) | class MPIPool:
method __init__ (line 35) | def __init__(self, comm=None):
method wait (line 59) | def wait(self):
method map (line 81) | def map(self, worker, tasks):
method close (line 143) | def close(self):
method is_master (line 152) | def is_master(self):
method is_worker (line 156) | def is_worker(self):
method __enter__ (line 160) | def __enter__(self):
method __exit__ (line 164) | def __exit__(self, *args):
function split_ranks (line 169) | def split_ranks(N_ranks, N_chunks):
class ChainManager (line 189) | class ChainManager:
method __init__ (line 204) | def __init__(self, nchains=1, comm=None):
method __enter__ (line 220) | def __enter__(self):
method __exit__ (line 258) | def __exit__(self, exc_type, exc_value, exc_traceback):
method get_rank (line 276) | def get_rank(self):
method get_pool (line 284) | def get_pool(self):
method gather (line 293) | def gather(self, x, root):
method scatter (line 312) | def scatter(self, x, root):
method allgather (line 331) | def allgather(self, x):
method bcast (line 348) | def bcast(self, x, root):
FILE: zeus/plotting.py
function cornerplot (line 3) | def cornerplot(samples,
function _quantile (line 194) | def _quantile(x, q, weights=None):
FILE: zeus/samples.py
class samples (line 3) | class samples:
method __init__ (line 12) | def __init__(self, ndim, nwalkers):
method extend (line 19) | def extend(self, n, blobs):
method save (line 43) | def save(self, x, logp, blobs):
method chain (line 58) | def chain(self):
method length (line 69) | def length(self):
method flatten (line 79) | def flatten(self, discard=0, thin=1):
method logprob (line 94) | def logprob(self):
method flatten_logprob (line 104) | def flatten_logprob(self, discard=0, thin=1):
method flatten_blobs (line 118) | def flatten_blobs(self, discard=0, thin=1):
Condensed preview — 49 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,116K chars).
[
{
"path": ".github/workflows/release_to_pypi.yml",
"chars": 2262,
"preview": "name: Publish zeus to PyPI / GitHub\n\non:\n push:\n branches:\n - main\n paths:\n - 'zeus/_version.py' # Onl"
},
{
"path": ".github/workflows/setup_and_run_tests.yml",
"chars": 1264,
"preview": "# This workflow will install Python dependencies, run tests and lint with a variety of Python versions\nname: Setup zeus "
},
{
"path": ".gitignore",
"chars": 121,
"preview": "__pycache__/\nzeus/_pycache__/\n*.py[cod]\n.ipynb_checkpoints/\nexamples/.ipynb_checkpoints/\nzeus_mcmc.egg-info/\nbuild/\ndist"
},
{
"path": ".readthedocs.yaml",
"chars": 662,
"preview": "# .readthedocs.yaml\n# Read the Docs configuration file\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html f"
},
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "MANIFEST.in",
"chars": 43,
"preview": "include LICENSE README.md requirements.txt\n"
},
{
"path": "README.md",
"chars": 3039,
"preview": "\n\n**zeus is a Python implementation of the Ensemble Slice Sampling method.**\n\n- Fast & Robust *Bayesian"
},
{
"path": "docs/Makefile",
"chars": 634,
"preview": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line, and also\n# from the "
},
{
"path": "docs/_static/copybutton.js",
"chars": 2652,
"preview": "// originally taken from scikit-learn's Sphinx theme\n$(document).ready(function() {\n /* Add a [>>>] button on the top"
},
{
"path": "docs/_static/default.css",
"chars": 1854,
"preview": "body { color: #444444 !important; }\n\nh1 { font-size: 40px !important; }\nh2 { font-size: 32px !important; }\nh3 { font-siz"
},
{
"path": "docs/api/autocorr.rst",
"chars": 132,
"preview": "===============================\nAutocorrelation Time Estimation\n===============================\n\n.. autofunction:: zeus."
},
{
"path": "docs/api/callbacks.rst",
"chars": 1030,
"preview": "=============\nThe Callbacks\n=============\n\nStarting from version 2.4.0, ``zeus`` supports callback functions. Those are "
},
{
"path": "docs/api/moves.rst",
"chars": 945,
"preview": "==================\nThe Ensemble Moves\n==================\n\n``zeus`` was originally built on the ``Differential`` and ``Ga"
},
{
"path": "docs/api/parallel.rst",
"chars": 351,
"preview": "=============================\nThe Chain Manager & MPI Tools\n=============================\n\nThe ``Chain Manager`` can be "
},
{
"path": "docs/api/plotting.rst",
"chars": 135,
"preview": "================\nPlotting Results\n================\n\n\nCornerplot\n==========\n\n.. currentmodule:: zeus\n\n.. autofunction:: z"
},
{
"path": "docs/api/sampler.rst",
"chars": 131,
"preview": "==========================\nThe Ensemble Slice Sampler\n==========================\n\n.. autoclass:: zeus.EnsembleSampler\n "
},
{
"path": "docs/api.rst",
"chars": 205,
"preview": "=============\nAPI Reference\n=============\n\n**zeus** consists mainly of six parts:\n\n.. toctree::\n :maxdepth: 2\n\n api/"
},
{
"path": "docs/conf.py",
"chars": 5288,
"preview": "# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common op"
},
{
"path": "docs/cookbook.rst",
"chars": 2959,
"preview": "========\nCookbook\n========\n\nMCMC Sampling recipes\n=====================\n\n- `Sampling from a multivariate Normal distribu"
},
{
"path": "docs/faq.rst",
"chars": 4300,
"preview": "==========================\nFrequently Asked Questions\n==========================\n\nWhat is the acceptance rate of ``zeus`"
},
{
"path": "docs/index.rst",
"chars": 6241,
"preview": ".. title:: zeus documentation\n\n.. figure:: ./../logo.png\n :scale: 30 %\n :align: center\n\n.. raw:: html\n\n <style>"
},
{
"path": "docs/make.bat",
"chars": 795,
"preview": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=sp"
},
{
"path": "docs/notebooks/GR.ipynb",
"chars": 3006,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Parallel sampling using MPI and G"
},
{
"path": "docs/notebooks/MPI.ipynb",
"chars": 2605,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Parallelizing sampling using MPI\""
},
{
"path": "docs/notebooks/blobs.ipynb",
"chars": 4754,
"preview": "{\n \"metadata\": {\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_ext"
},
{
"path": "docs/notebooks/convergence.ipynb",
"chars": 324761,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Automated Convergence Diagnostics"
},
{
"path": "docs/notebooks/datafit.ipynb",
"chars": 1545524,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Fitting a model to data\"\n ]\n }"
},
{
"path": "docs/notebooks/multiprocessing.ipynb",
"chars": 4059,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Parallelizing sampling using mult"
},
{
"path": "docs/notebooks/progress.ipynb",
"chars": 3365,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Incrementally saving progress to "
},
{
"path": "docs/requirements.txt",
"chars": 56,
"preview": "sphinx_bootstrap_theme\nnbsphinx\nIPython\ndocutils==0.17.1"
},
{
"path": "requirements.txt",
"chars": 73,
"preview": "numpy\nscipy>=1.5.0\ntqdm\nsetuptools\npytest\nmatplotlib\nseaborn\nscikit-learn"
},
{
"path": "setup.cfg",
"chars": 908,
"preview": "[metadata]\nname = zeus-mcmc\nversion = attr: zeus._version.version\nauthor = Minas Karamanis\nauthor_email = minaskar@gmail"
},
{
"path": "setup.py",
"chars": 37,
"preview": "import setuptools\n\nsetuptools.setup()"
},
{
"path": "tests/test_autocorr.py",
"chars": 503,
"preview": "import numpy as np\nimport pytest\n\nfrom zeus.autocorr import _autocorr_time_1d\n\ndef get_chain(seed=42, ndim=5, N=100000):"
},
{
"path": "tests/test_fwrapper.py",
"chars": 2166,
"preview": "import numpy as np\nimport pytest\n\n#from zeus import fwrapper\nfrom zeus.fwrapper import _FunctionWrapper\n\ndef func0(x):\n "
},
{
"path": "tests/test_sampler.py",
"chars": 1525,
"preview": "import numpy as np\nimport pytest\n\nimport zeus\n\ndef logp(x):\n return -0.5 * np.sum((x-1.0)**2.0)\n\n\ndef test_mean(logp="
},
{
"path": "tests/test_samples.py",
"chars": 1278,
"preview": "import pytest\nimport numpy as np\n\nfrom zeus import samples\n\n\ndef test_chain(seed=42):\n np.random.seed(seed)\n nstep"
},
{
"path": "zeus/__init__.py",
"chars": 1072,
"preview": "__bibtex__ = \"\"\"\n@article{karamanis2021zeus,\n title={zeus: A Python implementation of Ensemble Slice Samplin"
},
{
"path": "zeus/_version.py",
"chars": 17,
"preview": "version = \"2.5.4\""
},
{
"path": "zeus/autocorr.py",
"chars": 3163,
"preview": "import numpy as np\nfrom scipy.fft import fft, ifft\n\ndef _autocorr_func_1d(x, norm=True):\n \"\"\"\n Autocorrelation Fun"
},
{
"path": "zeus/callbacks.py",
"chars": 12604,
"preview": "import numpy as np\nfrom .autocorr import AutoCorrTime\n\ntry:\n import h5py\nexcept ImportError:\n h5py = None\n\n\nclass "
},
{
"path": "zeus/ensemble.py",
"chars": 28517,
"preview": "import numpy as np\nfrom tqdm import tqdm\nimport logging\n\ntry:\n from collections.abc import Iterable\nexcept ImportErro"
},
{
"path": "zeus/fwrapper.py",
"chars": 1016,
"preview": "import numpy as np\n\nclass _FunctionWrapper(object):\n \"\"\"\n This is a hack to make the likelihood function pickleabl"
},
{
"path": "zeus/moves.py",
"chars": 9074,
"preview": "import numpy as np \nfrom itertools import permutations\nimport random\n\ntry:\n from scipy.stats import gaussian_kde\nexce"
},
{
"path": "zeus/parallel.py",
"chars": 10661,
"preview": "import sys\nimport atexit\n\n\nMPI = None\n\ndef _import_mpi(use_dill=False):\n global MPI\n try:\n from mpi4py impo"
},
{
"path": "zeus/plotting.py",
"chars": 8514,
"preview": "import numpy as np\n\ndef cornerplot(samples,\n labels=None,\n weights=None,\n leve"
},
{
"path": "zeus/samples.py",
"chars": 3990,
"preview": "import numpy as np\n\nclass samples:\n '''\n Creates object that stores the samples.\n Args:\n ndim (int): Num"
}
]
// ... and 2 more files (download for full content)
About this extraction
This page contains the full source code of the minaskar/zeus GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 49 files (89.1 MB), approximately 1.1M tokens, and a symbol index with 114 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.