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https://github.com/stan-dev/pystan2

PyStan, the Python interface to Stan
https://github.com/stan-dev/pystan2

machine-learning probabilistic-programming python stan statistics

Last synced: about 2 months ago
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PyStan, the Python interface to Stan

Host: GitHub
URL: https://github.com/stan-dev/pystan2
Owner: stan-dev
License: gpl-3.0
Archived: true
Created: 2013-05-24T01:21:21.000Z (over 11 years ago)
Default Branch: develop
Last Pushed: 2021-02-17T17:59:24.000Z (over 3 years ago)
Last Synced: 2024-04-14T01:46:30.785Z (5 months ago)
Topics: machine-learning, probabilistic-programming, python, stan, statistics
Language: Python
Homepage:
Size: 53.4 MB
Stars: 918
Watchers: 51
Forks: 191
Open Issues: 0
Metadata Files:
- Readme: README.rst
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

README

        PyStan: The Python Interface to Stan

====================================

.. image:: https://raw.githubusercontent.com/stan-dev/logos/master/logo.png

    :alt: Stan logo

    :scale: 50 %

|pypi| |travis| |appveyor| |zenodo|

.. tip:: PyStan 3 is available for Linux and macOS users. Visit the `PyStan 3 documentation `_ for details. PyStan 2 is not maintained.

**PyStan** provides a Python interface to Stan, a package for Bayesian inference

using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo.

For more information on `Stan `_ and its modeling language,

see the Stan User's Guide and Reference Manual at `http://mc-stan.org/

`_.

Important links

---------------

- HTML documentation: https://pystan2.readthedocs.org

- Issue tracker: https://github.com/stan-dev/pystan/issues

- Source code repository: https://github.com/stan-dev/pystan

- Stan: http://mc-stan.org/

- Stan User's Guide and Reference Manual (pdf) available at http://mc-stan.org

Related projects

----------------

- ArviZ: `Exploratory analysis of Bayesian models with Python `_ by @arviz-devs

- Jupyter tool: `StanMagic `_ by @Arvinds-ds

- Jupyter tool: `JupyterStan `_ by @janfreyberg

- Scikit-learn integration: `pystan-sklearn `_ by @rgerkin.

Projects using PyStan

---------------------

- BAMBI: `BAyesian Model-Building Interface `_ by @bambinos

- hBayesDM: `hierarchical Bayesian modeling of Decision-Making tasks `_ by @CCS-Lab

- Orbit: `Object-oRiented BayesIan Timeseries models `_ by @uber

- Prophet: `Timeseries forecasting `_ by @facebook

Similar projects

----------------

- PyMC3: https://docs.pymc.io/

- emcee: https://emcee.readthedocs.io/en/stable/

PyStan3 / Stan3

---------------

The development of PyStan3 with updated API can be found under `stan-dev/pystan-next `_

Detailed Installation Instructions

----------------------------------

Detailed installation instructions can be found in the

`doc/installation_beginner.md `_ file.

Windows Installation Instructions

---------------------------------

Detailed installation instructions for Windows can be found in docs under `PyStan on Windows `_

Quick Installation (Linux and macOS)

------------------------------------

`NumPy  `_ and `Cython `_

(version 0.22 or greater) are required. `matplotlib `_

is optional. ArviZ is recommended for visualization and analysis.

PyStan and the required packages may be installed from the `Python Package Index

`_ using ``pip``.

::

   pip install pystan

Alternatively, if Cython (version 0.22 or greater) and NumPy are already

available, PyStan may be installed from source with the following commands

::

   git clone --recursive https://github.com/stan-dev/pystan.git

   cd pystan

   python setup.py install

To install latest development version user can also use ``pip``

::

    pip install git+https://github.com/stan-dev/pystan

If you encounter an ``ImportError`` after compiling from source, try changing

out of the source directory before attempting ``import pystan``. On Linux and

OS X ``cd /tmp`` will work.

``make`` (``mingw32-make`` on Windows) is a requirement for building from source.

Example

-------

.. code-block:: python

    import pystan

    import numpy as np

    import matplotlib.pyplot as plt

    schools_code = """

    data {

        int J; // number of schools

        real y[J]; // estimated treatment effects

        real sigma[J]; // s.e. of effect estimates

    }

    parameters {

        real mu;

        real tau;

        real eta[J];

    }

    transformed parameters {

        real theta[J];

        for (j in 1:J)

            theta[j] = mu + tau * eta[j];

    }

    model {

        eta ~ normal(0, 1);

        y ~ normal(theta, sigma);

    }

    """

    schools_dat = {'J': 8,

                   'y': [28,  8, -3,  7, -1,  1, 18, 12],

                   'sigma': [15, 10, 16, 11,  9, 11, 10, 18]}

    sm = pystan.StanModel(model_code=schools_code)

    fit = sm.sampling(data=schools_dat, iter=1000, chains=4)

    print(fit)

    eta = fit.extract(permuted=True)['eta']

    np.mean(eta, axis=0)

    # if matplotlib is installed (optional, not required), a visual summary and

    # traceplot are available

    fit.plot()

    plt.show()

    # updated traceplot can be plotted with

    import arviz as az

    az.plot_trace(fit)

.. |pypi| image:: https://badge.fury.io/py/pystan.png

    :target: https://badge.fury.io/py/pystan

    :alt: pypi version

.. |travis| image:: https://travis-ci.org/stan-dev/pystan.png?branch=master

    :target: https://travis-ci.org/stan-dev/pystan

    :alt: travis-ci build status

.. |appveyor| image:: https://ci.appveyor.com/api/projects/status/49e69yl5ngxkpmab?svg=true

    :target: https://ci.appveyor.com/project/pystan/pystan

    :alt: appveyor-ci build status

.. |zenodo| image:: https://zenodo.org/badge/10256919.svg

    :target: https://zenodo.org/badge/latestdoi/10256919

    :alt: zenodo citation DOI