Skip to content
dev
Go to file
Code

Latest commit

* Edit examples

* Add back example on learning parameters

* Add hyperlink to blog post in TAN example
a4a9a72

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
dev
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

pgmpy

Build Status Appveyor codecov Codacy Badge Downloads Join the chat at https://gitter.im/pgmpy/pgmpy

pgmpy is a python library for working with Probabilistic Graphical Models.

Documentation and list of algorithms supported is at our official site http://pgmpy.org/
Examples on using pgmpy: https://github.com/pgmpy/pgmpy/tree/dev/examples
Basic tutorial on Probabilistic Graphical models using pgmpy: https://github.com/pgmpy/pgmpy_notebook

Our mailing list is at https://groups.google.com/forum/#!forum/pgmpy .

We have our community chat at gitter.

Dependencies

pgmpy has following non optional dependencies:

  • python 3.6 or higher
  • networkX
  • scipy
  • numpy
  • pytorch

Some of the functionality would also require:

  • tqdm
  • pandas
  • pyparsing
  • statsmodels
  • joblib

Installation

pgmpy is available both on pypi and anaconda. For installing through anaconda use:

$ conda install -c ankurankan pgmpy

For installing through pip:

$ pip install -r requirements.txt  # only if you want to run unittests
$ pip install pgmpy

To install pgmpy from the source code:

$ git clone https://github.com/pgmpy/pgmpy 
$ cd pgmpy/
$ pip install -r requirements.txt
$ python setup.py install

If you face any problems during installation let us know, via issues, mail or at our gitter channel.

Development

Code

Our latest codebase is available on the dev branch of the repository.

Contributing

Issues can be reported at our issues section.

Before opening a pull request, please have a look at our contributing guide

Contributing guide contains some points that will make our life's easier in reviewing and merging your PR.

If you face any problems in pull request, feel free to ask them on the mailing list or gitter.

If you want to implement any new features, please have a discussion about it on the issue tracker or the mailing list before starting to work on it.

Testing

After installation, you can launch the test form pgmpy source directory (you will need to have the pytest package installed):

$ pytest -v

to see the coverage of existing code use following command

$ pytest --cov-report html --cov=pgmpy

Documentation and usage

The documentation is hosted at: http://pgmpy.org/

We use sphinx to build the documentation. To build the documentation on your local system use:

$ cd /path/to/pgmpy/docs
$ make html

The generated docs will be in _build/html

Examples

We have a few example jupyter notebooks here: https://github.com/pgmpy/pgmpy/tree/dev/examples For more detailed jupyter notebooks and basic tutorials on Graphical Models check: https://github.com/pgmpy/pgmpy_notebook/

Citing

Please use the following bibtex for citing pgmpy in your research:

@inproceedings{ankan2015pgmpy,
  title={pgmpy: Probabilistic graphical models using python},
  author={Ankan, Ankur and Panda, Abinash},
  booktitle={Proceedings of the 14th Python in Science Conference (SCIPY 2015)},
  year={2015},
  organization={Citeseer}
}

License

pgmpy is released under MIT License. You can read about our license at here

You can’t perform that action at this time.