# pgmpy

**Repository Path**: titzp/pgmpy

## Basic Information

- **Project Name**: pgmpy
- **Description**: Python Library for Inference (Causal and Probabilistic) and learning in Bayesian Networks
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: dev
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2022-05-07
- **Last Updated**: 2022-05-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

pgmpy
=====
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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](https://gitter.im/pgmpy/pgmpy).

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:
```bash
$ conda install -c ankurankan pgmpy
```

For installing through pip:
```bash
$ 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](https://github.com/pgmpy/pgmpy/issues).

Before opening a pull request, please have a look at our [contributing guide](
https://github.com/pgmpy/pgmpy/blob/dev/Contributing.md)

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):
```bash
$ 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](https://github.com/pgmpy/pgmpy/blob/dev/LICENSE)