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[](http://mypy-lang.org/)
[](https://codecov.io/gh/py-why/pywhy-graphs)
# PyWhy-Graphs
pywhy-graphs is a Python graph library that extends [networkx](https://github.com/networkx/networkx) with the notion of a `MixedEdgeGraph` to implement a light-weight API for causal graphical structures that contain mixed-edges and contain causal graph traversal algorithms.
Note: The API is subject to change without deprecation cycles due to the current work-in-progress `MixedEdgeGraph` class in networkx. For more information, follow the PR at https://github.com/networkx/networkx/pull/5947
## Why?
Representation of causal graphical models in Python are severely lacking.
PyWhy-Graphs implements a graphical API layer for ADMG, CPDAG and PAG. For causal DAGs, we recommend using the `networkx.DiGraph` class and
ensuring acylicity via `networkx.is_directed_acyclic_graph` function.
Existing packages that aim to represent causal graphs either break from the networkX API, or only implement a subset of the relevant causal graphs. By keeping in-line with the robust NetworkX API, we aim to ensure a consistent user experience and a gentle introduction to causal graphical models. A `MixedEdgeGraph` instance is a composition of networkx graphs and has a similar API, with the additional notion of an "edge type", which specifies what edge type subgraph any function should operate over. For example:
```Python
# adds a directed edge from x to y
G.add_edge('x', 'y', edge_type='directed')
# adds a bidirected edge from x to y
G.add_edge('x', 'y', edge_type='bidirected')
```
Moreover, sampling from causal models is non-trivial, but a requirement for benchmarking many causal algorithms in discovery, ID, estimation and more. We aim to provide simulation modules that are easily connected with causal graphs to provide a simple robust API for modeling causal graphs and then simulating data.
# Documentation
See the [development version documentation](https://py-why.github.io/pywhy-graphs/dev/index.html).
Or see [stable version documentation](https://py-why.github.io/pywhy-graphs/stable/index.html)
# Installation
Installation is best done via `pip` or `conda`. For developers, they can also install from source using `pip`. See [installation page](https://py-why.github.io/pywhy-graphs/dev/installation.html) for full details.
## Dependencies
We aim to provide a very light-weight dependency structure. Minimally, pywhy-graphs requires:
* Python (>=3.8)
* numpy
* scipy
* networkx
Additional functionality may be required when running unit-tests and documentation.
## User Installation
If you already have a working installation of numpy, scipy and networkx, the easiest way to install pywhy-graphs is using `pip`:
# doesn't work until we make an official release :p
pip install -U pywhy-graphs
To install the package from github, clone the repository and then `cd` into the directory. You can then use `poetry` to install:
poetry install
# for vizualizing graph functionality
poetry install --extras viz
# if you would like an editable install of dodiscover for dev purposes
pip install -e .
pip install https://api.github.com/repos/py-why/pywhy-graphs/zipball/main
# Contributing
Pywhy-Graphs is always looking for new contributors to help make the package better, whether it is algorithms, documentation, examples of graph usage, and more! Contributing to Pywhy-Graphs will be rewarding because you will contribute to a much needed package for causal inference.
See our [contributing guide](https://github.com/py-why/pywhy-graphs/CONTRIBUTING.md) for more details.
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