Metadata-Version: 2.1
Name: graphlog
Version: 1.0.0
Summary: API to interface with the GraphLog Dataset
Home-page: UNKNOWN
Author: Koustuv Sinha and Shagun Sodhani
Author-email: sshagunsodhani@gmail.com
License: UNKNOWN
Description: [![CircleCI](https://circleci.com/gh/facebookresearch/GraphLog.svg?style=svg&circle-token=3de77dcba6da65107d3946878697d810251e00d9)](https://circleci.com/gh/facebookresearch/GraphLog)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/graphlog)
        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
        
        # GraphLog
        API to interface with the GraphLog Dataset. GraphLog is a multi-purpose, multi-relational graph dataset built using rules grounded in first-order logic.
        
        [Homepage](https://www.cs.mcgill.ca/~ksinha4/graphlog/) | [Paper](https://arxiv.org/abs/2003.06560) | [API Docs](https://graphlog.readthedocs.io/en/latest/)
        
        <img src="docs/images/graphlog_rule.png" width="400">
        
        ### Installation
        
        * Supported Python Version: 3.6+
        * Install PyTorch from https://pytorch.org/get-started/locally/
        * Install pytorch-geometric (and other dependencies) from https://github.com/rusty1s/pytorch_geometric#installation. Make sure that cpu/cuda versions for pytorch and pytorch-geometric etc matches.
        * `pip install graphlog`
        
        ### QuickStart
        
        Check out the notebooks on [Basic Usage](examples/Basic%20Usage.ipynb) and [Advanced Usage](examples/Advanced%20Usage.ipynb) to quickly start playing with GraphLog.
        
        ### Dev Setup
        
        * `pip install -e ".[dev]"`
        * Install pre-commit hooks `pre-commit install`
        * The code is linted using:
            * `black`
            * `flake8`
            * `mypy`
        * All the tests can be run locally using `nox`
        
        ### Experiments
        
        Code for experiments used in our paper are available in `experiments/` folder.
        
        ### Questions
        
        - If you have questions, open an Issue
        - Or, [join our Slack channel](https://join.slack.com/t/logicalml/shared_invite/zt-e7osm7j7-vfIRgJAbEHxYN5D70njvyw) and post your questions / comments!
        - To contribute, open a Pull Request (PR)
        
        ### Contributing
        
        Please open a Pull Request (PR).
        
        ### Citation
        
        If our work is useful for your research, consider citing it using the following bibtex:
        
        ```
        @article{sinha2020graphlog,
          Author = {Koustuv Sinha and Shagun Sodhani and Joelle Pineau and William L. Hamilton},
          Title = {Evaluating Logical Generalization in Graph Neural Networks},
          Year = {2020},
          arxiv = {https://arxiv.org/abs/2003.06560}
        }
        ```
        
        ### License
        
        CC-BY-NC 4.0 (Attr Non-Commercial Inter.)
        
        ### Terms of Use
        
        https://opensource.facebook.com/legal/terms
        
        ### Privacy Policy
        
        https://opensource.facebook.com/legal/privacy
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
