Auto Graph Learning
AutoGL is developed for researchers and developers to quickly conduct autoML on the graph datasets & tasks.
The workflow below shows the overall framework of AutoGL.
datasets to maintain dataset for graph-based machine learning, which is based on Dataset in PyTorch Geometric with some support added to corporate with the auto solver framework.
Different graph-based machine learning tasks are solved by different
AutoGL solvers, which make use of four main modules to automatically solve given tasks, namely
auto feature engineer,
hyperparameter optimization, and
Currently, the following algorithms are supported in AutoGL:
This toolkit also serves as a platform for users to implement and test their own autoML or graph-based machine learning models.
Please make sure you meet the following requirements before installing AutoGL.
Python >= 3.6.0
see https://pytorch.org/ for installation.
see https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html for installation.
Install from pip
Run the following command to install this package through
pip install auto-graph-learning
Install from source
Run the following command to install this package from the source.
git clone https://github.com/THUMNLab/AutoGL.git cd AutoGL python setup.py install
Install for development
If you are a developer of the AutoGL project, please use the following command to create a soft link, then you can modify the local package without install them again.
pip install -e .
Please refer to our documentation to see the detailed documentation.
You can also make the documentation locally. First, please install sphinx and sphinx-rtd-theme:
pip install -U Sphinx pip install sphinx-rtd-theme
Then, make an html documentation by:
cd docs make clean && make html
The documentation will be automatically generated under