AutoGL (i.e. Auto Graph Learning) is an automatic machine learning (AutoML) toolkit specified for graph datasets & tasks.
It will automatically handle all the stages involved in graph learning problems, including dataset download & management, data preprocessing and feature engineering, neural architecture search, model selection and training, hyper-parameter tuning and ensemble, which will reduce human labors and biases in the machine learning loop by a large scale. This toolkit also serves as a platform for users to implement and test their own auto or graph learning methods. The workflow below gives the overall framework of AutoGL.
Actively under development by THUMNLab
Feel free to open issues or contact us at email@example.com if you have any comments or suggestions!
from autogl.datasets import build_datasets_from_name from autogl.solver import AutoNodeClassifier cora = build_datasets_from_name('cora') solver = AutoNodeClassifier( graphModelList=['gcn', 'gat'] ) solver.fit(cora) prediction = solver.predict(cora)
Please first install the requirements of AutoGL and then install with the following command.
pip install autogl
git clone https://github.com/THUMNLab/AutoGL
python setup.py install