Metadata-Version: 2.1
Name: ogb_lite
Version: 0.0.2
Summary: Open Graph Benchmark Lite (ogb_lite) is a subset of the ogb project. It supports library-agnostic loaders and it does not require torch.
Home-page: https://github.com/CrawlScript/ogb_lite
Author: Jun Hu
Author-email: hujunxianligong@gmail.com
License: MIT
Description: 
        Open Graph Benchmark Lite (ogb_lite)
        ====================================
        
        Open Graph Benchmark Lite (ogb_lite) is a subset of the ogb project. It supports library-agnostic loaders and it does not require torch. 
        
        99.99% of the code is copied from the OGB project: 
        
        
        * `https://github.com/snap-stanford/ogb <https://github.com/snap-stanford/ogb>`_
        
        We only make some small changes such that you can use ogb_lite without installing torch.
        
        Installation
        ------------
        
        .. code-block:: bash
        
           pip install ogb_lite
        
        Tutorial
        --------
        
        ogb_lite only contains three library-agnostic loaders: ``NodePropPredDataset``\ , ``LinkPropPredDataset``\ , and ``GraphPropPredDataset``.
        
        **NodePropPredDataset:**
        
        .. code-block:: python
        
           # coding=utf-8
           from ogb_lite.nodeproppred import NodePropPredDataset
        
           dataset = NodePropPredDataset(name="ogbn-proteins")
        
           split_idx = dataset.get_idx_split()
           train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"]
           graph, label = dataset[0] # graph: library-agnostic graph object
        
           print(graph, label)
           print(train_idx, valid_idx, test_idx)
        
        **LinkPropPredDataset:**
        
        .. code-block:: python
        
           # coding=utf-8
        
           from ogb_lite.linkproppred import LinkPropPredDataset
        
           dataset = LinkPropPredDataset(name="ogbl-ppa")
        
           split_edge = dataset.get_edge_split()
           train_edge, valid_edge, test_edge = split_edge["train"], split_edge["valid"], split_edge["test"]
           graph = dataset[0]  # graph: library-agnostic graph object
        
           print(graph)
           print(train_edge, valid_edge, test_edge)
        
        **GraphPropPredDataset:**
        
        .. code-block:: python
        
           # coding=utf-8
        
           from ogb_lite.graphproppred import GraphPropPredDataset
        
           dataset = GraphPropPredDataset(name="ogbg-molhiv")
        
           split_idx = dataset.get_idx_split()
           train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"]
        
           graph, label = dataset[0]  # graph: library-agnostic graph object
           print(graph, label)
           print(train_idx, valid_idx, test_idx)
        
        Citing OGB
        ----------
        
        If you use OGB datasets in your work, please cite the OGB paper (Bibtex below).
        
        .. code-block::
        
           @article{hu2020ogb,
             title={Open Graph Benchmark: Datasets for Machine Learning on Graphs},
             author={Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec},
             journal={arXiv preprint arXiv:2005.00687},
             year={2020}
           }
        
Keywords: pytorch,graph machine learning,graph representation learning,graph neural networks
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: torch
