data-loadogb.rst

.. _guide_cn-data-pipeline-loadogb:

4.5 使用ogb包导入OGB数据集
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:ref:`(English Version) <guide-data-pipeline-loadogb>`

`Open Graph Benchmark (OGB) <https://ogb.stanford.edu/docs/home/>`__ 是一个图深度学习的基准数据集。
官方的 `ogb <https://github.com/snap-stanford/ogb>`__ 包提供了用于下载和处理OGB数据集到
:class:`dgl.data.DGLGraph` 对象的API。本节会介绍它们的基本用法。

首先使用pip安装ogb包：

.. code:: 

    pip install ogb


以下代码显示了如何为 *Graph Property Prediction* 任务加载数据集。

.. code:: 

    # 载入OGB的Graph Property Prediction数据集
    import dgl
    import torch
    from ogb.graphproppred import DglGraphPropPredDataset
    from dgl.dataloading import GraphDataLoader
    
    def _collate_fn(batch):
        # 小批次是一个元组(graph, label)列表
        graphs = [e[0] for e in batch]
        g = dgl.batch(graphs)
        labels = [e[1] for e in batch]
        labels = torch.stack(labels, 0)
        return g, labels
    
    # 载入数据集
    dataset = DglGraphPropPredDataset(name='ogbg-molhiv')
    split_idx = dataset.get_idx_split()
    # dataloader
    train_loader = GraphDataLoader(dataset[split_idx["train"]], batch_size=32, shuffle=True, collate_fn=_collate_fn)
    valid_loader = GraphDataLoader(dataset[split_idx["valid"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)
    test_loader = GraphDataLoader(dataset[split_idx["test"]], batch_size=32, shuffle=False, collate_fn=_collate_fn)

加载 *Node Property Prediction* 数据集类似，但要注意的是这种数据集只有一个图对象。

.. code:: 

    # 载入OGB的Node Property Prediction数据集
    from ogb.nodeproppred import DglNodePropPredDataset
    
    dataset = DglNodePropPredDataset(name='ogbn-proteins')
    split_idx = dataset.get_idx_split()
    
    # there is only one graph in Node Property Prediction datasets
    # 在Node Property Prediction数据集里只有一个图
    g, labels = dataset[0]
    # 获取划分的标签
    train_label = dataset.labels[split_idx['train']]
    valid_label = dataset.labels[split_idx['valid']]
    test_label = dataset.labels[split_idx['test']]

每个 *Link Property Prediction* 数据集也只包括一个图。

.. code::

    # 载入OGB的Link Property Prediction数据集
    from ogb.linkproppred import DglLinkPropPredDataset
    
    dataset = DglLinkPropPredDataset(name='ogbl-ppa')
    split_edge = dataset.get_edge_split()
    
    graph = dataset[0]
    print(split_edge['train'].keys())
    print(split_edge['valid'].keys())
    print(split_edge['test'].keys())