亚马逊评分数据集

class dgl.data.AmazonRatingsDataset(raw_dir=None, force_reload=False, verbose=True, transform=None)[source]

Bases: HeterophilousGraphDataset

来自论文《A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress? <https://arxiv.org/abs/2302.11640>》的Amazon-ratings数据集。

该数据集基于亚马逊产品共同购买数据。节点代表产品（书籍、音乐CD、DVD、VHS录像带），边连接经常一起购买的产品。任务是预测评论者给产品的平均评分。所有可能的评分值被分为五类。节点特征是产品描述中词语的词嵌入的平均值。

统计：

  • 节点数：24492

  • 边数: 186100

  • 类别：5

  • 节点特征：300

  • 10 个训练/验证/测试分割

Parameters:

  • raw_dir (str, optional) – Raw file directory to store the processed data. Default: ~/.dgl/

  • force_reload (bool, optional) – Whether to re-download the data source. Default: False

  • verbose (bool, optional) – Whether to print progress information. Default: True

  • transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access. Default: None

num_classes

节点类的数量

Type:

int

示例

>>> from dgl.data import AmazonRatingsDataset
>>> dataset = AmazonRatingsDataset()
>>> g = dataset[0]
>>> num_classes = dataset.num_classes

>>> # get node features
>>> feat = g.ndata["feat"]

>>> # get the first data split
>>> train_mask = g.ndata["train_mask"][:, 0]
>>> val_mask = g.ndata["val_mask"][:, 0]
>>> test_mask = g.ndata["test_mask"][:, 0]

>>> # get labels
>>> label = g.ndata['label']
__getitem__(idx)

获取索引处的数据对象。

__len__()

数据集中的示例数量。