Flickr数据集

class dgl.data.FlickrDataset(raw_dir=None, force_reload=False, verbose=False, transform=None, reorder=False)[source]

Bases: DGLBuiltinDataset

用于节点分类的Flickr数据集来自GraphSAINT: 基于图采样的归纳学习方法

该数据集的任务是根据在线图像的描述和常见属性对图像类型进行分类。

Flickr 数据集统计：

节点数：89,250
边数：899,756
类别数量：7
节点特征大小：500

Parameters:

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: False
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.
reorder (bool) – Whether to reorder the graph using reorder_graph(). Default: False.

num_classes

节点类的数量

Type:: int

示例

>>> from dgl.data import FlickrDataset
>>> dataset = FlickrDataset()
>>> dataset.num_classes
7
>>> g = dataset[0]
>>> # get node feature
>>> feat = g.ndata['feat']
>>> # get node labels
>>> labels = g.ndata['label']
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']

__getitem__(idx)[source]

获取图形对象

Parameters:

idx (int) – Item index, FlickrDataset has only one graph object

Returns:

The graph contains:

ndata['label']: node label
ndata['feat']: node feature
ndata['train_mask']: mask for training node set
ndata['val_mask']: mask for validation node set
ndata['test_mask']: mask for test node set

Return type:

dgl.DGLGraph

__len__()[source]: 数据集中图的数量。