PPIDataset

class dgl.data.PPIDataset(mode='train', raw_dir=None, force_reload=False, verbose=False, transform=None)[source]

Bases: DGLBuiltinDataset

Protein-Protein Interaction dataset for inductive node classification

A toy Protein-Protein Interaction network dataset. The dataset contains 24 graphs. The average number of nodes per graph is 2372. Each node has 50 features and 121 labels. 20 graphs for training, 2 for validation and 2 for testing.

Reference: http://snap.stanford.edu/graphsage/

Statistics:

  • Train examples: 20

  • Valid examples: 2

  • Test examples: 2

Parameters:

  • mode (str) – Must be one of (β€˜train’, β€˜valid’, β€˜test’). Default: β€˜train’

  • 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: 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.

num_labels

Number of labels for each node

Type:

int

labels

Node labels

Type:

Tensor

features

Node features

Type:

Tensor

Examples

>>> dataset = PPIDataset(mode='valid')
>>> num_classes = dataset.num_classes
>>> for g in dataset:
....    feat = g.ndata['feat']
....    label = g.ndata['label']
....    # your code here
>>>
__getitem__(item)[source]

Get the item^th sample.

Parameters:

item (int) – The sample index.

Returns:

graph structure, node features and node labels.

  • ndata['feat']: node features

  • ndata['label']: node labels

Return type:

dgl.DGLGraph

__len__()[source]

Return number of samples in this dataset.