AIFBDataset

class dgl.data.AIFBDataset(print_every=10000, insert_reverse=True, raw_dir=None, force_reload=False, verbose=True, transform=None)

Bases: RDFGraphDataset

AIFB dataset for node classification task

AIFB DataSet is a Semantic Web (RDF) dataset used as a benchmark in data mining. It records the organizational structure of AIFB at the University of Karlsruhe.

AIFB dataset statistics:

• Nodes: 7262

• Edges: 48810 (including reverse edges)

• Target Category: Personen

• Number of Classes: 4

• Label Split:

  • Train: 140

  • Test: 36

Parameters:

• print_every (int) – Preprocessing log for every X tuples. Default: 10000.

• insert_reverse (bool) – If true, add reverse edge and reverse relations to the final graph. Default: True.

• 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_classes

Number of classes to predict

Type:

int

predict_category

The entity category (node type) that has labels for prediction

Type:

str

Examples

>>> dataset = dgl.data.rdf.AIFBDataset()
>>> graph = dataset[0]
>>> category = dataset.predict_category
>>> num_classes = dataset.num_classes
>>>
>>> train_mask = g.nodes[category].data['train_mask']
>>> test_mask = g.nodes[category].data['test_mask']
>>> label = g.nodes[category].data['label']

__getitem__(idx)

Gets the graph object

Parameters:

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

Returns:

The graph contains:

• ndata['train_mask']: mask for training node set

• ndata['test_mask']: mask for testing node set

• ndata['label']: node labels

Return type:

dgl.DGLGraph

__len__()

The number of graphs in the dataset.

Return type:

int