MUTAGDataset¶

class dgl.data.MUTAGDataset(print_every=10000, insert_reverse=True, raw_dir=None, force_reload=False, verbose=True, transform=None)[source]¶

Bases: dgl.data.rdf.RDFGraphDataset

MUTAG dataset for node classification task

Mutag dataset statistics:

Nodes: 27163
Edges: 148100 (including reverse edges)
Target Category: d
Number of Classes: 2
Label Split:
- Train: 272
- Test: 68

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

graph¶

Graph structure

Type: dgl.DGLGraph

Examples

>>> dataset = dgl.data.rdf.MUTAGDataset()
>>> 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)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, MUTAGDataset 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__()[source]¶

The number of graphs in the dataset.

Returns
Return type: int