WN18Dataset¶
-
class
dgl.data.
WN18Dataset
(reverse=True, raw_dir=None, force_reload=False, verbose=True, transform=None)[source]¶ Bases:
dgl.data.knowledge_graph.KnowledgeGraphDataset
WN18 link prediction dataset.
The WN18 dataset was introduced in Translating Embeddings for Modeling Multi-relational Data. It included the full 18 relations scraped from WordNet for roughly 41,000 synsets. When creating the dataset, a reverse edge with reversed relation types are created for each edge by default.
WN18 dataset statistics:
Nodes: 40943
Number of relation types: 18
Number of reversed relation types: 18
Label Split:
Train: 141442
Valid: 5000
Test: 5000
- Parameters
reverse (bool) – Whether to add reverse edge. 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. TheDGLGraph
object will be transformed before every access.
Examples
>>> dataset = WN18Dataset() >>> g = dataset.graph >>> e_type = g.edata['e_type'] >>> >>> # get data split >>> train_mask = g.edata['train_mask'] >>> val_mask = g.edata['val_mask'] >>> >>> train_set = th.arange(g.num_edges())[train_mask] >>> val_set = th.arange(g.num_edges())[val_mask] >>> >>> # build train_g >>> train_edges = train_set >>> train_g = g.edge_subgraph(train_edges, relabel_nodes=False) >>> train_g.edata['e_type'] = e_type[train_edges]; >>> >>> # build val_g >>> val_edges = th.cat([train_edges, val_edges]) >>> val_g = g.edge_subgraph(val_edges, relabel_nodes=False) >>> val_g.edata['e_type'] = e_type[val_edges]; >>> >>> # Train, Validation and Test >>>
-
__getitem__
(idx)[source]¶ Gets the graph object
- Parameters
idx (int) – Item index, WN18Dataset has only one graph object
- Returns
The graph contains
edata['e_type']
: edge relation typeedata['train_edge_mask']
: positive training edge maskedata['val_edge_mask']
: positive validation edge maskedata['test_edge_mask']
: positive testing edge maskedata['train_mask']
: training edge set mask (include reversed training edges)edata['val_mask']
: validation edge set mask (include reversed validation edges)edata['test_mask']
: testing edge set mask (include reversed testing edges)ndata['ntype']
: node type. All 0 in this dataset
- Return type