"""Wiki-CS Dataset"""
import itertools
import os
import json
import numpy as np
from .. import backend as F
from ..convert import graph
from .dgl_dataset import DGLBuiltinDataset
from ..transforms import to_bidirected, reorder_graph
from .utils import generate_mask_tensor, load_graphs, save_graphs, _get_dgl_url
[docs]class WikiCSDataset(DGLBuiltinDataset):
r"""Wiki-CS is a Wikipedia-based dataset for node classification from `Wiki-CS: A Wikipedia-Based
Benchmark for Graph Neural Networks <https://arxiv.org/abs/2007.02901v2>`_
The dataset consists of nodes corresponding to Computer Science articles, with edges based on
hyperlinks and 10 classes representing different branches of the field.
WikiCS dataset statistics:
- Nodes: 11,701
- Edges: 431,726 (note that the original dataset has 216,123 edges but DGL adds
the reverse edges and removes the duplicate edges, hence with a different number)
- Number of classes: 10
- Node feature size: 300
- Number of different train, validation, stopping splits: 20
- Number of test split: 1
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 :class:`~dgl.DGLGraph` object and returns
a transformed version. The :class:`~dgl.DGLGraph` object will be
transformed before every access.
Attributes
----------
num_classes : int
Number of node classes
Examples
--------
>>> from dgl.data import WikiCSDataset
>>> dataset = WikiCSDataset()
>>> dataset.num_classes
10
>>> 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']
>>> stopping_mask = g.ndata['stopping_mask']
>>> test_mask = g.ndata['test_mask']
>>> # The shape of train, val and stopping masks are (num_nodes, num_splits).
>>> # The num_splits is the number of different train, validation, stopping splits.
>>> # Due to the number of test spilt is 1, the shape of test mask is (num_nodes,).
>>> print(train_mask.shape, val_mask.shape, stopping_mask.shape)
(11701, 20) (11701, 20) (11701, 20)
>>> print(test_mask.shape)
(11701,)
"""
def __init__(self, raw_dir=None, force_reload=False, verbose=False, transform=None):
_url = _get_dgl_url('dataset/wiki_cs.zip')
super(WikiCSDataset, self).__init__(name='wiki_cs',
raw_dir=raw_dir,
url=_url,
force_reload=force_reload,
verbose=verbose,
transform=transform)
def process(self):
"""process raw data to graph, labels and masks"""
with open(os.path.join(self.raw_path, 'data.json')) as f:
data = json.load(f)
features = F.tensor(np.array(data['features']), dtype=F.float32)
labels = F.tensor(np.array(data['labels']), dtype=F.int64)
train_masks = np.array(data['train_masks'], dtype=bool).T
val_masks = np.array(data['val_masks'], dtype=bool).T
stopping_masks = np.array(data['stopping_masks'], dtype=bool).T
test_mask = np.array(data['test_mask'], dtype=bool)
edges = [[(i, j) for j in js] for i, js in enumerate(data['links'])]
edges = np.array(list(itertools.chain(*edges)))
src, dst = edges[:, 0], edges[:, 1]
g = graph((src, dst))
g = to_bidirected(g)
g.ndata['feat'] = features
g.ndata['label'] = labels
g.ndata['train_mask'] = generate_mask_tensor(train_masks)
g.ndata['val_mask'] = generate_mask_tensor(val_masks)
g.ndata['stopping_mask'] = generate_mask_tensor(stopping_masks)
g.ndata['test_mask'] = generate_mask_tensor(test_mask)
g = reorder_graph(g, node_permute_algo='rcmk', edge_permute_algo='dst', store_ids=False)
self._graph = g
def has_cache(self):
graph_path = os.path.join(self.save_path, 'dgl_graph.bin')
return os.path.exists(graph_path)
def save(self):
graph_path = os.path.join(self.save_path, 'dgl_graph.bin')
save_graphs(graph_path, self._graph)
def load(self):
graph_path = os.path.join(self.save_path, 'dgl_graph.bin')
g, _ = load_graphs(graph_path)
self._graph = g[0]
@property
def num_classes(self):
return 10
[docs] def __len__(self):
r"""The number of graphs in the dataset."""
return 1
[docs] def __getitem__(self, idx):
r""" Get graph object
Parameters
----------
idx : int
Item index, WikiCSDataset has only one graph object
Returns
-------
:class:`dgl.DGLGraph`
The graph contains:
- ``ndata['feat']``: node features
- ``ndata['label']``: node labels
- ``ndata['train_mask']``: train mask is for retrieving the nodes for training.
- ``ndata['val_mask']``: val mask is for retrieving the nodes for hyperparameter tuning.
- ``ndata['stopping_mask']``: stopping mask is for retrieving the nodes for early stopping criterion.
- ``ndata['test_mask']``: test mask is for retrieving the nodes for testing.
"""
assert idx == 0, "This dataset has only one graph"
if self._transform is None:
return self._graph
else:
return self._transform(self._graph)