Source code for

"""Wiki-CS Dataset"""
import itertools
import json
import os

import numpy as np

from .. import backend as F
from ..convert import graph
from ..transforms import reorder_graph, to_bidirected
from .dgl_dataset import DGLBuiltinDataset
from .utils import _get_dgl_url, generate_mask_tensor, load_graphs, save_graphs

[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 <>`_ 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 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/") 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)