wehub-resource-sync
105 lines
3.0 KiB
Python
105 lines
3.0 KiB
Python
import os
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import easygraph as eg
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import numpy as np
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import scipy.sparse as sp
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from easygraph.classes.graph import Graph
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from .graph_dataset_base import EasyGraphBuiltinDataset
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from .utils import data_type_dict
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from .utils import download
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from .utils import extract_archive
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from .utils import tensor
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class RedditDataset(EasyGraphBuiltinDataset):
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r"""Reddit posts graph (Sept 2014) for community (subreddit) classification.
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Statistics:
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- Nodes: ~232,965
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- Edges: ~114 million (approx.)
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- Features per node: 602
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- Classes: number of subreddit communities
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Data are split by post-day: first 20 days train, then validation (30%), test (rest).
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Parameters
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----------
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self_loop : bool
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Add self-loop edges if True.
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raw_dir, force_reload, verbose, transform : same as EasyGraphBuiltinDataset
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"""
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def __init__(
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self,
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self_loop=False,
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raw_dir=None,
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force_reload=False,
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verbose=True,
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transform=None,
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):
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name = "reddit"
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url = "https://data.dgl.ai/dataset/reddit.zip"
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self.self_loop = self_loop
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super().__init__(
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name=name,
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url=url,
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raw_dir=raw_dir,
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force_reload=force_reload,
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verbose=verbose,
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transform=transform,
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)
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def process(self):
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# Expect two files extracted: reddit_data.npz & reddit_graph.npz
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data = np.load(os.path.join(self.raw_path, "reddit_data.npz"))
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feat = data["feature"] # shape [N, 602]
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labels = data["label"] # shape [N]
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split = data["node_types"] # 1=train,2=val,3=test
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# Load adjacency
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adj = sp.load_npz(os.path.join(self.raw_path, "reddit_graph.npz"))
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src, dst = adj.nonzero()
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if self.self_loop:
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self_loops = np.arange(adj.shape[0])
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src = np.concatenate([src, self_loops])
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dst = np.concatenate([dst, self_loops])
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edges = list(zip(src, dst))
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# Build graph
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g = eg.Graph()
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g.add_edges_from(edges)
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# Assign node features, labels, and masks
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for i in range(feat.shape[0]):
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g.add_node(
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i,
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feat=feat[i],
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label=int(labels[i]),
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train_mask=(split[i] == 1),
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val_mask=(split[i] == 2),
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test_mask=(split[i] == 3),
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)
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self._g = g
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self._num_classes = int(np.max(labels) + 1)
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if self.verbose:
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print("Loaded Reddit dataset:")
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print(f" NumNodes: {g.number_of_nodes()}")
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print(f" NumEdges: {g.number_of_edges()}")
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print(f" NumFeats: {feat.shape[1]}")
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print(f" NumClasses: {self._num_classes}")
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def __getitem__(self, idx):
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assert idx == 0, "RedditDataset only contains one graph"
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return self._g if self.transform is None else self.transform(self._g)
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def __len__(self):
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return 1
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@property
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def num_classes(self):
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return self._num_classes
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