wehub-resource-sync
484 lines
14 KiB
Python
484 lines
14 KiB
Python
"""Datasets introduced in the Geom-GCN paper."""
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import os
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import numpy as np
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from ..convert import graph
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from .dgl_dataset import DGLBuiltinDataset
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from .utils import _get_dgl_url
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class GeomGCNDataset(DGLBuiltinDataset):
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r"""Datasets introduced in
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`Geom-GCN: Geometric Graph Convolutional Networks
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<https://arxiv.org/abs/2002.05287>`__
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Parameters
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----------
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name : str
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Name of the dataset.
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raw_dir : str
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Raw file directory to store the processed data.
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force_reload : bool
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Whether to re-download the data source.
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verbose : bool
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Whether to print progress information.
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transform : callable
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access.
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"""
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def __init__(self, name, raw_dir, force_reload, verbose, transform):
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url = _get_dgl_url(f"dataset/{name}.zip")
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super(GeomGCNDataset, self).__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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"""Load and process the data."""
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try:
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import torch
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except ImportError:
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raise ModuleNotFoundError(
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"This dataset requires PyTorch to be the backend."
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)
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# Process node features and labels.
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with open(f"{self.raw_path}/out1_node_feature_label.txt", "r") as f:
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data = f.read().split("\n")[1:-1]
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features = [
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[float(v) for v in r.split("\t")[1].split(",")] for r in data
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]
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features = torch.tensor(features, dtype=torch.float)
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labels = [int(r.split("\t")[2]) for r in data]
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self._num_classes = max(labels) + 1
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labels = torch.tensor(labels, dtype=torch.long)
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# Process graph structure.
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with open(f"{self.raw_path}/out1_graph_edges.txt", "r") as f:
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data = f.read().split("\n")[1:-1]
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data = [[int(v) for v in r.split("\t")] for r in data]
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dst, src = torch.tensor(data, dtype=torch.long).t().contiguous()
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self._g = graph((src, dst), num_nodes=features.size(0))
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self._g.ndata["feat"] = features
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self._g.ndata["label"] = labels
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# Process 10 train/val/test node splits.
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train_masks, val_masks, test_masks = [], [], []
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for i in range(10):
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filepath = f"{self.raw_path}/{self.name}_split_0.6_0.2_{i}.npz"
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f = np.load(filepath)
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train_masks += [torch.from_numpy(f["train_mask"])]
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val_masks += [torch.from_numpy(f["val_mask"])]
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test_masks += [torch.from_numpy(f["test_mask"])]
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self._g.ndata["train_mask"] = torch.stack(train_masks, dim=1).bool()
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self._g.ndata["val_mask"] = torch.stack(val_masks, dim=1).bool()
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self._g.ndata["test_mask"] = torch.stack(test_masks, dim=1).bool()
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def has_cache(self):
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return os.path.exists(self.raw_path)
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def load(self):
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self.process()
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def __getitem__(self, idx):
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assert idx == 0, "This dataset has only one graph."
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if self._transform is None:
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return self._g
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else:
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return 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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class ChameleonDataset(GeomGCNDataset):
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r"""Wikipedia page-page network on chameleons from `Multi-scale Attributed
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Node Embedding <https://arxiv.org/abs/1909.13021>`__ and later modified by
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`Geom-GCN: Geometric Graph Convolutional Networks
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<https://arxiv.org/abs/2002.05287>`__
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Nodes represent articles from the English Wikipedia, edges reflect mutual
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links between them. Node features indicate the presence of particular nouns
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in the articles. The nodes were classified into 5 classes in terms of their
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average monthly traffic.
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Statistics:
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- Nodes: 2277
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- Edges: 36101
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- Number of Classes: 5
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- 10 train/val/test splits
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- Train: 1092
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- Val: 729
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- Test: 456
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Parameters
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----------
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raw_dir : str, optional
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Raw file directory to store the processed data. Default: ~/.dgl/
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force_reload : bool, optional
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Whether to re-download the data source. Default: False
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verbose : bool, optional
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Whether to print progress information. Default: True
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access. Default: None
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Attributes
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----------
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num_classes : int
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Number of node classes
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Notes
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-----
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The graph does not come with edges for both directions.
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Examples
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--------
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>>> from dgl.data import ChameleonDataset
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>>> dataset = ChameleonDataset()
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>>> g = dataset[0]
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>>> num_classes = dataset.num_classes
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>>> # get node features
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>>> feat = g.ndata["feat"]
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>>> # get data split
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>>> train_mask = g.ndata["train_mask"]
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>>> val_mask = g.ndata["val_mask"]
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>>> test_mask = g.ndata["test_mask"]
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>>> # get labels
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>>> label = g.ndata['label']
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"""
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def __init__(
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self, raw_dir=None, force_reload=False, verbose=True, transform=None
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):
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super(ChameleonDataset, self).__init__(
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name="chameleon",
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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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class SquirrelDataset(GeomGCNDataset):
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r"""Wikipedia page-page network on squirrels from `Multi-scale Attributed
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Node Embedding <https://arxiv.org/abs/1909.13021>`__ and later modified by
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`Geom-GCN: Geometric Graph Convolutional Networks
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<https://arxiv.org/abs/2002.05287>`__
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Nodes represent articles from the English Wikipedia, edges reflect mutual
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links between them. Node features indicate the presence of particular nouns
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in the articles. The nodes were classified into 5 classes in terms of their
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average monthly traffic.
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Statistics:
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- Nodes: 5201
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- Edges: 217073
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- Number of Classes: 5
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- 10 train/val/test splits
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- Train: 2496
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- Val: 1664
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- Test: 1041
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Parameters
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----------
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raw_dir : str, optional
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Raw file directory to store the processed data. Default: ~/.dgl/
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force_reload : bool, optional
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Whether to re-download the data source. Default: False
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verbose : bool, optional
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Whether to print progress information. Default: True
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access. Default: None
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Attributes
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----------
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num_classes : int
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Number of node classes
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Notes
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-----
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The graph does not come with edges for both directions.
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Examples
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--------
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>>> from dgl.data import SquirrelDataset
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>>> dataset = SquirrelDataset()
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>>> g = dataset[0]
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>>> num_classes = dataset.num_classes
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>>> # get node features
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>>> feat = g.ndata["feat"]
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>>> # get data split
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>>> train_mask = g.ndata["train_mask"]
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>>> val_mask = g.ndata["val_mask"]
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>>> test_mask = g.ndata["test_mask"]
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>>> # get labels
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>>> label = g.ndata['label']
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"""
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def __init__(
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self, raw_dir=None, force_reload=False, verbose=True, transform=None
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):
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super(SquirrelDataset, self).__init__(
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name="squirrel",
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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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class CornellDataset(GeomGCNDataset):
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r"""Cornell subset of
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`WebKB <http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/>`__,
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later modified by `Geom-GCN: Geometric Graph Convolutional Networks
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<https://arxiv.org/abs/2002.05287>`__
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Nodes represent web pages. Edges represent hyperlinks between them. Node
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features are the bag-of-words representation of web pages. The web pages
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are manually classified into the five categories, student, project, course,
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staff, and faculty.
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Statistics:
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- Nodes: 183
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- Edges: 298
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- Number of Classes: 5
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- 10 train/val/test splits
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- Train: 87
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- Val: 59
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- Test: 37
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Parameters
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----------
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raw_dir : str, optional
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Raw file directory to store the processed data. Default: ~/.dgl/
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force_reload : bool, optional
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Whether to re-download the data source. Default: False
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verbose : bool, optional
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Whether to print progress information. Default: True
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access. Default: None
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Attributes
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----------
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num_classes : int
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Number of node classes
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Notes
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-----
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The graph does not come with edges for both directions.
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Examples
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--------
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>>> from dgl.data import CornellDataset
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>>> dataset = CornellDataset()
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>>> g = dataset[0]
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>>> num_classes = dataset.num_classes
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>>> # get node features
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>>> feat = g.ndata["feat"]
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>>> # get data split
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>>> train_mask = g.ndata["train_mask"]
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>>> val_mask = g.ndata["val_mask"]
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>>> test_mask = g.ndata["test_mask"]
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>>> # get labels
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>>> label = g.ndata['label']
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"""
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def __init__(
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self, raw_dir=None, force_reload=False, verbose=True, transform=None
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):
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super(CornellDataset, self).__init__(
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name="cornell",
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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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class TexasDataset(GeomGCNDataset):
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r"""Texas subset of
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`WebKB <http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/>`__,
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later modified by `Geom-GCN: Geometric Graph Convolutional Networks
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<https://arxiv.org/abs/2002.05287>`__
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Nodes represent web pages. Edges represent hyperlinks between them. Node
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features are the bag-of-words representation of web pages. The web pages
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are manually classified into the five categories, student, project, course,
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staff, and faculty.
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Statistics:
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- Nodes: 183
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- Edges: 325
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- Number of Classes: 5
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- 10 train/val/test splits
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- Train: 87
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- Val: 59
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- Test: 37
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Parameters
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----------
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raw_dir : str, optional
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Raw file directory to store the processed data. Default: ~/.dgl/
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force_reload : bool, optional
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Whether to re-download the data source. Default: False
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verbose : bool, optional
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Whether to print progress information. Default: True
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access. Default: None
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Attributes
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----------
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num_classes : int
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Number of node classes
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Notes
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-----
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The graph does not come with edges for both directions.
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Examples
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--------
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>>> from dgl.data import TexasDataset
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>>> dataset = TexasDataset()
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>>> g = dataset[0]
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>>> num_classes = dataset.num_classes
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>>> # get node features
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>>> feat = g.ndata["feat"]
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>>> # get data split
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>>> train_mask = g.ndata["train_mask"]
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>>> val_mask = g.ndata["val_mask"]
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>>> test_mask = g.ndata["test_mask"]
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>>> # get labels
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>>> label = g.ndata['label']
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"""
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def __init__(
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self, raw_dir=None, force_reload=False, verbose=True, transform=None
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):
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super(TexasDataset, self).__init__(
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name="texas",
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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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class WisconsinDataset(GeomGCNDataset):
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r"""Wisconsin subset of
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`WebKB <http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/>`__,
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later modified by `Geom-GCN: Geometric Graph Convolutional Networks
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<https://arxiv.org/abs/2002.05287>`__
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Nodes represent web pages. Edges represent hyperlinks between them. Node
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features are the bag-of-words representation of web pages. The web pages
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are manually classified into the five categories, student, project, course,
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staff, and faculty.
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Statistics:
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- Nodes: 251
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- Edges: 515
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- Number of Classes: 5
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- 10 train/val/test splits
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- Train: 120
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- Val: 80
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- Test: 51
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Parameters
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----------
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raw_dir : str, optional
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Raw file directory to store the processed data. Default: ~/.dgl/
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force_reload : bool, optional
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Whether to re-download the data source. Default: False
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verbose : bool, optional
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Whether to print progress information. Default: True
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transform : callable, optional
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A transform that takes in a :class:`~dgl.DGLGraph` object and returns
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a transformed version. The :class:`~dgl.DGLGraph` object will be
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transformed before every access. Default: None
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|
Attributes
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----------
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num_classes : int
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Number of node classes
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Notes
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-----
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The graph does not come with edges for both directions.
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Examples
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--------
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>>> from dgl.data import WisconsinDataset
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>>> dataset = WisconsinDataset()
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>>> g = dataset[0]
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>>> num_classes = dataset.num_classes
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>>> # get node features
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>>> feat = g.ndata["feat"]
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>>> # get data split
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>>> train_mask = g.ndata["train_mask"]
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>>> val_mask = g.ndata["val_mask"]
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>>> test_mask = g.ndata["test_mask"]
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>>> # get labels
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>>> label = g.ndata['label']
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"""
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def __init__(
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self, raw_dir=None, force_reload=False, verbose=True, transform=None
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):
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super(WisconsinDataset, self).__init__(
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name="wisconsin",
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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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