wehub-resource-sync
131 lines
3.9 KiB
Python
131 lines
3.9 KiB
Python
""" PATTERNDataset for inductive learning. """
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import os
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from .dgl_dataset import DGLBuiltinDataset
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from .utils import _get_dgl_url, load_graphs
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class PATTERNDataset(DGLBuiltinDataset):
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r"""PATTERN dataset for graph pattern recognition task.
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Each graph G contains 5 communities with sizes randomly selected between [5, 35].
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The SBM of each community is p = 0.5, q = 0.35, and the node features on G are
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generated with a uniform random distribution with a vocabulary of size 3, i.e. {0, 1, 2}.
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Then randomly generate 100 patterns P composed of 20 nodes with intra-probability :math:`p_P` = 0.5
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and extra-probability :math:`q_P` = 0.5 (i.e. 50% of nodes in P are connected to G). The node features
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for P are also generated as a random signal with values {0, 1, 2}. The graphs are of sizes
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44-188 nodes. The output node labels have value 1 if the node belongs to P and value 0 if it is in G.
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Reference `<https://arxiv.org/pdf/2003.00982.pdf>`_
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Statistics:
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- Train examples: 10,000
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- Valid examples: 2,000
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- Test examples: 2,000
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- Number of classes for each node: 2
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Parameters
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----------
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mode : str
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Must be one of ('train', 'valid', 'test').
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Default: 'train'
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raw_dir : str
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Raw file directory to download/contains the input data directory.
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Default: ~/.dgl/
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force_reload : bool
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Whether to reload the dataset.
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Default: False
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verbose : bool
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Whether to print out progress information.
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Default: False
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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.
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Attributes
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----------
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num_classes : int
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Number of classes for each node.
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Examples
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--------
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>>> from dgl.data import PATTERNDataset
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>>> data = PATTERNDataset(mode='train')
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>>> data.num_classes
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2
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>>> len(trainset)
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10000
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>>> data[0]
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Graph(num_nodes=108, num_edges=4884, ndata_schemes={'feat': Scheme(shape=(), dtype=torch.int64), 'label': Scheme(shape=(), dtype=torch.int16)}
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edata_schemes={'feat': Scheme(shape=(1,), dtype=torch.float32)})
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"""
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def __init__(
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self,
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mode="train",
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raw_dir=None,
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force_reload=False,
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verbose=False,
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transform=None,
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):
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assert mode in ["train", "valid", "test"]
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self.mode = mode
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_url = _get_dgl_url("dataset/SBM_PATTERN.zip")
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super(PATTERNDataset, self).__init__(
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name="pattern",
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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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self.load()
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@property
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def graph_path(self):
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return os.path.join(
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self.save_path, "SBM_PATTERN_{}.bin".format(self.mode)
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)
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def has_cache(self):
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return os.path.exists(self.graph_path)
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def load(self):
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self._graphs, _ = load_graphs(self.graph_path)
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@property
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def num_classes(self):
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r"""Number of classes for each node."""
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return 2
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def __len__(self):
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r"""The number of examples in the dataset."""
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return len(self._graphs)
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def __getitem__(self, idx):
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r"""Get the idx^th sample.
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Parameters
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---------
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idx : int
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The sample index.
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Returns
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-------
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:class:`dgl.DGLGraph`
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graph structure, node features, node labels and edge features.
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- ``ndata['feat']``: node features
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- ``ndata['label']``: node labels
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- ``edata['feat']``: edge features
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"""
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if self._transform is None:
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return self._graphs[idx]
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else:
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return self._transform(self._graphs[idx])
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