wehub-resource-sync
94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
import easygraph as eg
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from .graph_dataset_base import EasyGraphDataset
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from .utils import _set_labels
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from .utils import tensor
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""" KarateClubDataset for inductive learning. """
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class KarateClubDataset(EasyGraphDataset):
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"""Karate Club dataset for Node Classification
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Zachary's karate club is a social network of a university
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karate club, described in the paper "An Information Flow
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Model for Conflict and Fission in Small Groups" by Wayne W. Zachary.
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The network became a popular example of community structure in
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networks after its use by Michelle Girvan and Mark Newman in 2002.
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Official website: `<http://konect.cc/networks/ucidata-zachary/>`_
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Karate Club dataset statistics:
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- Nodes: 34
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- Edges: 156
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- Number of Classes: 2
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Parameters
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----------
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transform : callable, optional
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A transform that takes in a :class:`~eg.Graph` object and returns
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a transformed version. The :class:`~eg.Graph` 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 node classes
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Examples
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--------
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>>> dataset = KarateClubDataset()
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>>> num_classes = dataset.num_classes
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>>> g = dataset[0]
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>>> labels = g.ndata['label']
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"""
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def __init__(self, transform=None):
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super(KarateClubDataset, self).__init__(name="karate_club", transform=transform)
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def process(self):
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import numpy as np
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kc_graph = eg.get_graph_karateclub()
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label = np.asarray(
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[kc_graph.nodes[i]["club"] != "Mr. Hi" for i in kc_graph.nodes]
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).astype(np.int64)
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label = tensor(label)
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kc_graph = _set_labels(kc_graph, label)
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kc_graph.ndata["label"] = label
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self._graph = kc_graph
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self._data = [kc_graph]
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@property
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def num_classes(self):
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"""Number of classes."""
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return 2
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def __getitem__(self, idx):
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r"""Get graph object
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Parameters
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----------
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idx : int
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Item index, KarateClubDataset has only one graph object
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Returns
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-------
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:class:`eg.Graph`
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graph structure and labels.
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- ``ndata['label']``: ground truth labels
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"""
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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._graph
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else:
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return self._transform(self._graph)
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def __len__(self):
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r"""The number of graphs in the dataset."""
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return 1
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