wehub-resource-sync
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
"""
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****************NOTE*****************
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CREDITS : Thomas Kipf
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since datasets are the same as those in kipf's implementation,
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Their preprocessing source was used as-is.
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*************************************
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"""
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import pickle as pkl
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import sys
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import networkx as nx
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import numpy as np
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import scipy.sparse as sp
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def parse_index_file(filename):
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index = []
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for line in open(filename):
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index.append(int(line.strip()))
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return index
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def load_data(dataset):
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# load the data: x, tx, allx, graph
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names = ["x", "tx", "allx", "graph"]
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objects = []
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for i in range(len(names)):
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with open("data/ind.{}.{}".format(dataset, names[i]), "rb") as f:
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if sys.version_info > (3, 0):
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objects.append(pkl.load(f, encoding="latin1"))
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else:
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objects.append(pkl.load(f))
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x, tx, allx, graph = tuple(objects)
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test_idx_reorder = parse_index_file(
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"data/ind.{}.test.index".format(dataset)
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)
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test_idx_range = np.sort(test_idx_reorder)
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if dataset == "citeseer":
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# Fix citeseer dataset (there are some isolated nodes in the graph)
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# Find isolated nodes, add them as zero-vecs into the right position
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test_idx_range_full = range(
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min(test_idx_reorder), max(test_idx_reorder) + 1
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)
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tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))
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tx_extended[test_idx_range - min(test_idx_range), :] = tx
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tx = tx_extended
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features = sp.vstack((allx, tx)).tolil()
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features[test_idx_reorder, :] = features[test_idx_range, :]
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adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
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return adj, features
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