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utils_data.py
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utils_data.py
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# MIT License
#
# Copyright (c) 2019 Geom-GCN Authors
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import os
import re
import networkx as nx
import numpy as np
import scipy.sparse as sp
import torch as th
from dgl import DGLGraph
from sklearn.model_selection import ShuffleSplit
import utils
def load_data(dataset_name, splits_file_path=None, train_percentage=None, val_percentage=None, embedding_mode=None,
embedding_method=None,
embedding_method_graph=None, embedding_method_space=None):
if dataset_name in {'cora', 'citeseer', 'pubmed'}:
adj, features, labels, _, _, _ = utils.load_data(dataset_name)
labels = np.argmax(labels, axis=-1)
features = features.todense()
G = nx.DiGraph(adj)
else:
graph_adjacency_list_file_path = os.path.join('new_data', dataset_name, 'out1_graph_edges.txt')
graph_node_features_and_labels_file_path = os.path.join('new_data', dataset_name,
f'out1_node_feature_label.txt')
G = nx.DiGraph()
graph_node_features_dict = {}
graph_labels_dict = {}
if dataset_name == 'film':
with open(graph_node_features_and_labels_file_path) as graph_node_features_and_labels_file:
graph_node_features_and_labels_file.readline()
for line in graph_node_features_and_labels_file:
line = line.rstrip().split('\t')
assert (len(line) == 3)
assert (int(line[0]) not in graph_node_features_dict and int(line[0]) not in graph_labels_dict)
feature_blank = np.zeros(932, dtype=np.uint8)
feature_blank[np.array(line[1].split(','), dtype=np.uint16)] = 1
graph_node_features_dict[int(line[0])] = feature_blank
graph_labels_dict[int(line[0])] = int(line[2])
else:
with open(graph_node_features_and_labels_file_path) as graph_node_features_and_labels_file:
graph_node_features_and_labels_file.readline()
for line in graph_node_features_and_labels_file:
line = line.rstrip().split('\t')
assert (len(line) == 3)
assert (int(line[0]) not in graph_node_features_dict and int(line[0]) not in graph_labels_dict)
graph_node_features_dict[int(line[0])] = np.array(line[1].split(','), dtype=np.uint8)
graph_labels_dict[int(line[0])] = int(line[2])
with open(graph_adjacency_list_file_path) as graph_adjacency_list_file:
graph_adjacency_list_file.readline()
for line in graph_adjacency_list_file:
line = line.rstrip().split('\t')
assert (len(line) == 2)
if int(line[0]) not in G:
G.add_node(int(line[0]), features=graph_node_features_dict[int(line[0])],
label=graph_labels_dict[int(line[0])])
if int(line[1]) not in G:
G.add_node(int(line[1]), features=graph_node_features_dict[int(line[1])],
label=graph_labels_dict[int(line[1])])
G.add_edge(int(line[0]), int(line[1]))
adj = nx.adjacency_matrix(G, sorted(G.nodes()))
features = np.array(
[features for _, features in sorted(G.nodes(data='features'), key=lambda x: x[0])])
labels = np.array(
[label for _, label in sorted(G.nodes(data='label'), key=lambda x: x[0])])
features = utils.preprocess_features(features)
if not embedding_mode:
g = DGLGraph(adj + sp.eye(adj.shape[0]))
else:
if embedding_mode == 'ExperimentTwoAll':
embedding_file_path = os.path.join('embedding_method_combinations_all',
f'outf_nodes_relation_{dataset_name}all_embedding_methods.txt')
elif embedding_mode == 'ExperimentTwoPairs':
embedding_file_path = os.path.join('embedding_method_combinations_in_pairs',
f'outf_nodes_relation_{dataset_name}_graph_{embedding_method_graph}_space_{embedding_method_space}.txt')
else:
embedding_file_path = os.path.join('structural_neighborhood',
f'outf_nodes_space_relation_{dataset_name}_{embedding_method}.txt')
space_and_relation_type_to_idx_dict = {}
with open(embedding_file_path) as embedding_file:
for line in embedding_file:
if line.rstrip() == 'node1,node2 space relation_type':
continue
line = re.split(r'[\t,]', line.rstrip())
assert (len(line) == 4)
assert (int(line[0]) in G and int(line[1]) in G)
if (line[2], int(line[3])) not in space_and_relation_type_to_idx_dict:
space_and_relation_type_to_idx_dict[(line[2], int(line[3]))] = len(
space_and_relation_type_to_idx_dict)
if G.has_edge(int(line[0]), int(line[1])):
G.remove_edge(int(line[0]), int(line[1]))
G.add_edge(int(line[0]), int(line[1]), subgraph_idx=space_and_relation_type_to_idx_dict[
(line[2], int(line[3]))])
space_and_relation_type_to_idx_dict['self_loop'] = len(space_and_relation_type_to_idx_dict)
for node in sorted(G.nodes()):
if G.has_edge(node, node):
G.remove_edge(node, node)
G.add_edge(node, node, subgraph_idx=space_and_relation_type_to_idx_dict['self_loop'])
adj = nx.adjacency_matrix(G, sorted(G.nodes()))
g = DGLGraph(adj)
for u, v, feature in G.edges(data='subgraph_idx'):
g.edges[g.edge_id(u, v)].data['subgraph_idx'] = th.tensor([feature])
if splits_file_path:
with np.load(splits_file_path) as splits_file:
train_mask = splits_file['train_mask']
val_mask = splits_file['val_mask']
test_mask = splits_file['test_mask']
else:
assert (train_percentage is not None and val_percentage is not None)
assert (train_percentage < 1.0 and val_percentage < 1.0 and train_percentage + val_percentage < 1.0)
if dataset_name in {'cora', 'citeseer'}:
disconnected_node_file_path = os.path.join('unconnected_nodes', f'{dataset_name}_unconnected_nodes.txt')
with open(disconnected_node_file_path) as disconnected_node_file:
disconnected_node_file.readline()
disconnected_nodes = []
for line in disconnected_node_file:
line = line.rstrip()
disconnected_nodes.append(int(line))
disconnected_nodes = np.array(disconnected_nodes)
connected_nodes = np.setdiff1d(np.arange(features.shape[0]), disconnected_nodes)
connected_labels = labels[connected_nodes]
train_and_val_index, test_index = next(
ShuffleSplit(n_splits=1, train_size=train_percentage + val_percentage).split(
np.empty_like(connected_labels), connected_labels))
train_index, val_index = next(ShuffleSplit(n_splits=1, train_size=train_percentage).split(
np.empty_like(connected_labels[train_and_val_index]), connected_labels[train_and_val_index]))
train_index = train_and_val_index[train_index]
val_index = train_and_val_index[val_index]
train_mask = np.zeros_like(labels)
train_mask[connected_nodes[train_index]] = 1
val_mask = np.zeros_like(labels)
val_mask[connected_nodes[val_index]] = 1
test_mask = np.zeros_like(labels)
test_mask[connected_nodes[test_index]] = 1
else:
train_and_val_index, test_index = next(
ShuffleSplit(n_splits=1, train_size=train_percentage + val_percentage).split(
np.empty_like(labels), labels))
train_index, val_index = next(ShuffleSplit(n_splits=1, train_size=train_percentage).split(
np.empty_like(labels[train_and_val_index]), labels[train_and_val_index]))
train_index = train_and_val_index[train_index]
val_index = train_and_val_index[val_index]
train_mask = np.zeros_like(labels)
train_mask[train_index] = 1
val_mask = np.zeros_like(labels)
val_mask[val_index] = 1
test_mask = np.zeros_like(labels)
test_mask[test_index] = 1
num_features = features.shape[1]
num_labels = len(np.unique(labels))
assert (np.array_equal(np.unique(labels), np.arange(len(np.unique(labels)))))
features = th.FloatTensor(features)
labels = th.LongTensor(labels)
train_mask = th.BoolTensor(train_mask)
val_mask = th.BoolTensor(val_mask)
test_mask = th.BoolTensor(test_mask)
# Adapted from https://docs.dgl.ai/tutorials/models/1_gnn/1_gcn.html
degs = g.in_degrees().float()
norm = th.pow(degs, -0.5).cuda()
norm[th.isinf(norm)] = 0
g.ndata['norm'] = norm.unsqueeze(1)
return g, features, labels, train_mask, val_mask, test_mask, num_features, num_labels