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test_generate.py
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from graph_tool.generation import random_graph
from graph_tool.generation import random_rewire
from graph_tool.generation import generate_sbm
import numpy as np
import random
import math
import os
from distutils.dir_util import copy_tree
import graph_tool
#outputs the graph collection. Only for undirected graphs!
def write_to_files(collection_name, graphs, labels):
path = os.path.join('datasets', collection_name)
if not os.path.exists(path):
os.mkdir(path)
with open(os.path.join(path, collection_name + '_A.txt'), 'w+') as f:
node_id = 1
for i, graph in enumerate(graphs, start=1):
for edge in graph.edges():
f.write(str(graph.vertex_index[edge.source()]+node_id))
f.write(', ')
f.write(str(graph.vertex_index[edge.target()]+node_id))
f.write('\n')
f.write(str(graph.vertex_index[edge.target()]+node_id))
f.write(', ')
f.write(str(graph.vertex_index[edge.source()]+node_id))
f.write('\n')
node_id += graph.num_vertices()
with open(os.path.join(path, collection_name + '_edge_labels.txt'), 'w+') as f:
for i, graph in enumerate(graphs, start=1):
for edge in graph.edges():
f.write('1')
f.write('\n')
f.write('1')
f.write('\n')
with open(os.path.join(path, collection_name + '_graph_indicator.txt'), 'w+') as f:
for i, graph in enumerate(graphs, start=1):
for node in graph.vertices():
f.write(str(i))
f.write('\n')
with open(os.path.join(path, collection_name + '_node_labels.txt'), 'w+') as f:
for i, graph in enumerate(graphs, start=1):
for node in graph.vertices():
node_degree = len(graph.get_out_edges(node))
f.write(str(node_degree))
f.write('\n')
with open(os.path.join(path, collection_name + '_graph_labels.txt'), 'w+') as f:
for label in labels:
f.write(str(label))
f.write('\n')
def write_modified_dataset(previous_name, new_name, graphs, prop_maps):
path = os.path.join('datasets', new_name)
orig_path = os.path.join('datasets', previous_name)
#THIS ASSUMES THAT TARGET FOLDER DOES NOT EXIST
copy_tree(orig_path, path)
with open(os.path.join(path, new_name + '_A.txt'), 'w+') as f:
node_id = 0
current_line = 1
for i, graph in enumerate(graphs, start=0):
for edge in graph.edges():
#find correct edge to write
right_edge = 0
for e in graph.edges():
if prop_maps[i][e] == current_line:
right_edge = e
break
f.write(str(graph.vertex_index[right_edge.source()]+node_id))
f.write(', ')
f.write(str(graph.vertex_index[right_edge.target()]+node_id))
f.write('\n')
current_line += 1
def read_dataset(collection_name):
path = os.path.join('datasets', collection_name)
node_to_graph_ids = [-1]
graph_number = 0
with open(os.path.join(path, collection_name + '_graph_indicator.txt'), 'r') as f:
li = f.readline().rstrip('\n')
while li != '':
x = int(li)
node_to_graph_ids.append(x-1)
graph_number = max(graph_number, x)
li = f.readline().rstrip('\n')
graphs = []
for i in range(graph_number):
graphs.append(graph_tool.Graph())
prop_maps = []
for gra in graphs:
x = gra.new_edge_property('int')
prop_maps.append(x)
orig_line = 1
with open(os.path.join(path, collection_name + '_A.txt'), 'r') as f:
li = f.readline().rstrip('\n')
while li != '':
x = li.split(', ')
x1 = int(x[0])
x2 = int(x[1])
graph_no = node_to_graph_ids[x1]
e = graphs[graph_no].add_edge(x1, x2)
prop_maps[graph_no][e] = orig_line
orig_line += 1
li = f.readline().rstrip('\n')
return graphs, prop_maps
def rewire_dataset_with_preserved_degree(orig_dataset_name, new_dataset_name):
graphs, prop_maps = read_dataset(orig_dataset_name)
for g in graphs:
random_rewire(g, model='configuration', n_iter=10, edge_sweep=True)
write_modified_dataset(orig_dataset_name, new_dataset_name, graphs, prop_maps)
def rewire_dataset_partially(orig_dataset_name, new_dataset_name, p=0.1):
graphs, prop_maps = read_dataset(orig_dataset_name)
for g in graphs:
pins = g.new_edge_property('int')
for e in g.edges():
if random.random() < p:
pins[e] = 0
else:
pins[e] = 1
random_rewire(g, model='erdos', n_iter=10, edge_sweep=True, pin=pins)
write_modified_dataset(orig_dataset_name, new_dataset_name, graphs, prop_maps)
def generate_GNE(n, m):
ak = math.floor(2 * m / n)
dm = 2 * m - ak * n
g = random_graph(n, lambda i: ak + 1 if i < dm else ak, directed=False, random=False)
random_rewire(g, model='erdos', n_iter=10, edge_sweep=True)
return g
def generate_GNP(n, p):
m = 0
for i in range(round(n*(n-1)/2)):
if random.random() < p:
m += 1
return generate_GNE(n, m)
def generate_block_model(nodes, groups, in_group_p, between_group_p):
group_memberships = []
group_sizes = [0] * groups
for i in range(nodes):
group_memberships.append((i % groups))
group_sizes[i % groups] += 1
probabilities = np.ndarray([groups, groups])
for i in range(groups):
for j in range(groups):
if i == j:
probabilities[i][j] = in_group_p * group_sizes[i] * group_sizes[j]
else:
probabilities[i][j] = between_group_p * group_sizes[i] * group_sizes[j] / 2
return generate_sbm(group_memberships, probabilities)
"""
labels = []
graphs = []
for i in range(50):
#graphs.append(generate_GNP(70, 0.08))
graphs.append(generate_block_model(100, 2, 0.2, 0.01))
labels.append(1)
for i in range(50):
#graphs.append(generate_GNP(72, 0.08))
temp_graph = generate_block_model(100, 2, 0.2, 0.01)
random_rewire(temp_graph, model='erdos', n_iter=10, edge_sweep=True)
graphs.append(temp_graph)
labels.append(2)
#graphs.append(generate_GNP(100, 0.2))
#graphs.append(generate_block_model(100, 2, 0.8, 0.2))
write_to_files("EXP3", graphs, labels)
"""
rewire_dataset_with_preserved_degree('MUTAG', 'MUTAGSHUFFLED1')
rewire_dataset_partially('MUTAG', 'MUTAGSHUFFLED2', 0.1)