print "Done!"
# Create a list of random graph pairs
print "Creating list of random graph pairs..."
for _ in xrange(100):
g = nx.watts_strogatz_graph(g_base_nodes, 5, 0.1)
graphPair = graph_pair_class.GraphPair(g_base, g)
for f in feature_list:
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
# Create a list of PING graphs
print "Creating PING graphs..."
for _ in xrange(100):
g1, g2 = ping.create(2, 10)
graphPair = graph_pair_class.GraphPair(g1, g2)
for f in feature_list:
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
# Convert graphPairs to a Pandas dataframe
data = [g.features + [g.is_isomorphic] for g in graphPairs]
columns = ['feature_' + str(x)
for x in range(len(feature_list))] + ['is_isomorphic']
data = pd.DataFrame(data, columns=columns)
# Train model on random samples
model = LogisticRegression()
print "Done!"
# Create a list of random graph pairs
print "Creating list of random graph pairs..."
for _ in xrange(100):
g = nx.watts_strogatz_graph(g_base_nodes, 5, 0.1)
graphPair = graph_pair_class.GraphPair(g_base, g)
for f in feature_list:
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
# Create a list of PING graphs
print "Creating PING graphs..."
for _ in xrange(100):
g1, g2 = ping.create(2,10)
graphPair = graph_pair_class.GraphPair(g1,g2)
for f in feature_list:
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
'''Neural Network model'''
X = np.array([g.features for g in graphPairs])
y = np.array([g.is_isomorphic for g in graphPairs])
shuffle = range(len(y))
random.shuffle(shuffle)
X = X[shuffle]
y = y[shuffle]
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
# Create a list of random graph pairs
print "Creating list of random graph pairs..."
for _ in xrange(num_of_graphs):
g = nx.watts_strogatz_graph(g_base_nodes, 5, 0.1)
graphPair = graph_pair_class.GraphPair(g_base, g)
for f in feature_list:
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
# Create a list of PING graphs
print "Creating PING graphs..."
for _ in xrange(num_of_graphs):
g1, g2 = ping.create(int(nodes/5),nodes)
graphPair = graph_pair_class.GraphPair(g1,g2)
for f in feature_list:
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
print "Pickling graphs"
filename = 'graphPairs_' + str(nodes) + '_nodes_' + str(len(graphPairs)) + '_pairs.bro'
f = open(filename, 'wb')
pickle.dump(graphPairs, f)
f.close()
print "Done!"
graphPairs += [graphPair]
print "Done!"
# Create a list of random graph pairs
print "Creating list of random graph pairs..."
for _ in xrange(num_of_graphs):
g = nx.watts_strogatz_graph(g_base_nodes, 5, 0.1)
graphPair = graph_pair_class.GraphPair(g_base, g)
for f in feature_list:
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
# Create a list of PING graphs
print "Creating PING graphs..."
for _ in xrange(num_of_graphs):
g1, g2 = ping.create(int(nodes / 5), nodes)
graphPair = graph_pair_class.GraphPair(g1, g2)
for f in feature_list:
graphPair.add_feature(f)
graphPairs += [graphPair]
print "Done!"
print "Pickling graphs"
filename = 'graphPairs_' + str(nodes) + '_nodes_' + str(
len(graphPairs)) + '_pairs.bro'
f = open(filename, 'wb')
pickle.dump(graphPairs, f)
f.close()
print "Done!"
