def test_ascos(self):
G = networkx.Graph()
G.add_edge(1, 2)
G.add_edge(1, 4)
G.add_edge(1, 5)
G.add_edge(1, 6)
G.add_edge(2, 3)
node_ids, sim = ascos(G)
nt.assert_equal(len(node_ids), 6)
nt.assert_equal(sim.shape, (6, 6))
sim_ans = numpy.matrix(('1 0.5732 0.3474 0.5296 0.5296 0.5296;'
'0.7563 1 0.6063 0.4005 0.4005 0.4005;'
'0.6807 0.9000 1 0.3604 0.3604 0.3604;'
'0.9000 0.5159 0.3126 1 0.4766 0.4766;'
'0.9000 0.5159 0.3126 0.4766 1 0.4766;'
'0.9000 0.5159 0.3126 0.4766 0.4766 1'))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
nt.assert_almost_equal(sim[i, j], sim_ans[i, j], 4)
def test_ascos(self):
G = networkx.Graph()
G.add_edge(1,2)
G.add_edge(1,4)
G.add_edge(1,5)
G.add_edge(1,6)
G.add_edge(2,3)
node_ids, sim = ascos(G)
nt.assert_equal(len(node_ids), 6)
nt.assert_equal(sim.shape, (6, 6))
sim_ans = numpy.matrix((
'1 0.5732 0.3474 0.5296 0.5296 0.5296;'
'0.7563 1 0.6063 0.4005 0.4005 0.4005;'
'0.6807 0.9000 1 0.3604 0.3604 0.3604;'
'0.9000 0.5159 0.3126 1 0.4766 0.4766;'
'0.9000 0.5159 0.3126 0.4766 1 0.4766;'
'0.9000 0.5159 0.3126 0.4766 0.4766 1'))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
nt.assert_almost_equal(sim[i,j], sim_ans[i,j], 4)
def test_weighted_ascos(self):
G = networkx.Graph()
G.add_edge('a', 'b', weight=1)
node_ids, sim = ascos(G, is_weighted=True)
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
if i == j:
nt.assert_equal(sim[i, j], 1)
else:
nt.assert_almost_equal(sim[i, j], .9 * (1 - math.exp(-1)),
4)
G['a']['b']['weight'] = 100
node_ids, sim = ascos(G, is_weighted=True)
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
if i == j:
nt.assert_equal(sim[i, j], 1)
else:
nt.assert_almost_equal(sim[i, j],
.9 * (1 - math.exp(-100)), 4)
G = networkx.Graph()
G.add_edge('a', 'b', weight=1)
G.add_edge('b', 'c', weight=1)
node_ids, sim = ascos(G, is_weighted=True)
sim_ans = numpy.matrix(('1 0.1931 .5689;'
'0.1931 1 0.5689;'
'0.3394 0.3394 1'))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
nt.assert_almost_equal(sim[i, j], sim_ans[i, j], 4)
G = networkx.Graph()
G.add_edge('a', 'b', weight=1)
G.add_edge('b', 'c', weight=10)
node_ids, sim = ascos(G, is_weighted=True)
sim_ans = numpy.matrix(('1 0.4796 0.5689;'
'0.1762 1 0.9000;'
'0.1959 0.8429 1'))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
nt.assert_almost_equal(sim[i, j], sim_ans[i, j], 4)
G = networkx.Graph()
G.add_edge(1, 2)
G.add_edge(1, 4, weight=2)
G.add_edge(1, 5)
G.add_edge(1, 6)
G.add_edge(2, 3)
node_ids, sim = ascos(G, is_weighted=True)
nt.assert_equal(len(node_ids), 6)
nt.assert_equal(sim.shape, (6, 6))
sim_ans = numpy.matrix(('1 0.1810 0.0543 0.3742 0.1738 0.1738;'
'0.3394 1 0.2999 0.1270 0.0590 0.0590;'
'0.1931 0.5689 1 0.0722 0.0335 0.0335;'
'0.7782 0.1409 0.0422 1 0.1353 0.1353;'
'0.5689 0.1030 0.0308 0.2129 1 0.0989;'
'0.5689 0.1030 0.0308 0.2129 0.0989 1'))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
nt.assert_almost_equal(sim[i, j], sim_ans[i, j], 4)
# Calculate similarity for different graphs
from similarity import cosine, ascos, jaccard, \
katz, lhn, rss2, dice, inverse_log_weighted, rsa
import pickle
import pandas
### VERTEX SIMILARITY
graphs = pickle.load(open('data/graphs_networkx.pkl', 'rb'))
results = dict()
for graph_type, G in graphs.iteritems():
print "Calculating for %s" % (graph_type)
results[graph_type] = dict()
print "ASCOS ---------------------------"
results[graph_type]["ascos"] = ascos(G)
print "COSINE ---------------------------"
results[graph_type]["cosine"] = cosine(G)
print "JACCARD --------------------------"
results[graph_type]["jaccard"] = jaccard(G)
print "KATZ -----------------------------"
results[graph_type]["katz"] = katz(G)
print "LHN ------------------------------"
results[graph_type]["lhn"] = lhn(G)
print "RSS2 -----------------------------"
results[graph_type]["rss2"] = rss2(G)
print "DICE -----------------------------"
results[graph_type]["dice"] = dice(G)
print "INVERSE LOG WEIGHTED --------------"
results[graph_type]["inverse_log_weighted"] = inverse_log_weighted(G)
def test_weighted_ascos(self):
G = networkx.Graph()
G.add_edge('a', 'b', weight=1)
node_ids, sim = ascos(G, is_weighted=True)
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
if i == j:
nt.assert_equal(sim[i, j], 1)
else:
nt.assert_almost_equal(sim[i,j], .9 * (1 - math.exp(-1)), 4)
G['a']['b']['weight'] = 100
node_ids, sim = ascos(G, is_weighted=True)
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
if i == j:
nt.assert_equal(sim[i, j], 1)
else:
nt.assert_almost_equal(sim[i,j], .9 * (1 - math.exp(-100)), 4)
G = networkx.Graph()
G.add_edge('a', 'b', weight=1)
G.add_edge('b', 'c', weight=1)
node_ids, sim = ascos(G, is_weighted=True)
sim_ans = numpy.matrix((
'1 0.1931 .5689;'
'0.1931 1 0.5689;'
'0.3394 0.3394 1'))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
nt.assert_almost_equal(sim[i, j], sim_ans[i, j], 4)
G = networkx.Graph()
G.add_edge('a', 'b', weight=1)
G.add_edge('b', 'c', weight=10)
node_ids, sim = ascos(G, is_weighted=True)
sim_ans = numpy.matrix((
'1 0.4796 0.5689;'
'0.1762 1 0.9000;'
'0.1959 0.8429 1'))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
nt.assert_almost_equal(sim[i, j], sim_ans[i, j], 4)
G = networkx.Graph()
G.add_edge(1,2)
G.add_edge(1,4, weight=2)
G.add_edge(1,5)
G.add_edge(1,6)
G.add_edge(2,3)
node_ids, sim = ascos(G, is_weighted=True)
nt.assert_equal(len(node_ids), 6)
nt.assert_equal(sim.shape, (6, 6))
sim_ans = numpy.matrix((
'1 0.1810 0.0543 0.3742 0.1738 0.1738;'
'0.3394 1 0.2999 0.1270 0.0590 0.0590;'
'0.1931 0.5689 1 0.0722 0.0335 0.0335;'
'0.7782 0.1409 0.0422 1 0.1353 0.1353;'
'0.5689 0.1030 0.0308 0.2129 1 0.0989;'
'0.5689 0.1030 0.0308 0.2129 0.0989 1'))
for i in range(sim.shape[0]):
for j in range(sim.shape[1]):
nt.assert_almost_equal(sim[i,j], sim_ans[i,j], 4)
# Calculate similarity for different graphs
from similarity import cosine, ascos, jaccard, \
katz, lhn, rss2, dice, inverse_log_weighted, rsa
import pickle
import pandas
### VERTEX SIMILARITY
graphs = pickle.load(open('data/graphs_networkx.pkl','rb'))
results = dict()
for graph_type,G in graphs.iteritems():
print "Calculating for %s" %(graph_type)
results[graph_type] = dict()
print "ASCOS ---------------------------"
results[graph_type]["ascos"] = ascos(G)
print "COSINE ---------------------------"
results[graph_type]["cosine"] = cosine(G)
print "JACCARD --------------------------"
results[graph_type]["jaccard"] = jaccard(G)
print "KATZ -----------------------------"
results[graph_type]["katz"] = katz(G)
print "LHN ------------------------------"
results[graph_type]["lhn"] = lhn(G)
print "RSS2 -----------------------------"
results[graph_type]["rss2"] = rss2(G)
print "DICE -----------------------------"
results[graph_type]["dice"] = dice(G)
print "INVERSE LOG WEIGHTED --------------"
results[graph_type]["inverse_log_weighted"] = inverse_log_weighted(G)