def get_frequency(G_q, train_g_path):
"""
# Projecting a Query Graph to BoFG histogram
"""
train_lst = fn.get_file_list(train_g_path, ".g")
freq_lst = []
for g_tr in train_lst:
G_tr = nx.read_gpickle(train_g_path + g_tr)
# Count the frequency of each G_tr in G_q
# Check that Node labels of G_tr belongs to that of G_q
with open(train_g_path + g_tr[:-2] + ".nodelabel", "r") as nl:
G_tr_nlabels = json.load(nl)
G_q_nlabels = [int(n_tup[1]["label"]) for n_tup in G_q.nodes(data=True)]
if fn.is_subset(G_tr_nlabels, G_q_nlabels):
# print G_tr_nlabels, G_tr.edges(data=True)
nm = iso.numerical_node_match("label", 1)
em = iso.numerical_edge_match("weight", 0)
GM = iso.GraphMatcher(G_q, G_tr, node_match=nm, edge_match=em)
frequency = len(list(GM.subgraph_isomorphisms_iter()))
if frequency > 0:
BoFG_ID = int(g_tr[-5:-2])
freq_lst.append((BoFG_ID, frequency))
return freq_lst
def get_frequency(G_q, train_g_path):
"""
# Projecting a Query Graph to BoFG histogram
"""
train_lst = fn.get_file_list(train_g_path, '.g')
freq_lst = []
for g_tr in train_lst:
G_tr = nx.read_gpickle(train_g_path + g_tr)
# Count the frequency of each G_tr in G_q
# Check that Node labels of G_tr belongs to that of G_q
with open(train_g_path + g_tr[:-2] + '.nodelabel', 'r') as nl:
G_tr_nlabels = json.load(nl)
G_q_nlabels = [
int(n_tup[1]['label']) for n_tup in G_q.nodes(data=True)
]
if fn.is_subset(G_tr_nlabels, G_q_nlabels):
# print G_tr_nlabels, G_tr.edges(data=True)
nm = iso.numerical_node_match('label', 1)
em = iso.numerical_edge_match('weight', 0)
GM = iso.GraphMatcher(G_q, G_tr, node_match=nm, edge_match=em)
frequency = len(list(GM.subgraph_isomorphisms_iter()))
if frequency > 0:
BoFG_ID = int(g_tr[-5:-2])
freq_lst.append((BoFG_ID, frequency))
return freq_lst
G_q.add_edge( v1, v2, weight=order_dist )
"""
# Projecting a Query Graph to BoFG histogram
"""
train_path = 'train/model_1/graph/'
train_lst = fn.get_file_list( train_path, '.g' )
for g_tr in train_lst:
G_tr = nx.read_gpickle( train_path + g_tr )
# Count the frequency of each G_tr in G_q
# Check that Node labels of G_tr belongs to that of G_q
with open( train_path + g_tr[:-2] + '.nodelabel', 'r') as nl:
G_tr_nlabels = json.load( nl )
G_q_nlabels = [ int(n_tup[1]['label']) for n_tup in G_q.nodes(data=True)]
if fn.is_subset( G_tr_nlabels, G_q_nlabels ):
# print G_tr_nlabels, G_tr.edges(data=True)
nm = iso.numerical_node_match('label', 1)
em = iso.numerical_edge_match('weight', 0)
GM = iso.GraphMatcher( G_q, G_tr, node_match=nm, edge_match=em )
#==============================================================================
# for subgraph in GM.subgraph_isomorphisms_iter():
# print subgraph
#==============================================================================
if len(list(GM.subgraph_isomorphisms_iter())) > 0:
BoFG_ID = int( g_tr[-5:-2] )
if v1 == v2: # Remove Nodes self to self
del G_q_od[v1][v2]
else: # Modify Weights of Other Nodes & Assign them to Graph G
# Edge Label
order_dist = G_q_od[v1][v2]
order_dist -= 1
G_q.add_edge(v1, v2, weight=order_dist)
"""
# Projecting a Query Graph to BoFG histogram
"""
train_path = 'train/model_1/graph/'
train_lst = fn.get_file_list(train_path, '.g')
for g_tr in train_lst:
G_tr = nx.read_gpickle(train_path + g_tr)
# Count the frequency of each G_tr in G_q
# Check that Node labels of G_tr belongs to that of G_q
with open(train_path + g_tr[:-2] + '.nodelabel', 'r') as nl:
G_tr_nlabels = json.load(nl)
G_q_nlabels = [int(n_tup[1]['label']) for n_tup in G_q.nodes(data=True)]
if fn.is_subset(G_tr_nlabels, G_q_nlabels):
# print G_tr_nlabels, G_tr.edges(data=True)
nm = iso.numerical_node_match('label', 1)
em = iso.numerical_edge_match('weight', 0)
GM = iso.GraphMatcher(G_q, G_tr, node_match=nm, edge_match=em)
#==============================================================================
# for subgraph in GM.subgraph_isomorphisms_iter():
# print subgraph
#==============================================================================
if len(list(GM.subgraph_isomorphisms_iter())) > 0:
BoFG_ID = int(g_tr[-5:-2])
