def test_edges_iter(self):
assert_equal(list(self.G.edges_iter()), list(nx.edges_iter(self.G)))
assert_equal(list(self.DG.edges_iter()), list(nx.edges_iter(self.DG)))
assert_equal(list(self.G.edges_iter(nbunch=[0, 1, 3])),
list(nx.edges_iter(self.G, nbunch=[0, 1, 3])))
assert_equal(list(self.DG.edges_iter(nbunch=[0, 1, 3])),
list(nx.edges_iter(self.DG, nbunch=[0, 1, 3])))
def edge_count_pruner_2(graph,k,nodes=0):
if nodes == 0:
nodes = graph.nodes()
start = time.time()
pruned = []
for n in nodes:
found = []
count = 0
edges = nx.edges_iter(graph, nbunch=n)
for x in edges:
if x[1] not in found:
count += 1
found.append(x[1])
edges2 = nx.edges_iter(graph,nbunch=x[1])
for y in edges2:
if y[1] not in found:
count += 1
found.append(y[1])
if count > k:
pruned.append(n)
print time.time() - start
print float(len(pruned))/(float(len(graph.nodes())))
return pruned
def _define_bond(graph, node1, node2, patch):
"""
Process a bond defined in a psf file and adds it to the graph.
Checks for + or - in bonded atom name and sets the node "residue"
attribute accordingly if it is present.
Args:
graph (networkx graph): Graph to add bond to
node1 (str): Atom name from psf file of first atom
node2 (str): Atom name from psf file of second atom
patch (bool): If this bond is defined by a patch
Returns:
(bool) if bond could be defined
Raises:
ValueError if a non +- atom name is not defined in the MASS
line dictionary
"""
# Sanity check and process first atom name
if "+" in node1:
graph.add_node(node1, type="", residue="+", patched=patch)
elif "-" in node1:
graph.add_node(node1, type="", residue="-", patched=patch)
elif node1 not in graph.nodes():
return False
# Now sanity check and process second atom name
if "+" in node2:
graph.add_node(node2, type="", residue="+", patched=patch)
elif "-" in node2:
graph.add_node(node2, type="", residue="-", patched=patch)
elif node2 not in graph.nodes():
return False
# If we are applying a patch and there are _join atoms attached
# to the atom we are applying a bond to, delete the _join atom.
# It can be added back later if it was actually needed.
if graph.node[node1]["patched"] and not graph.node[node2]["patched"]:
neighbor_joins = [e[1] for e in nx.edges_iter(graph, nbunch=[node2]) \
if graph.node[e[1]]["residue"] != "self" and \
not graph.node[e[1]]["patched"]]
graph.remove_nodes_from(neighbor_joins)
elif graph.node[node2]["patched"] and not graph.node[node1]["patched"]:
neighbor_joins = [e[1] for e in nx.edges_iter(graph, nbunch=[node1]) \
if graph.node[e[1]]["residue"] != "self" and \
not graph.node[e[1]]["patched"]]
graph.remove_nodes_from(neighbor_joins)
graph.add_edge(node1, node2, patched=patch)
return True
def encodeUntilAccessConstraint(accessConstraint, resource, visited):
if accessConstraint[0] == 'EU':
accessConstraint1 = accessConstraint[1]
accessConstraint2 = accessConstraint[2]
accessConstraint2Encoded = encodeAccessConstraint(accessConstraint2, resource)
accessConstraint1Encoded = encodeAccessConstraint(accessConstraint1, resource)
successorConstraints = []
for PEP in networkx.edges_iter(conf.resourceStructure, resource):
successor = PEP[1]
if successor in visited:
continue
succVisited = set(visited)
succVisited.add(resource)
successorAccessConstraintEncoded = encodeUntilAccessConstraint(accessConstraint, successor, succVisited)
pepTemplate = template.PEPTemplate(PEP)
successorConstraints.append(And(pepTemplate, successorAccessConstraintEncoded))
return Or(accessConstraint2Encoded, And(accessConstraint1Encoded, Or(successorConstraints)))
elif accessConstraint[0] == 'AU':
accessConstraint1 = accessConstraint[1]
accessConstraint2 = accessConstraint[2]
accessConstraint2Encoded = encodeAccessConstraint(accessConstraint2, resource)
accessConstraint1Encoded = encodeAccessConstraint(accessConstraint1, resource)
successorConstraints = []
noBackEdgesConstraints = []
existsSuccessorConstraints = []
for PEP in networkx.edges_iter(conf.resourceStructure, resource):
successor = PEP[1]
if successor in visited:
noBackEdgesConstraints.append(Not(template.PEPTemplate(PEP)))
continue
existsSuccessorConstraints.append(template.PEPTemplate(PEP))
succVisited = set(visited)
succVisited.add(resource)
successorAccessConstraintEncoded = encodeUntilAccessConstraint(accessConstraint, successor, succVisited)
pepTemplate = template.PEPTemplate(PEP)
successorConstraints.append(Implies(pepTemplate, successorAccessConstraintEncoded))
noBackEdges = And(noBackEdgesConstraints)
existsSuccessor = Or(existsSuccessorConstraints)
return Or(accessConstraint2Encoded, And(accessConstraint1Encoded, noBackEdges, existsSuccessor, And(successorConstraints)))
else:
raise NameError('Not an until access constraint:', accessConstraint)
def rewire(M,p):
R = nx.Graph(M)
rewireSet = [i for i in nx.edges_iter(R) if random.random() < p]
R.remove_edges_from(rewireSet)
for i in rewireSet:
R.add_edge(i[0],random.sample([k for k in nx.non_neighbors(R,i[0])],1)[0])
return R
def community_bridge(self, n):
N = set(self.G.neighbors(n))
communities = list(fx.read_communities())
if self.p is None:
complete = 0
for pair in nx.edges_iter(self.G):
if fx.same_community(communities, pair[0], pair[1]):
#Pair is complete
complete += 1
#Probability two linked nodes belong to same community
self.p = complete / nx.number_of_edges(self.G)
print("p =", self.p)
if self.q is None:
pure = 0
nonlinked = 0
for pair in nx.non_edges(self.G):
nonlinked += 1
if not fx.same_community(communities, pair[0], pair[1]):
#Pair is pure
pure += 1
#Probability two non-linked nodes do not belong to same comunity
self.q = pure / nonlinked
print("q =", self.q)
sum = 0
for vj in N:
#Number of common neighbors
n1 = len(N.intersection(set(self.G.neighbors(vj))))
n2 = len(N) - n1
sum += 1 / (1 + n1 * self.p + n2 * (1 - self.q))
return sum
def N_W_S(graph, beta, nodes_add_rate):
network = graph
edges_add = []
num_of_nodes_add = int(nx.number_of_nodes(network) * nodes_add_rate)
num = 1
i = 0
j = 0
num = 1
while num <= num_of_nodes_add:
node_selected = random.randint(0, nx.number_of_nodes(network) - 1)
node_1 = node_selected - 1
node_2 = node_selected + 1
node_3 = node_selected + 2
for edge in nx.edges_iter(network):
x = random.uniform(0, 1)
if x < beta:
node_random_selected = random.randint(
0,
nx.number_of_nodes(network) - 1)
#print 'node_random_selected',node_random_selected
#print network.nodes()[node_random_selected]
if node_random_selected != edge[0] and node_random_selected != edge[1] \
and (not ((edge[0], network.nodes()[node_random_selected]) in edges_add))\
and (not ((edge[0], network.nodes()[node_random_selected]) in network.edges())):
edges_add.append(
(edge[0], network.nodes()[node_random_selected]))
network.add_edges_from(edges_add)
def draw_connection_at_node(self, network, node, visited):
result=None
neighbs = [edge for edge in nx.edges_iter(network, node)]
neighbs = filter(lambda x : x not in visited, neighbs)
if len(neighbs)>0:
result = random.choice(neighbs)
return result
def ind_cascade(node_id,nc,activated,q):
edges = nx.edges_iter(nc,nbunch=node_id)
for x in edges:
#print "Edge " + str(x)
if x[1] not in activated and edge_activate(nc[x[0]][x[1]]['weight'],nc.node[x[1]]['review_count']):
activated.add(x[1])
q.put(x[1])
def rewire(M, p):
R = nx.Graph(M)
rewireSet = [i for i in nx.edges_iter(R) if random.random() < p]
R.remove_edges_from(rewireSet)
for i in rewireSet:
R.add_edge(i[0],
random.sample([k for k in nx.non_neighbors(R, i[0])], 1)[0])
return R
def scramble(M,p):
R = nx.Graph(M)
removeSet = [i for i in nx.edges_iter(R) if random.random() < p]
R.remove_edges_from(removeSet)
nodes = R.nodes()
addFromNodes = [random.sample(nodes,1)[0] for i in removeSet]
for node in addFromNodes:
R.add_edge(node,random.sample([k for k in nx.non_neighbors(R,node)],1)[0])
return R
def scramble(M, p):
R = nx.Graph(M)
removeSet = [i for i in nx.edges_iter(R) if random.random() < p]
R.remove_edges_from(removeSet)
nodes = R.nodes()
addFromNodes = [random.sample(nodes, 1)[0] for i in removeSet]
for node in addFromNodes:
R.add_edge(node,
random.sample([k for k in nx.non_neighbors(R, node)], 1)[0])
return R
def run(self):
self.logging.debug("Start monitor-thread.")
link_usage = {}
link_usage.clear()
self.logging.debug("LinkUsage calc IN")
for src,dst in nx.edges_iter(self.topology):
#self.logging.debug("LinkUsage calc %s -> %s",src,dst)
f_list_ = self.bin_content[src][dst]
contention_ = 0
for f in f_list_:
contention_ += self.flow_map[f]
link_usage[(src,dst)]=contention_
self.logging.info("LinkUsage %s", link_usage)
def _prune_joins(graph):
"""
Prunes _join elements that have been fulfilled by the addition of
this patch.
DEPRECATED! But a useful function for detecting fulfilled +- joins
that match by element so I'm keeping it.
Pruning now done in _define_bond
Args:
graph (networkx graph): The residue to prun
"""
unpatched = [n for n in graph.nodes() if not graph.node[n]["patched"]]
for uun in unpatched:
neighbor_joins = [e[1] for e in nx.edges_iter(graph, nbunch=[uun]) if \
graph.node[e[1]]["residue"] != "self" and \
not graph.node[e[1]]["patched"]]
for nei in neighbor_joins:
if any(graph.node[e[1]]["element"] == graph.node[nei]["element"] for \
e in nx.edges_iter(graph, nbunch=[uun]) if \
graph.node[e[1]]["patched"]):
graph.remove_node(nei)
def calculate_accuracy(self, net_original, results):
#results dictionary {node_id: class_result}
counter=0
good=0
areResults = False
for item in results.iteritems():
areResults = True
if(item[0].label==item[1]):
good+=1
neighbs = [edge for edge in nx.edges_iter(net_original, item[0])]
if(len(neighbs)>0):
counter+=1
return good/float(counter) if areResults else -1
def volume(nodes, graph):
"""
Compute the volume for a list of nodes, which
is the number of edges in `graph` with at least one end in
nodes.
Params:
nodes...a list of strings for the nodes to compute the volume of.
graph...a networkx graph
>>> volume(['A', 'B', 'C'], example_graph())
4
"""
###TODO
pass
return (len(list(nx.edges_iter(graph, nodes))))
def getFolloweesAndFollowers(self, graph):
followees = {}
followers = {}
for node in networkx.edges_iter(graph):
if node[0] in followees:
followees[node[0]].append(node[1])
else:
followees[node[0]] = [node[1]]
if node[1] in followers:
followers[node[1]].append(node[0])
else:
followers[node[1]] = [node[0]]
return followees, followers
def init_full_cascade(nodes,nc,max_iterations= float("inf")):
activated = set(nodes)
q = Queue.Queue()
for node in nodes:
q.put((node, 0))
while not q.empty():
node, iteration = q.get()
if iteration <= max_iterations:
edges = nx.edges_iter(nc, nbunch=node)
for x in edges:
#if x[1] not in activated and edge_activate(nc[x[0]][x[1]]['weight'],nc.node[x[1]]['review_count']):
if x[1] not in activated and True:
activated.add(x[1])
q.put((x[1], iteration+1))
else:
return activated
#ind_cascade(node,nc,activated,q)
return activated
def _rewrite(cls, graph, stringize):
"""
Rewrite objects in graph.
:param graph: the graph
:param bool stringize: if True, stringize, otherwise destringize
"""
if stringize:
node_methods = [r.stringize for r in cls._NODE_REWRITERS]
edge_methods = [r.stringize for r in cls._EDGE_REWRITERS]
else:
node_methods = [r.destringize for r in cls._NODE_REWRITERS]
edge_methods = [r.destringize for r in cls._EDGE_REWRITERS]
for node in nx.nodes_iter(graph):
for rewriter in node_methods:
rewriter(graph, node)
for edge in nx.edges_iter(graph):
for rewriter in edge_methods:
rewriter(graph, edge)
def relation_counts(self):
edges = chain.from_iterable(self.G[src_n][target_n].values() for src_n, target_n in nx.edges_iter(self.G))
types = [e['type'] for e in edges]
grouped = groupby(sorted(types))
return dict((name, len(list(vals))) for name, vals in grouped)
if __name__ == "__main__":
TG = nx.MultiDiGraph()
TG.add_edge('a', 'b', startTime=1, duraTime=1)
TG.add_edge('a', 'b', startTime=2, duraTime=1)
TG.add_edge('a', 'c', startTime=2, duraTime=1)
TG.add_edge('a', 'c', startTime=4, duraTime=1)
TG.add_edge('b', 'f', startTime=5, duraTime=1)
TG.add_edge('c', 'f', startTime=6, duraTime=1)
TG.add_edge('c', 'g', startTime=7, duraTime=1)
quad_tuple_edges = []
memo = []
for edge in nx.edges_iter(TG):
if edge not in memo:
memo.append(edge)
r = quad_tuple(edge)
quad_tuple_edges.extend(r)
else:
continue
edge_stream = sorted(quad_tuple_edges, key=lambda item: item[2])
TDG = nx.DiGraph()
# add edge where duraTime = 1
for e in edge_stream:
TDG.add_edge((e[0], e[2]), (e[1], e[2] + e[3]), duraTime=1)
values = ex.predict_feats(W, b_arr, vect)
# Part 1:
# Get all neighbour feats of instance feats
start_feats = set()
expanded_feats = {} # first-level neighbs
for feat in feats:
for node in G.nodes_iter():
# if feat is found in the graph, then we get its neighbours and then break to
# next feat
if node == feat:
start_feats.add(feat)
# first level neighbours
for edge in nx.edges_iter(G, [node]):
nodea = edge[0]
nodeb = edge[1]
ew = G[nodea][nodeb]["weight"]
if nodeb not in start_feats:
expanded_feats[nodeb] = ew
# go to next feature
break
# Dict to hold all new feats
new_feats = {}
# We add all neighbs
for key, value in expanded_feats.iteritems():
def augmentEdges(g):
r1 = nx.edge_betweenness_centrality(g, normalized=True, weight='weight')
for x in nx.edges_iter(g):
g[x[0]][x[1]]['edge_betweenness_centrality'] = r1[x]
return g
def LocationLinks(self):
return list(nx.edges_iter(self.graph))
for s in range(runs-1):
chance = np.random.random_sample();
if chance < 0.1:#5*GG.number_of_nodes()/nodes:
ofile.write(str(s+1) + ',');
if i in scheduled:
temp = scheduled[i];
temp.append(s);
scheduled[i] = temp;
else:
temp = list();
temp.append(s);
scheduled[i] = temp;
centrality = nx.betweenness_centrality(GG);
for node in GG:
#print centrality[node];
wins[node,(s)*winsize + int(centrality[node]*200)-1] = i+1;
for ad in nx.edges_iter(GG,node):
offset = np.random.randint(1,3,[1,1]);
wins[ad,(s)*winsize + int(centrality[node]*200) + offset] = i+1;
ofile.seek(-1, os.SEEK_CUR);
ofile.write(',-');
ofile.write('\n');
print i;
sampled = np.random.randint(0,nodes-1,[1,100]);
sio.savemat('raster-' + timestamp + '.mat',{'wins':wins[sampled,:]})
def jsonloader(time="day",centrality="eigenvector",percent="100"):
print "test"
#-------------------------Define Edges-------------------------:
#allLines = open('merged.csv').read().encode('utf-8')
allLines = open('edges5.Csv').read().encode('utf-8')
#allLines = open(/csv).read().encode('utf-8')
data = StringIO.StringIO(allLines)
G = nx.Graph()
edges = nx.read_edgelist(data, delimiter=',', nodetype=unicode)
for e in edges.edges():
G.add_edge(*e)
N,K = edges.order(), edges.size()
print "Nodes: ", N
print "Edges: ", K
avg_deg = int(math.ceil(float(K)/N))
print "Average degree: ", avg_deg
degree = G.degree()
degreelist = []
for n in degree:
degreelist.append(degree[n])
degreelist = filter(lambda x: x>1, degreelist)
#set min %
degreelist.sort()
topp = 90
topn = 50
toplist = degreelist[int(len(degreelist) * topp/100) : int(len(degreelist))]
median=sorted(degreelist)[len(degreelist)/2]
mediantopp=sorted(toplist)[len(toplist)/2]
print "Degree Median: ", median
print "Degree Median(top %): ", mediantopp
Range = (max(degreelist)-min(degreelist))
Rangetopp = (max(toplist)-min(toplist))
print "Degree Range: ", Range
print "Degree Range(top %): ", Rangetopp
#'Fan-boy' trimmer
def remove_edges(g, in_degree):
g2=g.copy()
#d_in=g2.in_degree(g2)
#d_out=g2.out_degree(g2)
#print(d_in)
#print(d_out)
d = g2.degree(g2)
for n in g2.nodes():
#if d_in[n]==in_degree and d_out[n] == out_degree:
if d[n] <= in_degree:
g2.remove_node(n)
return g2
def remove_minoredges(g, topn):
import heapq
g3=g.copy()
d = g3.degree(g3)
#d.most_common()
a = sorted(d, key=d.get, reverse=False)[:int(len(d)-topn)]
for item in a:
g3.remove_node(item)
return g3
#G = remove_edges(G,mediantopp)
G = remove_minoredges(G,topn)
#-------------------------Finding Community-------------------------
import community
Gc = community.best_partition(G)
for n, m in Gc.items():
Gc[n] = int(m)
color = []
for nodes in nx.nodes_iter(G):
value = int(Gc[nodes]*100)
color.append(value)
#print "color: ",color
nx.set_node_attributes(G,'group',Gc)
#-------------------------Finding Centrality-------------------------
#大きさを定義するリストを作成
#bb=nx.degree_centrality(G)
#bb=nx.betweenness_centrality(G)
#bb=nx.closeness_centrality(G)
bb=nx.eigenvector_centrality(G)
for n, m in bb.items():
bb[n] = int(math.ceil(m*25))
size = []
for nodes in nx.nodes_iter(G):
value = int(bb[nodes]*100)
#print value
size.append(value)
#print len(size)
#print bb
#for n in len(size):
#nx.set_node_attributes(G, 'betweenness',
nx.set_node_attributes(G,'betweenness',bb)
#-------------------------Finding edge Centrality-------------------------
#線分の大きさを定義するリストを作成
cc=nx.edge_betweenness_centrality(G)
for n, m in cc.items():
cc[n] = int(math.ceil(m*500))
edgesize = []
for edges in nx.edges_iter(G):
value = int(cc[edges]*500)
edgesize.append(value)
nx.set_edge_attributes(G,'length',cc)
#--------------------------------------------------
G = remove_minoredges(G,topn)
size = np.asarray(size)
pos = nx.spring_layout(G)
#nx.draw_networkx_nodes(G,pos,node_color=color,alpha=0.8,node_size=size)
nx.draw_networkx_nodes(G,pos,alpha=0.8,node_size=size)
nx.draw_networkx_edges(G,pos,alpha=0.2,edge_size=edgesize)
nx.draw_networkx_labels(G,pos,font_size=10,font_color='black')
def save(G, fname):
from networkx.readwrite import json_graph
data = json_graph.dumps(G, sort_keys=True,indent=2)
f = open(fname, 'w')
f.write(data)
save(G, "./d3/graph.json")
plt.savefig('./d3/graph_merged.png')
# 关于图形的方法： nx.density(G) #返回图形的密度 nx.degree_histogram(G) #返回列表中每个值的频率 nx.info(G) #返回图形的摘要信息 ：节点数，边数，平均度 etc.. P=nx.create_empty_copy(G) #返回 移除边的 图形G的 副本 nx.is_directed(DG) #返回图形是否有向 #关于点的方法 nx.nodes(G) #以列表的形式返回所有节点 nx.number_of_nodes(G) #返回节点数 nx.all_neighbors(G,'tank') #以iterator的形式返回一个节点的所有邻居 nx.non_neighbors(G,'tank') #以iterator的形式返回一个节点的所有非邻居 nx.common_neighbors(G,'','') #以iterator的形式返回两个节点的公共邻居 nx.nodes_iter(G) #以iterator的形式返回所有节点 g = nx.compose(G,DG) #返会合并公共的node??? sorted(G.degree().values()) #关于边的方法 nx.edges(G) #以列表的形式返回所有的边 nx.number_of_edges(G) #返回边数 nx.non_edges(G) #以iterator的形式返回所有不存在的边 nx.edges_iter(G) #以iterator的形式返回所有边 #关于MultiDiGraph() G.successors(node) #返回一个节点的后继 G.predecessor(node) #返回一个节点的前任 G.number_of_edges(node,node) #返回两个节点间的边数 G.size() #返回图的边数 G.nodes_with_selfloops() #以列表形式返回(自己到自己）的节点 G.out_degree() #返回向外联系的数量；括号中可以填点，返回点的；也可不填，返回所有点的列表
def createjson(datas):
#-------------------------Define Edges-------------------------:
#allLines = open('edges5.Csv').read().encode('utf-8')
allLines = datas
#allLines = open(/csv).read().encode('utf-8')
list = allLines.splitlines()
datalist = []
for n in list:
if n.count(",") > 1:
logging.info(n)
n = n.replace(",",u"，").replace(u"，",",", 1)
datalist.append(n)
datalist = '\n'.join(datalist)
datalist = StringIO.StringIO(datalist)
G = nx.Graph()
edges = nx.read_edgelist(datalist, delimiter=',', nodetype=unicode)
for e in edges.edges():
G.add_edge(*e)
#-------------------------Calculate statistics-------------------------
N,K = edges.order(), edges.size()
logging.info("Nodes: ", N)
logging.debug("Edges: ", K)
avg_deg = int(math.ceil(float(K)/N))
logging.debug( "Average degree: ", avg_deg)
degree = G.degree()
degreelist = []
for n in degree:
degreelist.append(degree[n])
degreelist = filter(lambda x: x>1, degreelist)
#set min %
degreelist.sort()
topp = 90
topn = 50
toplist = degreelist[int(len(degreelist) * topp/100) : int(len(degreelist))]
median=sorted(degreelist)[len(degreelist)/2]
mediantopp=sorted(toplist)[len(toplist)/2]
logging.debug( "Degree Median: ", median)
logging.debug( "Degree Median(top %): ", mediantopp)
Range = (max(degreelist)-min(degreelist))
Rangetopp = (max(toplist)-min(toplist))
logging.debug( "Degree Range: ", Range)
logging.debug( "Degree Range(top %): ", Rangetopp)
#-------------------------Trim down tagalongs-------------------------
#'Fan-boy' trimmer
def remove_edges(g, in_degree):
g2=g.copy()
#d_in=g2.in_degree(g2)
#d_out=g2.out_degree(g2)
d = g2.degree(g2)
for n in g2.nodes():
#if d_in[n]==in_degree and d_out[n] == out_degree:
if d[n] <= in_degree:
g2.remove_node(n)
return g2
def remove_minoredges(g, topn):
import heapq
g3=g.copy()
d = g3.degree(g3)
#d.most_common()
a = sorted(d, key=d.get, reverse=False)[:int(len(d)-topn)]
for item in a:
g3.remove_node(item)
return g3
#G = remove_edges(G,mediantopp)
G = remove_minoredges(G,topn)
#-------------------------Finding Community-------------------------
import community
Gc = community.best_partition(G)
for n, m in Gc.items():
Gc[n] = int(m)
color = []
for nodes in nx.nodes_iter(G):
value = int(Gc[nodes]*100)
color.append(value)
nx.set_node_attributes(G,'group',Gc)
#-------------------------Finding Centrality-------------------------
#大きさを定義するリストを作成
#bb=nx.degree_centrality(G)
#bb=nx.betweenness_centrality(G)
#bb=nx.closeness_centrality(G)
bb=nx.eigenvector_centrality(G)
for n, m in bb.items():
bb[n] = int(math.ceil(m*25))
size = []
for nodes in nx.nodes_iter(G):
value = int(bb[nodes]*100)
size.append(value)
nx.set_node_attributes(G,'betweenness',bb)
#-------------------------Finding edge Centrality-------------------------
#線分の大きさを定義するリストを作成
cc=nx.edge_betweenness_centrality(G)
for n, m in cc.items():
cc[n] = int(math.ceil(m*500))
edgesize = []
for edges in nx.edges_iter(G):
value = int(cc[edges]*500)
edgesize.append(value)
nx.set_edge_attributes(G,'length',cc)
#--------------------------------------------------
G = remove_minoredges(G,topn)
from networkx.readwrite import json_graph
data = json_graph.dumps(G, sort_keys=True,indent=2)
return data
def get_lipid_head(self, selection):
"""
Obtains a name mapping for a lipid head group given a selection
describing a possible lipid.
Args:
selection (VMD atomsel): Selection to set names for
Returns:
(dict int->str) Atom index to resname matched
(dict int->str) Atom index to atom name matched up
(int) Atom index corresponding to - direction tail
Raises:
KeyError: if no matching possible
"""
resname = selection.get('resname')[0]
rgraph = self.parse_vmd_graph(selection)[0]
# Check if a lipid head group is part of this selection.
# Remove _join residues from the head so that subgraph match can
# be successfully completed
matches = {}
for matchname in (_ for _ in self.lipid_heads if self.known_res.get(_)):
graph = self.known_res.get(matchname)
truncated = nx.Graph(graph)
truncated.remove_nodes_from([n for n in graph.nodes() if \
graph.node[n]["residue"] != "self"])
matcher = isomorphism.GraphMatcher(rgraph, truncated,
node_match=self._check_atom_match)
if matcher.subgraph_is_isomorphic():
matches[matchname] = next(matcher.match())
if not matches:
return (None, None, None)
matchname = max(matches.keys(), key=(lambda x: len(self.known_res[x])))
match = matches[matchname]
graph = self.known_res.get(matchname)
# Generate naming dictionaries to return
nammatch = dict((i, graph.node[match[i]].get("atomname")) \
for i in match.keys() if \
graph.node[match[i]].get("residue") == "self")
resmatch = dict((i, graph.node[match[i]].get("resname")) \
for i in match.keys() if \
graph.node[match[i]].get("residue") == "self")
# Find atom index on non-truncated graph that corresponds to the
# - direction join atom. Necessary to figure out the order in which
# to list the tails.
minusbnded = [_ for _ in match.keys() if match[_] in \
[e[1] for e in nx.edges_iter(graph, nbunch=["-"])]]
if len(minusbnded) != 1:
raise ValueError("Could not identify tail attached to lipid %s:%s!"
% (resname, selection.get('resid')[0]))
minusidx = [_ for _ in atomsel("index %s" % minusbnded[0]).bonds[0] \
if _ not in match.keys()]
if len(minusidx) != 1:
raise ValueError("Could not identify tail attached to lipid %s:%s!"
% (resname, selection.get('resid')[0]))
return (resmatch, nammatch, minusidx[0])
path_feats = {}
threshold = 0.95
# go through values and save those above threshold into expanded_feats dict
for j in xrange(len(values)):
this_target_word = target_words[j]
value = values[j]
# only continue if not in feats already
if this_target_word not in feats:
# if predicted value exceeds threshold, we continue with it
if value >= threshold:
# add to expanded feats
predicted_feats[this_target_word] = float("{0:.4f}".format(value))
# first-level path feats
for edge in nx.edges_iter(G, [this_target_word]):
nodea = edge[0]
nodeb = edge[1]
edge_weight = G[nodea][nodeb]["weight"]
feat_val = value * edge_weight
#print "*", nodea, nodeb, edge_weight
if nodeb not in feats and nodeb not in predicted_feats:
path_feats[nodeb] = float("{0:.4f}".format(feat_val))
# Add predicted feats
for key, value in predicted_feats.iteritems():
if key not in feats:
sentences[i].append(key + ":" + str(value))
# Add path feats with weighted edges
def encodeAccessConstraint(accessConstraint, resource):
###
### TRUE/FALSE
###
if accessConstraint == 'true':
return True
elif accessConstraint == 'false':
return False
###
### ATTRIBUTE CONSTRAINT
###
elif len(accessConstraint) == 3 and accessConstraint[1] == 'in':
attrName = accessConstraint[0]
attrVals = accessConstraint[2]
attrVal = conf.resourceStructure.node[resource][attrName]
return any(attrVal == x for x in attrVals)
###
### UNARY: NOT
###
elif accessConstraint[0] == 'not':
constraint = encodeAccessConstraint(accessConstraint[1], resource)
return Not(constraint)
###
### BINARY: =>, AND, OR
###
elif any(accessConstraint[0] == x for x in ['and', 'or', '=>']):
constraintLeft = encodeAccessConstraint(accessConstraint[1], resource)
constraintRight = encodeAccessConstraint(accessConstraint[2], resource)
if accessConstraint[0] == 'and':
return And(constraintLeft, constraintRight)
elif accessConstraint[0] == 'or':
return Or(constraintLeft, constraintRight)
elif accessConstraint[0] == '=>':
return Implies(constraintLeft, constraintRight)
###
### EX
###
elif accessConstraint[0] == 'EX':
successorConstraints = []
for PEP in networkx.edges_iter(conf.resourceStructure, resource):
successor = PEP[1]
constraint = encodeAccessConstraint(accessConstraint, successor)
successorConstraints.append(And(template.PEPTemplate(PEP), constraint))
return Or(successorConstraints)
###
### AX
###
elif accessConstraint[0] == 'AX':
successorConstraints = []
for PEP in networkx.edges_iter(conf.resourceStructure, resource):
successor = PEP[1]
constraint = encodeAccessConstraint(accessConstraint, successor)
successorConstraints.append(Implies(template.PEPTemplate(PEP), constraint))
return And(successorConstraints)
###
### EU
###
elif accessConstraint[0] == 'EU':
return encodeUntilAccessConstraint(accessConstraint, resource, set())
###
### AU
###
elif accessConstraint[0] == 'AU':
return encodeUntilAccessConstraint(accessConstraint, resource, set())
###
### SYNTACTIC SHORTHANDS
###
elif accessConstraint[0] == 'AR':
return encodeAccessConstraint(['not', ['EU', ['not', accessConstraint[1]], ['not', accessConstraint[2]]]], resource)
elif accessConstraint[0] == 'AG':
return encodeAccessConstraint(['not', ['EU', 'true', ['not', accessConstraint[1]]]], resource)
elif accessConstraint[0] == 'EF':
return encodeAccessConstraint(['EU', 'true', accessConstraint[1]], resource)
elif accessConstraint[0] == 'AF':
return encodeAccessConstraint(['AU', 'true', accessConstraint[1]], resource)
else:
raise NameError('Could not encodeAccessConstraint access constraint: ' + str(accessConstraint))
default=0):
bib_graph[clean_author_handle][clean_next_author_handle][
'weight'] = bib_graph[clean_author_handle][
clean_next_author_handle]['weight'] + 1
else:
bib_graph.add_edge(clean_author_handle,
clean_next_author_handle,
weight=1)
node_weight = []
edge_weight = [
data.values()[0] for a, b, data in bib_graph.edges(data=True)
]
for n in range(0, len(bib_graph)):
node_weight.append(0)
for e in nx.edges_iter(bib_graph, bib_graph.nodes()[n]):
node_weight[n] = node_weight[n] + bib_graph.get_edge_data(
e[0], e[1]).values()[0]
node_weight = [n * 2.5 for n in node_weight]
fig = plt.figure(1, figsize=(8, 8))
if year_iter == max_year_input:
init_pos = nx.pydot_layout(bib_graph)
for node in bib_graph:
pos[node] = init_pos[node]
nx.draw_networkx(bib_graph,
pos,
node_size=node_weight,
node_color=node_weight,
edge_color=edge_weight,
cmap=plt.cm.OrRd,
#values = ex.predict_feats(W, b_arr, vect)
# Part 1:
# Get all neighbour feats of instance feats
start_feats = set()
expanded_feats = {} # first-level neighbs
for feat in feats:
for node in G.nodes_iter():
# if feat is found in the graph, then we get its neighbours and then break to
# next feat
if node == feat:
start_feats.add(feat)
# first level neighbours
for edge in nx.edges_iter(G, [node]):
nodea = edge[0]
nodeb = edge[1]
ew = G[nodea][nodeb]["weight"]
if nodeb not in start_feats:
expanded_feats[nodeb] = ew
# go to next feature
break
# Dict to hold all new feats
new_feats = {}
# We add all neighbs
for key, value in expanded_feats.iteritems():
continue for author_index, author_handle in enumerate(item_handle.get_authorsList()[:len(item_handle.get_authorsList())-1]): clean_author_handle = str(author_handle) for next_author_index, next_author_handle in enumerate(item_handle.get_authorsList()[author_index+1:]): clean_next_author_handle = str(next_author_handle) if bib_graph.get_edge_data(clean_author_handle, clean_next_author_handle, default=0): bib_graph[clean_author_handle][clean_next_author_handle]['weight'] = bib_graph[clean_author_handle][clean_next_author_handle]['weight'] + 1 else: bib_graph.add_edge(clean_author_handle, clean_next_author_handle, weight = 1) node_weight=[] edge_weight = [data.values()[0] for a,b,data in bib_graph.edges(data=True)] for n in range(0,len(bib_graph)): node_weight.append(0) for e in nx.edges_iter(bib_graph, bib_graph.nodes()[n]): node_weight[n] = node_weight[n] + bib_graph.get_edge_data(e[0],e[1]).values()[0] node_weight = [n*2.5 for n in node_weight] fig = plt.figure(1, figsize=(8, 8)) if year_iter == max_year_input: init_pos=nx.pydot_layout(bib_graph) for node in bib_graph: pos[node] = init_pos[node] nx.draw_networkx(bib_graph, pos, node_size=node_weight, node_color=node_weight, edge_color=edge_weight, cmap=plt.cm.OrRd, font_size=9) if year_iter == max_year_input: xmax=max(xx for xx,yy in pos.values()) xmin=min(xx for xx,yy in pos.values()) ymax=max(yy for xx,yy in pos.values()) ymin=min(yy for xx,yy in pos.values())
# <codecell>
nx.draw_spring(G2000)
plt.show()
# <codecell>
G2000.degree('michael')
# <codecell>
nx.write_adjlist(karate, 'karateclub_adj.csv')
# <codecell>
for edge in nx.edges_iter(karate):
print edge
# <codecell>
karate_json = {"nodes":[],"links":[]}
for node in nx.nodes_iter(karate):
n = {"name":node, "group":1}
karate_json["nodes"].append(n)
for edge in nx.edges_iter(karate):
e = {"source":edge[0], "target":edge[1], "value":1}
karate_json["links"].append(e)
# <codecell>
print karate_json
def encodeAccessConstraint(accessConstraint, resource):
###
### TRUE/FALSE
###
if accessConstraint == 'true':
return True
elif accessConstraint == 'false':
return False
###
### ATTRIBUTE CONSTRAINT
###
elif len(accessConstraint) == 3 and accessConstraint[1] == 'in':
attrName = accessConstraint[0]
attrVals = accessConstraint[2]
attrVal = conf.resourceStructure.node[resource][attrName]
return any(attrVal == x for x in attrVals)
###
### UNARY: NOT
###
elif accessConstraint[0] == 'not':
constraint = encodeAccessConstraint(accessConstraint[1], resource)
return Not(constraint)
###
### BINARY: =>, AND, OR
###
elif any(accessConstraint[0] == x for x in ['and', 'or', '=>']):
constraintLeft = encodeAccessConstraint(accessConstraint[1], resource)
constraintRight = encodeAccessConstraint(accessConstraint[2], resource)
if accessConstraint[0] == 'and':
return And(constraintLeft, constraintRight)
elif accessConstraint[0] == 'or':
return Or(constraintLeft, constraintRight)
elif accessConstraint[0] == '=>':
return Implies(constraintLeft, constraintRight)
###
### EX
###
elif accessConstraint[0] == 'EX':
successorConstraints = []
for PEP in networkx.edges_iter(conf.resourceStructure, resource):
successor = PEP[1]
constraint = encodeAccessConstraint(accessConstraint, successor)
successorConstraints.append(
And(template.PEPTemplate(PEP), constraint))
return Or(successorConstraints)
###
### AX
###
elif accessConstraint[0] == 'AX':
successorConstraints = []
for PEP in networkx.edges_iter(conf.resourceStructure, resource):
successor = PEP[1]
constraint = encodeAccessConstraint(accessConstraint, successor)
successorConstraints.append(
Implies(template.PEPTemplate(PEP), constraint))
return And(successorConstraints)
###
### EU
###
elif accessConstraint[0] == 'EU':
return encodeUntilAccessConstraint(accessConstraint, resource, set())
###
### AU
###
elif accessConstraint[0] == 'AU':
return encodeUntilAccessConstraint(accessConstraint, resource, set())
###
### SYNTACTIC SHORTHANDS
###
elif accessConstraint[0] == 'AR':
return encodeAccessConstraint([
'not',
['EU', ['not', accessConstraint[1]], ['not', accessConstraint[2]]]
], resource)
elif accessConstraint[0] == 'AG':
return encodeAccessConstraint(
['not', ['EU', 'true', ['not', accessConstraint[1]]]], resource)
elif accessConstraint[0] == 'EF':
return encodeAccessConstraint(['EU', 'true', accessConstraint[1]],
resource)
elif accessConstraint[0] == 'AF':
return encodeAccessConstraint(['AU', 'true', accessConstraint[1]],
resource)
else:
raise NameError(
'Could not encodeAccessConstraint access constraint: ' +
str(accessConstraint))
# Get all neighbour feats of instance feats
start_feats = set()
expanded_feats = {} # first-level neighbs
# to add only mutual, we record an array of all nodes each is a neighbour of, then at the end
# we can check it's neighb array and see if it's more than 1
counts = {}
# instance feats
for feat in feats:
for node in G.nodes_iter():
# if feat is found in the graph, then we get its neighbours and then break to next feat
if node == feat:
start_feats.add(feat)
# first level neighbours
for edge in nx.edges_iter(G, [node]):
nodeb = edge[1]
ew = G[node][nodeb]["weight"]
if nodeb not in start_feats:
# add weight to dict
expanded_feats[nodeb] = ew
# and also log node in set of neighbs
try:
n = counts[nodeb]
n.add(node)
counts[nodeb] = n
except:
counts[nodeb] = set([node])
def lbp(g):
#initial guess for messages (set them all to 1)
for (u,v) in g.edges():
g[u][v]['mssd_h']=float(1)
g[u][v]['mssd_a']=float(1)
g[u][v]['mssd_f']=float(1)
g[u][v]['msds_h']=float(1)
g[u][v]['msds_a']=float(1)
g[u][v]['msds_f']=float(1)
g[u][v]['mssdO_h']=float(1)
g[u][v]['mssdO_a']=float(1)
g[u][v]['mssdO_f']=float(1)
g[u][v]['msdsO_h']=float(1)
g[u][v]['msdsO_a']=float(1)
g[u][v]['msdsO_f']=float(1)
#compute degree of each node
for u in g.nodes():
g.node[u]['neighbours']=list(set(nx.edges_iter(g,nbunch=u)))
g.node[u]['degree']=float(len(g.node[u]['neighbours']))
#compute sum of the weight.
for u in g.nodes():
n=g.node[u]['neighbours']
suma=0
for (h,w) in n:
suma=suma+g[h][w]['weight']
g.node[u]['sumweights']=float(suma)
# the prior for all the users (based on weight and degree)
for u in g.nodes():
g.node[u]['prior_h']=float(1)/(g.node[u]['sumweights']/g.node[u]['degree'])
g.node[u]['prior_a']=g.node[u]['prior_f']=float((float(1)-g.node[u]['prior_h'])/2)
# g.node[u]['prior_h']=g.node[u]['prior_a']=g.node[u]['prior_f']=float(0.333)
#compute propagation matrix values
for (u,v) in g.edges():
comp=auxi.propagation(g[u][v]['weight'],0.05)
g[u][v]['c_ff']=float(comp[0])
g[u][v]['c_fa']=float(comp[1])
g[u][v]['c_fh']=float(comp[2])
g[u][v]['c_af']=float(comp[3])
g[u][v]['c_aa']=float(comp[4])
g[u][v]['c_ah']=float(comp[5])
g[u][v]['c_hf']=float(comp[6])
g[u][v]['c_ha']=float(comp[7])
g[u][v]['c_hh']=float(comp[8])
#set who is the source and who is the sink.
for (u,v) in g.edges():
g[u][v]['source']=u
g[u][v]['dest']=v
# save the neighbours.
for (u,v) in g.edges():
g[v][u]['ns']=list(set(nx.edges_iter(g,nbunch=g[u][v]['source']))-set([(u,v)]))
g[u][v]['nd']=list(set(nx.edges_iter(g,nbunch=g[u][v]['dest']))-set([(v,u)]))
#main loop: we iterate until the stopping criterion is reached on the L-2 norm of the messages.
tol=1
numedges=len(g.edges())
vector0=[float(10)]*numedges*6
j=1
while j<5:
vector=[]
#message update from source to dest
for (u,v) in g.edges():
a=auxi.prods((u,v),'h',g)
b=auxi.prods((u,v),'a',g)
c=auxi.prods((u,v),'f',g)
g[u][v]['mssd_h']=g[u][v]['c_hh']*g.node[g[u][v]['source']]['prior_h']*a+g[u][v]['c_ah']*g.node[g[u][v]['source']]['prior_a']*b+g[u][v]['c_fh']*g.node[g[u][v]['source']]['prior_f']*c
g[u][v]['mssd_a']=g[u][v]['c_ha']*g.node[g[u][v]['source']]['prior_h']*a+g[u][v]['c_aa']*g.node[g[u][v]['source']]['prior_a']*b+g[u][v]['c_fa']*g.node[g[u][v]['source']]['prior_f']*c
g[u][v]['mssd_f']=g[u][v]['c_hf']*g.node[g[u][v]['source']]['prior_h']*a+g[u][v]['c_af']*g.node[g[u][v]['source']]['prior_a']*b+g[u][v]['c_ff']*g.node[g[u][v]['source']]['prior_f']*c
alpha=g[u][v]['mssd_h']+g[u][v]['mssd_a']+g[u][v]['mssd_f']
g[u][v]['mssd_h']=g[u][v]['mssd_h']/alpha
g[u][v]['mssd_a']=g[u][v]['mssd_a']/alpha
g[u][v]['mssd_f']=g[u][v]['mssd_f']/alpha
vector.append(g[u][v]['mssd_h'])
vector.append(g[u][v]['mssd_a'])
vector.append(g[u][v]['mssd_f'])
#message update from dest to source
for (u,v) in g.edges():
a=auxi.prodd((u,v),'h',g)
b=auxi.prodd((u,v),'a',g)
c=auxi.prodd((u,v),'f',g)
g[u][v]['msds_h']=g[u][v]['c_hh']*g.node[g[u][v]['dest']]['prior_h']*a+g[u][v]['c_ha']*g.node[g[u][v]['dest']]['prior_a']*b+g[u][v]['c_hf']*g.node[g[u][v]['dest']]['prior_f']*c
g[u][v]['msds_a']=g[u][v]['c_ah']*g.node[g[u][v]['dest']]['prior_h']*a+g[u][v]['c_aa']*g.node[g[u][v]['dest']]['prior_a']*b+g[u][v]['c_af']*g.node[g[u][v]['dest']]['prior_f']*c
g[u][v]['msds_f']=g[u][v]['c_fh']*g.node[g[u][v]['dest']]['prior_h']*a+g[u][v]['c_fa']*g.node[g[u][v]['dest']]['prior_a']*b+g[u][v]['c_ff']*g.node[g[u][v]['dest']]['prior_f']*c
alpha=g[u][v]['msds_h']+g[u][v]['msds_a']+g[u][v]['msds_f']
g[u][v]['msds_h']=g[u][v]['msds_h']/alpha
g[u][v]['msds_a']=g[u][v]['msds_a']/alpha
g[u][v]['msds_f']=g[u][v]['msds_f']/alpha
vector.append(g[u][v]['msds_h'])
vector.append(g[u][v]['msds_a'])
vector.append(g[u][v]['msds_f'])
#update old messages
for (u,v) in g.edges():
g[u][v]['mssdO_h']=g[u][v]['mssd_h']
g[u][v]['mssdO_a']=g[u][v]['mssd_a']
g[u][v]['mssdO_f']=g[u][v]['mssd_f']
g[u][v]['msdsO_h']=g[u][v]['msds_h']
g[u][v]['msdsO_a']=g[u][v]['msds_a']
g[u][v]['msdsO_f']=g[u][v]['msds_f']
for u in g.nodes():
g.node[u]['belief_h']=g.node[u]['prior_h']*auxi.prodnode(u,'h',g)
g.node[u]['belief_a']=g.node[u]['prior_a']*auxi.prodnode(u,'a',g)
g.node[u]['belief_f']=g.node[u]['prior_f']*auxi.prodnode(u,'f',g)
alpha=g.node[u]['belief_h']+g.node[u]['belief_a']+g.node[u]['belief_f']
g.node[u]['belief_h']=g.node[u]['belief_h']/alpha
g.node[u]['belief_a']=g.node[u]['belief_a']/alpha
g.node[u]['belief_f']=g.node[u]['belief_f']/alpha
g.node[u]['prior_h']=g.node[u]['belief_h']
g.node[u]['prior_a']=g.node[u]['belief_a']
g.node[u]['prior_f']=g.node[u]['belief_f']
tol=np.linalg.norm(np.array(vector)-np.array(vector0),ord=2)
vector0=vector
print j
print tol
j=j+1
#compute final beliefs:
for u in g.nodes():
g.node[u]['belief_h']=g.node[u]['prior_h']*auxi.prodnode(u,'h',g)
g.node[u]['belief_a']=g.node[u]['prior_a']*auxi.prodnode(u,'a',g)
g.node[u]['belief_f']=g.node[u]['prior_f']*auxi.prodnode(u,'f',g)
alpha=g.node[u]['belief_h']+g.node[u]['belief_a']+g.node[u]['belief_f']
g.node[u]['belief_h']=g.node[u]['belief_h']/alpha
g.node[u]['belief_a']=g.node[u]['belief_a']/alpha
g.node[u]['belief_f']=g.node[u]['belief_f']/alpha
#convert back to float:
for u in g.nodes():
g.node[u]['belief_h']=float(g.node[u]['belief_h'])
g.node[u]['belief_a']=float(g.node[u]['belief_a'])
g.node[u]['belief_f']=float(g.node[u]['belief_f'])
g.node[u]['prior_h']=float(g.node[u]['prior_h'])
g.node[u]['prior_a']=float(g.node[u]['prior_a'])
g.node[u]['prior_f']=float(g.node[u]['prior_f'])
for (u,v) in g.edges():
g[u][v]['mssd_h']=float(g[u][v]['mssd_h'])
g[u][v]['mssd_a']=float(g[u][v]['mssd_a'])
g[u][v]['mssd_f']=float(g[u][v]['mssd_f'])
g[u][v]['msds_h']=float(g[u][v]['msds_h'])
g[u][v]['msds_a']=float(g[u][v]['msds_a'])
g[u][v]['msds_f']=float(g[u][v]['msds_f'])
g[u][v]['c_ff']=float(g[u][v]['c_ff'])
g[u][v]['c_fa']=float(g[u][v]['c_fa'])
g[u][v]['c_fh']=float(g[u][v]['c_fh'])
g[u][v]['c_af']=float(g[u][v]['c_af'])
g[u][v]['c_aa']=float(g[u][v]['c_aa'])
g[u][v]['c_ah']=float(g[u][v]['c_ah'])
g[u][v]['c_hf']=float(g[u][v]['c_hf'])
g[u][v]['c_ha']=float(g[u][v]['c_ha'])
g[u][v]['c_hh']=float(g[u][v]['c_hh'])
fraud_belief={}
for u in g.nodes():
fraud_belief[u]={'belief_f':g.node[u]['belief_f'], 'degree':len(g.node[u]['neighbours']),'sumweight':g.node[u]['sumweights']}
users=pd.DataFrame.from_dict(fraud_belief,orient='index')
users=users.reset_index()
users.columns=['userid','belief_f','sumweight','degree']
users = users.sort(['userid'], ascending=False)
users[['userid']] = users[['userid']].astype(str)
return users, j
for key in sorted(predicted_feats,
key=predicted_feats.get,
reverse=True):
ranked_by_pred.append(key)
# Get instance feats that are in ClassiNet
instance_feats = []
for feat in feats:
for node in G.nodes_iter():
if node == feat:
instance_feats.append(feat)
# Get neighbouring nodes in graph from starting points given by instance feats and predicted feats
neighb_feats = {}
for feat in instance_feats:
for edge in nx.edges_iter(G, [feat]):
nodea = edge[0]
nodeb = edge[1]
#edge_weight = G[nodea][nodeb]
#print nodea, nodeb
if nodeb not in feats:
try:
neighb_feats[nodeb] += 1
except:
neighb_feats[nodeb] = 1
for feat in predicted_feats:
for edge in nx.edges_iter(G, [feat]):
nodea = edge[0]
nodeb = edge[1]
#edge_weight = G[nodea][nodeb]
#print nodea, nodeb
# <codecell>
nx.draw_spring(G2000)
plt.show()
# <codecell>
G2000.degree('michael')
# <codecell>
nx.write_adjlist(karate, 'karateclub_adj.csv')
# <codecell>
for edge in nx.edges_iter(karate):
print edge
# <codecell>
karate_json = {"nodes": [], "links": []}
for node in nx.nodes_iter(karate):
n = {"name": node, "group": 1}
karate_json["nodes"].append(n)
for edge in nx.edges_iter(karate):
e = {"source": edge[0], "target": edge[1], "value": 1}
karate_json["links"].append(e)
# <codecell>
print karate_json
def thin(G,p):
R = nx.Graph(G)
removeSet = [i for i in nx.edges_iter(R) if random.random() < p]
R.remove_edges_from(removeSet)
return R
def relation_counts(self):
edges = chain.from_iterable(self.G[src_n][target_n].values() for src_n, target_n in nx.edges_iter(self.G))
types = [e['type'] for e in edges]
grouped = groupby(sorted(types))
return dict((name, len(list(vals))) for name, vals in grouped)
threshold = 0.9
min_value = 0.5
# go through values and save those above threshold into expanded_feats dict
for j in xrange(len(values)):
this_target_word = target_words[j]
value = values[j]
# only continue if not in feats already
if this_target_word not in feats:
# if predicted value exceeds threshold, we continue with it
if value >= threshold:
# add to expanded feats
predicted_feats[this_target_word] = float("{0:.4f}".format(value))
# first-level path feats
for edge in nx.edges_iter(G, [this_target_word]):
nodea = edge[0]
nodeb = edge[1]
edge_weight = G[nodea][nodeb]["weight"]
feat_val = value * edge_weight
print "*", nodea, nodeb, edge_weight
# only continue if value is reasonable
if feat_val >= min_value:
if nodeb not in feats and nodeb not in predicted_feats:
path_feats[nodeb] = float("{0:.4f}".format(feat_val))
# include second-level path feats if set
if n == 2:
for edge2 in nx.edges_iter(G, [nodeb]):
nodea2 = edge2[0]
def test_edges_iter(self):
assert_equal(list(self.G.edges_iter()),list(networkx.edges_iter(self.G)))
assert_equal(list(self.DG.edges_iter()),list(networkx.edges_iter(self.DG)))
assert_equal(list(self.G.edges_iter(nbunch=[0,1,3])),list(networkx.edges_iter(self.G,nbunch=[0,1,3])))
assert_equal(list(self.DG.edges_iter(nbunch=[0,1,3])),list(networkx.edges_iter(self.DG,nbunch=[0,1,3])))
threshold = 0.95
# go through values and save those above threshold into expanded_feats dict
for j in xrange(len(values)):
this_target_word = target_words[j]
value = values[j]
# only continue if not in feats already
if this_target_word not in feats:
# if predicted value exceeds threshold, we continue with it
if value >= threshold:
# add to expanded feats
predicted_feats[this_target_word] = float(
"{0:.4f}".format(value))
# first-level path feats
for edge in nx.edges_iter(G, [this_target_word]):
nodea = edge[0]
nodeb = edge[1]
edge_weight = G[nodea][nodeb]["weight"]
feat_val = value * edge_weight
#print "*", nodea, nodeb, edge_weight
if nodeb not in feats and nodeb not in predicted_feats:
path_feats[nodeb] = float(
"{0:.4f}".format(feat_val))
# Add predicted feats
for key, value in predicted_feats.iteritems():
if key not in feats:
sentences[i].append(key + ":" + str(value))
def count_triad_motifs(network, directed=None):
"""
Counts the occurences of triad motifs in a network.
Arguments:
network => The input network.
directed => Whether or not the network is directed.
Returns:
A fixed-size array with indices representing unique triad motifs and the values
representing their number of occurences within the network.
"""
if directed or nx.is_directed(network):
# Initialize an array for storing our motif counts. Each index represents the following motif:
# 0 => a <- b -> c
# 1 => a -> b <- c
# 2 => b -> a -> c
# 3 => a -> b <-> c
# 4 => c <-> a -> b
# 5 => a <-> b <-> c
# 6 => a <- b -> c <- a
# 7 => a -> b -> c -> a
# 8 => c <-> a -> b <- c
# 9 => c <- a -> b <-> c
# 10 => a <-> c -> b -> a
# 11 => a <-> b <-> c -> a
# 12 => a <-> b <-> c <-> a
motif_counts = np.zeros(shape=(13,), dtype=np.int)
# Store a set of visited node combinations so repeats don't occur.
visited_triplets = []
# Iterate through the valid edges.
for a, b in sorted(nx.edges_iter(network)):
# Take all unique c nodes that form valid a, b, c triplets.
# By ensuring that all neighbors have node ID greater than b we prevent any unique
# node triplets from being repeated.
c_neighbors = set(
[
neighbor
for neighbor in itertools.chain(nx.all_neighbors(network, a), nx.all_neighbors(network, b))
if neighbor != a and neighbor != b
]
)
# Iterate through the valid a, b, c triplets.
for c in c_neighbors:
# Make sure unique node triplets aren't repeated.
sorted_abc = sorted((a, b, c))
if sorted_abc in visited_triplets:
continue
else:
visited_triplets.append(sorted_abc)
# a <-------> b
# a c b
if network.has_edge(b, a):
# a <-------> b
# a --> c b
if network.has_edge(a, c):
# a <-------> b
# a <-> c b
if network.has_edge(c, a):
# a <-------> b
# a <-> c --> b
if network.has_edge(c, b):
# a <-------> b
# a <-> c <-> b
if network.has_edge(b, c):
motif_counts[12] += 1
# a <-------> b
# a <-> c --> b
else:
motif_counts[11] += 1
# a <-------> b
# a <-> c <-- b
elif network.has_edge(b, c):
motif_counts[11] += 1
# a <-------> b
# a <-> c b
else:
motif_counts[5] += 1
# a <-------> b
# a --> c b
else:
# a <-------> b
# a --> c --> b
if network.has_edge(c, b):
# a <-------> b
# a --> c <-> b
if network.has_edge(b, c):
motif_counts[11] += 1
# a <-------> b
# a --> c --> b
else:
motif_counts[10] += 1
# a <-------> b
# a --> c <-- b
elif network.has_edge(b, c):
motif_counts[8] += 1
# a <-------> b
# a --> c b
else:
motif_counts[4] += 1
# a <-------> b
# a <-- c b
elif network.has_edge(c, a):
# a <-------> b
# a <-- c --> b
if network.has_edge(c, b):
# a <-------> b
# a <-- c <-> b
if network.has_edge(b, c):
motif_counts[11] += 1
# a <-------> b
# a <-- c --> b
else:
motif_counts[9] += 1
# a <-------> b
# a <-- c <-- b
elif network.has_edge(b, c):
motif_counts[10] += 1
# a <-------> b
# a <-- c b
else:
motif_counts[3] += 1
# a <-------> b
# a c <-- b
elif network.has_edge(b, c):
# a <-------> b
# a c <-> b
if network.has_edge(c, b):
motif_counts[5] += 1
# a <-------> b
# a c <-- b
else:
motif_counts[4] += 1
# a <-------> b
# a c --> b
else:
motif_counts[3] += 1
# a --------> b
# a c b
else:
# a --------> b
# a --> c b
if network.has_edge(a, c):
# a --------> b
# a <-> c b
if network.has_edge(c, a):
# a --------> b
# a <-> c --> b
if network.has_edge(c, b):
# a --------> b
# a <-> c <-> b
if network.has_edge(b, c):
motif_counts[11] += 1
# a --------> b
# a <-> c --> b
else:
motif_counts[8] += 1
# a --------> b
# a <-> c <-- b
elif network.has_edge(b, c):
motif_counts[10] += 1
# a --------> b
# a <-> c b
else:
motif_counts[4] += 1
# a --------> b
# a --> c b
else:
# a --------> b
# a --> c --> b
if network.has_edge(c, b):
# a --------> b
# a --> c <-> b
if network.has_edge(b, c):
motif_counts[9] += 1
# a --------> b
# a --> c --> b
else:
motif_counts[6] += 1
# a --------> b
# a --> c <-- b
elif network.has_edge(b, c):
motif_counts[6] += 1
# a --------> b
# a --> c b
else:
motif_counts[0] += 1
# a --------> b
# a <-- c b
elif network.has_edge(c, a):
# a --------> b
# a <-- c --> b
if network.has_edge(c, b):
# a --------> b
# a <-- c <-> b
if network.has_edge(b, c):
motif_counts[10] += 1
# a --------> b
# a <-- c --> b
else:
motif_counts[6] += 1
# a --------> b
# a <-- c <-- b
elif network.has_edge(b, c):
motif_counts[7] += 1
# a --------> b
# a <-- c b
else:
motif_counts[2] += 1
# a --------> b
# a c <-- b
elif network.has_edge(b, c):
# a --------> b
# a c <-> b
if network.has_edge(c, b):
motif_counts[3] += 1
# a --------> b
# a c <-- b
else:
motif_counts[2] += 1
# a --------> b
# a c --> b
else:
motif_counts[1] += 1
else:
# Initialize an array for storing our motif counts. Each index represents the following motif:
# 0 => a - b - c - a
# 1 => a - b - c
motif_counts = np.zeros(shape=(2,), dtype=np.int)
# Iterate through the edges in the network.
for a, b in sorted(nx.edges_iter(network)):
# Find all node a neighbors such that their node ID is greater than node b (which, by the sorted
# nature of nx.edges_iter, will always be greater than node a).
a_neighbors = set([neighbor for neighbor in network[a] if neighbor > b])
# Find all node b neighbors such that their node ID is greater than node b. The prevents repeated
# consideration of node triplets.
b_neighbors = set([neighbor for neighbor in network[b] if neighbor > b])
# The number of triangle motifs is the number of common neighbors of a and b.
motif_counts[0] += len(a_neighbors.intersection(b_neighbors))
# The number of chain motifs is the number of unshared neighbors or a and b.
motif_counts[1] += len(a_neighbors.symmetric_difference(b_neighbors))
return motif_counts
def lbp(g, delta, df_1):
# initial guess for messages (set them all to 1)
arr1 = g.edges()
arr2 = range(len(df_1))
for (u, v), i in zip(arr1, arr2):
g[u][v]['mssd_h'] = Decimal(df_1['honest_propabiliy'][i])
g[u][v]['mssd_s'] = Decimal(df_1['fruad_propabiliy'][i])
g[u][v]['msds_h'] = Decimal(df_1['honest_propabiliy'][i])
g[u][v]['msds_s'] = Decimal(df_1['fruad_propabiliy'][i])
g[u][v]['mssdO_h'] = Decimal(df_1['honest_propabiliy'][i])
g[u][v]['mssdO_s'] = Decimal(df_1['fruad_propabiliy'][i])
g[u][v]['msdsO_h'] = Decimal(df_1['honest_propabiliy'][i])
g[u][v]['msdsO_s'] = Decimal(df_1['fruad_propabiliy'][i])
# compute degree of each node
for u in g.nodes():
g.node[u]['neighbours'] = list(set(nx.edges_iter(g, nbunch=u)))
g.node[u]['degree'] = Decimal(len(g.node[u]['neighbours']))
# compute sum of the weight.
for u in g.nodes():
n = g.node[u]['neighbours']
suma = 0
for (h, w) in n:
suma = suma + g[h][w]['weight']
g.node[u]['sumweights'] = Decimal(suma)
# the prior for all the users (based on weight and degree)
for u in g.nodes():
g.node[u]['prior_h'] = Decimal(1) / (g.node[u]['sumweights'] /
g.node[u]['degree'])
g.node[u]['prior_s'] = Decimal(1) - g.node[u]['prior_h']
# compute compatibility potentials
for (u, v) in g.edges():
comp = auxi.compatibility(g[u][v]['weight'], delta)
g[u][v]['c_hh'] = comp[0]
g[u][v]['c_sh'] = comp[1]
g[u][v]['c_hs'] = comp[2]
g[u][v]['c_ss'] = comp[3]
# set who is the source and who is the sink.
for (u, v) in g.edges():
g[u][v]['source'] = u
g[u][v]['dest'] = v
# save the neighbours.
for (u, v) in g.edges():
g[v][u]['ns'] = list(
set(nx.edges_iter(g, nbunch=g[u][v]['source'])) - set([(u, v)]))
g[u][v]['nd'] = list(
set(nx.edges_iter(g, nbunch=g[u][v]['dest'])) - set([(v, u)]))
# main loop: we iterate until the stopping criterion is reached on the L-2 norm of the messages.
delta = 0.0001
tol = 1
numedges = len(g.edges())
vector0 = [Decimal(10)] * numedges * 4
j = 1
str2 = '’'
while tol > delta:
vector = []
# message update from source to dest
for (u, v) in g.edges():
a = auxi.prods(u, v, 'h', g)
b = auxi.prods(u, v, 's', g)
g[u][v]['mssd_h'] = g[u][v]['c_hh'] * g.node[
g[u][v]['source']]['prior_h'] * a + g[u][v]['c_sh'] * g.node[
g[u][v]['source']]['prior_s'] * b
g[u][v]['mssd_s'] = g[u][v]['c_hs'] * g.node[
g[u][v]['source']]['prior_h'] * a + g[u][v]['c_ss'] * g.node[
g[u][v]['source']]['prior_s'] * b
alpha = g[u][v]['mssd_h'] + g[u][v]['mssd_s']
g[u][v]['mssd_h'] = g[u][v]['mssd_h'] / alpha
g[u][v]['mssd_s'] = g[u][v]['mssd_s'] / alpha
vector.append(g[u][v]['mssd_h'])
vector.append(g[u][v]['mssd_s'])
# message update from dest to source
for (u, v) in g.edges():
a = auxi.prodd(u, v, 'h', g)
b = auxi.prodd(u, v, 's', g)
g[u][v]['msds_h'] = g[u][v]['c_hh'] * g.node[
g[u][v]['dest']]['prior_h'] * a + g[u][v]['c_hs'] * g.node[
g[u][v]['dest']]['prior_s'] * b
g[u][v]['msds_s'] = g[u][v]['c_sh'] * g.node[
g[u][v]['dest']]['prior_h'] * a + g[u][v]['c_ss'] * g.node[
g[u][v]['dest']]['prior_s'] * b
alpha = g[u][v]['msds_h'] + g[u][v]['msds_s']
g[u][v]['msds_h'] = g[u][v]['msds_h'] / alpha
g[u][v]['msds_s'] = g[u][v]['msds_s'] / alpha
vector.append(g[u][v]['msds_h'])
vector.append(g[u][v]['msds_s'])
# update old messages
for (u, v) in g.edges():
g[u][v]['mssdO_h'] = g[u][v]['mssd_h']
g[u][v]['mssdO_s'] = g[u][v]['mssd_s']
g[u][v]['msdsO_h'] = g[u][v]['msds_h']
g[u][v]['msdsO_s'] = g[u][v]['msds_s']
tol = np.linalg.norm(np.array(vector) - np.array(vector0), ord=2)
vector0 = vector
print(j)
print(tol)
j = j + 1
# compute final beliefs:
for u in g.nodes():
g.node[u]['belief_h'] = g.node[u]['prior_h'] * auxi.prodnode(u, 'h', g)
g.node[u]['belief_s'] = g.node[u]['prior_s'] * auxi.prodnode(u, 's', g)
alpha = g.node[u]['belief_h'] + g.node[u]['belief_s']
g.node[u]['belief_h'] = g.node[u]['belief_h'] / alpha
g.node[u]['belief_s'] = g.node[u]['belief_s'] / alpha
# convert back to float:
for u in g.nodes():
g.node[u]['belief_h'] = float(g.node[u]['belief_h'])
g.node[u]['belief_s'] = float(g.node[u]['belief_s'])
g.node[u]['prior_h'] = float(g.node[u]['prior_h'])
g.node[u]['prior_s'] = float(g.node[u]['prior_s'])
for (u, v) in g.edges():
g[u][v]['mssd_h'] = float(g[u][v]['mssd_h'])
g[u][v]['mssd_s'] = float(g[u][v]['mssd_s'])
g[u][v]['msds_h'] = float(g[u][v]['msds_h'])
g[u][v]['msds_s'] = float(g[u][v]['msds_s'])
g[u][v]['c_hh'] = float(g[u][v]['c_hh'])
g[u][v]['c_hs'] = float(g[u][v]['c_hs'])
g[u][v]['c_sh'] = float(g[u][v]['c_sh'])
g[u][v]['c_ss'] = float(g[u][v]['c_ss'])
sybil_belief = {}
for u in g.nodes():
sybil_belief[u] = {
'belief_s': g.node[u]['belief_s'],
'degree': len(g.node[u]['neighbours']),
'sumweight': g.node[u]['sumweights']
}
users = pd.DataFrame.from_dict(sybil_belief, orient='index')
users = users.reset_index()
users.columns = ['userid', 'belief_s', 'degree', 'sumweight']
users = users.sort(['userid'], ascending=False)
users[['userid']] = str2 + users[['userid']].astype(str)
return users, j
min_value = 0.5
# go through values and save those above threshold into expanded_feats dict
for j in xrange(len(values)):
this_target_word = target_words[j]
value = values[j]
# only continue if not in feats already
if this_target_word not in feats:
# if predicted value exceeds threshold, we continue with it
if value >= threshold:
# add to expanded feats
predicted_feats[this_target_word] = float(
"{0:.4f}".format(value))
# first-level path feats
for edge in nx.edges_iter(G, [this_target_word]):
nodea = edge[0]
nodeb = edge[1]
edge_weight = G[nodea][nodeb]["weight"]
feat_val = value * edge_weight
print "*", nodea, nodeb, edge_weight
# only continue if value is reasonable
if feat_val >= min_value:
if nodeb not in feats and nodeb not in predicted_feats:
path_feats[nodeb] = float(
"{0:.4f}".format(feat_val))
# include second-level path feats if set
if n == 2:
for edge2 in nx.edges_iter(G, [nodeb]):
def encodeUntilAccessConstraint(accessConstraint, resource, visited):
if accessConstraint[0] == 'EU':
accessConstraint1 = accessConstraint[1]
accessConstraint2 = accessConstraint[2]
accessConstraint2Encoded = encodeAccessConstraint(
accessConstraint2, resource)
accessConstraint1Encoded = encodeAccessConstraint(
accessConstraint1, resource)
successorConstraints = []
for PEP in networkx.edges_iter(conf.resourceStructure, resource):
successor = PEP[1]
if successor in visited:
continue
succVisited = set(visited)
succVisited.add(resource)
successorAccessConstraintEncoded = encodeUntilAccessConstraint(
accessConstraint, successor, succVisited)
pepTemplate = template.PEPTemplate(PEP)
successorConstraints.append(
And(pepTemplate, successorAccessConstraintEncoded))
return Or(accessConstraint2Encoded,
And(accessConstraint1Encoded, Or(successorConstraints)))
elif accessConstraint[0] == 'AU':
accessConstraint1 = accessConstraint[1]
accessConstraint2 = accessConstraint[2]
accessConstraint2Encoded = encodeAccessConstraint(
accessConstraint2, resource)
accessConstraint1Encoded = encodeAccessConstraint(
accessConstraint1, resource)
successorConstraints = []
noBackEdgesConstraints = []
existsSuccessorConstraints = []
for PEP in networkx.edges_iter(conf.resourceStructure, resource):
successor = PEP[1]
if successor in visited:
noBackEdgesConstraints.append(Not(template.PEPTemplate(PEP)))
continue
existsSuccessorConstraints.append(template.PEPTemplate(PEP))
succVisited = set(visited)
succVisited.add(resource)
successorAccessConstraintEncoded = encodeUntilAccessConstraint(
accessConstraint, successor, succVisited)
pepTemplate = template.PEPTemplate(PEP)
successorConstraints.append(
Implies(pepTemplate, successorAccessConstraintEncoded))
noBackEdges = And(noBackEdgesConstraints)
existsSuccessor = Or(existsSuccessorConstraints)
return Or(
accessConstraint2Encoded,
And(accessConstraint1Encoded, noBackEdges, existsSuccessor,
And(successorConstraints)))
else:
raise NameError('Not an until access constraint:', accessConstraint)
def thin(G, p):
R = nx.Graph(G)
removeSet = [i for i in nx.edges_iter(R) if random.random() < p]
R.remove_edges_from(removeSet)
return R
# Compile prediction rank list, by adding predicted feats in descending order of prediction value
ranked_by_pred = []
for key in sorted(predicted_feats, key=predicted_feats.get, reverse=True):
ranked_by_pred.append(key)
# Get instance feats that are in ClassiNet
instance_feats = []
for feat in feats:
for node in G.nodes_iter():
if node == feat:
instance_feats.append(feat)
# Get neighbouring nodes in graph from starting points given by instance feats and predicted feats
neighb_feats = {}
for feat in instance_feats:
for edge in nx.edges_iter(G, [feat]):
nodea = edge[0]
nodeb = edge[1]
#edge_weight = G[nodea][nodeb]
#print nodea, nodeb
if nodeb not in feats:
try:
neighb_feats[nodeb] += 1
except:
neighb_feats[nodeb] = 1
for feat in predicted_feats:
for edge in nx.edges_iter(G, [feat]):
nodea = edge[0]
nodeb = edge[1]
#edge_weight = G[nodea][nodeb]
#print nodea, nodeb
def LP(graph_file, out_file, sim_method, t, p):
G = nx.read_edgelist(graph_file, nodetype=int)
#G = G.to_undirected()
#G = nx.convert_node_labels_to_integers(G)
# for debug
# print(nx.nodes(G))
node_num = nx.number_of_nodes(G)
edge_num = nx.number_of_edges(G)
# 列出所有不存在的链接，存放到non_edge_list中
# non_edge_num = (node_num * (node_num - 1)) / 2 - edge_num
non_edge_list = [pair(u, v) for u, v in nx.non_edges(G)]
non_edge_num = len(non_edge_list)
# for debug
print("V: %d\tE: %d\tNon: %d" % (node_num, edge_num, non_edge_num))
# for debug
# print(len(non_edge_list))
# print(non_edge_list)
# 执行t次独立的实验，每次从G中选择p*100%的链接作为测试集，剩余的链接作为训练集
test_num = int(edge_num * p)
pre_num = 0
for l in range(2, 101, 2):
if l < 20:
pre_num += 1
else:
break
# end if
# end for
pre_num += 1
# for debug
print('test_edge_num: %d' % test_num)
# 定义数组存放性能值
auc_list = []
rs_list = []
time_list = []
pre_matrix = [[0 for it in range(t)] for num in range(pre_num)]
# 迭代t次进行测试
for it in range(t):
if it % 10 == 0:
print('turn: %d' % it)
# end if
# 首先产生一批随机数
seed = math.sqrt(edge_num * node_num) + math.pow(
(1 + it) * 10, 3) # 随机数种子
random.seed(seed)
rand_set = set(random.sample(range(edge_num), test_num))
# rand_set = set()
# i = 0
# while (i < test_num):
# r = random.randint(0, edge_num - 1)
# if (r not in rand_set):
# rand_set.add(r)
# i += 1
# # end if
# # end while
# for debug
# print(rand_set)
# print(len(rand_set))
# 遍历G中链接，根据rand_set中的值分成训练集和测试集
training_graph = nx.Graph()
training_graph.add_nodes_from(range(node_num))
test_edge_list = []
r = 0
for u, v in nx.edges_iter(G):
u, v = pair(u, v)
# for debug
# print(u, v)
if r in rand_set: # 测试链接
test_edge_list.append((u, v))
else:
training_graph.add_edge(u, v) # 训练网络
# end if
r += 1
# end for
training_graph.to_undirected()
# for debug
# print(len(test_edge_list))
# print(test_edge_list)
# print(nx.number_of_edges(training_graph))
# print(nx.number_of_nodes(training_graph))
# print(nx.nodes(training_graph))
# print(nx.edges(training_graph))
# 计算相似度
# if (it % 10 == 0):
# print('计算相似度')
start = datetime.datetime.now()
sim_dict = similarities(training_graph, sim_method)
end = datetime.datetime.now()
# 0. 计算时间
time_list.append((end - start).microseconds)
# 1. 计算AUC
auc_value = AUC(sim_dict, test_edge_list, non_edge_list)
auc_list.append(auc_value)
# for debug
# print(auc_value)
# 创建一个数组，存放顶点对的相似度
sim_list = [((u, v), s) for (u, v), s in sim_dict.items()]
# sim_dict不在需要
sim_dict.clear()
# 对sim_list按照相似度降序排列
sim_list.sort(key=lambda x: (x[1], x[0]), reverse=True)
# 2. 计算Ranking Score
rank_score = Ranking_score(sim_list, test_edge_list, non_edge_num)
rs_list.append(rank_score)
# for debug
# print(rank_score)
# 3. 计算精度列表
pre_list = Precision(sim_list, test_edge_list, test_num)
for num in range(pre_num):
pre_matrix[num][it] = pre_list[num]
# end for
# end for
# 计算平均值和方差，并将结果输出到文件
auc_avg, auc_std = stats(auc_list)
print('AUC: %.4f(%.4f)' % (auc_avg, auc_std))
out_file.write('%.4f(%.4f)\t' % (auc_avg, auc_std))
rs_avg, rs_std = stats(rs_list)
print('Ranking_Score: %.4f(%.4f)' % (rs_avg, rs_std))
out_file.write('%.4f(%.4f)\t' % (rs_avg, rs_std))
time_avg, time_std = stats(time_list)
print('Time: %.4f(%.4f)' % (time_avg, time_std))
out_file.write('%.4f(%.4f)\t' % (time_avg, time_std))
pre_avg_list = []
pre_std_list = []
for num in range(pre_num):
pre_avg, pre_std = stats(pre_matrix[num])
pre_avg_list.append(pre_avg)
pre_std_list.append(pre_std)
# end for
print('Precision: ')
# out_file.write('\nPrecision: ')
for num in range(pre_num):
print('%.4f(%.4f)\t' % (pre_avg_list[num], pre_std_list[num]))
out_file.write('%.4f(%.4f)\t' % (pre_avg_list[num], pre_std_list[num]))
# end for
out_file.write('%d\n' % test_num)
# Get all neighbour feats of instance feats
start_feats = set()
expanded_feats = {} # first-level neighbs
# to add only mutual, we record an array of all nodes each is a neighbour of, then at the end
# we can check it's neighb array and see if it's more than 1
counts = {}
# instance feats
for feat in feats:
for node in G.nodes_iter():
# if feat is found in the graph, then we get its neighbours and then break to next feat
if node == feat:
start_feats.add(feat)
# first level neighbours
for edge in nx.edges_iter(G, [node]):
nodeb = edge[1]
ew = G[node][nodeb]["weight"]
if nodeb not in start_feats:
# add weight to dict
expanded_feats[nodeb] = ew
# and also log node in set of neighbs
try:
n = counts[nodeb]
n.add(node)
counts[nodeb] = n
except:
counts[nodeb] = set([node])
