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ETN.py
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ETN.py
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from copy import deepcopy
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
import itertools
import json
import os
def store_etns(S,file_name,gap,k,label):
if label:
name="gap_"+str(gap)+"_k_"+str(k)+"_LABEL.json"
else:
name="gap_"+str(gap)+"_k_"+str(k)+".json"
directory = "res/"+file_name+"/ETNS/"
if not os.path.exists(directory):
os.makedirs(directory)
a_file = open(directory+name, "w")
json.dump(S, a_file,indent=1)
a_file.close()
print("file stored in: \t"+directory+name)
def load_etns(file_name,gap,k,label):
directory = "res/"+file_name+"/ETNS/"
if label:
name="gap_"+str(gap)+"_k_"+str(k)+"_LABEL.json"
else:
name="gap_"+str(gap)+"_k_"+str(k)+".json"
with open(directory+name) as json_file:
S = json.load(json_file)
return S
def count_ETN(graphs,k,meta=None):
S = dict()
for i in range(len(graphs)-k + 1):
for v in graphs[i].nodes():
etn = build_ETN(graphs[i:i+k+1],v)
if not etn == None:
etns = get_ETNS(etn,meta)
if etns in S.keys():
S[etns] = S[etns] + 1
else:
S[etns] = 1
return(S)
def get_node_encoding_labeled(meta,node_encoding,ego):
categories = np.sort(list(np.unique(list(meta.values())))+["0"])
meta_binary = list(itertools.product([0, 1], repeat=round(len(categories)**(1/2)+0.5)))
meta_dict = dict()
for i in range(len(categories)):
meta_dict[categories[i]] = list(meta_binary[i])
new_node_encoding = dict()
for i in node_encoding:
tmp = []
for v in node_encoding[i]:
if(v==0):
tmp.extend(meta_dict["0"])
else:
tmp.extend(meta_dict[meta[int(i)]])
new_node_encoding[i]=tmp
ego_encoding = meta_dict[meta[ego]]
return(new_node_encoding,ego_encoding)
def from_ETNS_to_ETN(s,k,meta=None):
n = k + 1
if not(meta==None):
categories = np.sort(list(np.unique(list(meta.values())))+["0"])
meta_binary = list(itertools.product([0, 1], repeat=round(len(categories)**(1/2)+0.5)))
meta_dict = dict()
for i in range(len(categories)):
value = "".join(str(e) for e in meta_binary[i])
meta_dict[value] = categories[i]
s = s[2:] # update s
ego_encoding = s[:len(meta_binary[0])]
s = s[len(meta_binary[0]):] # update s
node_encoding = [s[i:i+len(meta_binary[0])] for i in range(0,len(s),len(meta_binary[0]))]
new_node_encoding = []
for i in node_encoding:
tmp = []
for j in range(0, len(i), len(meta_binary[0])):
value = i[j:j+len(meta_binary[0])]
tmp.append(meta_dict[value])
new_node_encoding.append(tmp)
node_encoding_labels = new_node_encoding
else:
node_encoding = [s[2:][i:i+1] for i in range(0, len(s[2:]))]
egos = [("0*_"+str(i),"0*_"+str(i+1)) for i in range(n-1)]
ETN = nx.Graph()
ETN.add_edges_from(egos)
# add ego labels
if not(meta == None):
for nod in list(ETN.nodes()):
ETN.nodes()[nod]["label"] = meta_dict[ego_encoding]
new_node_encoding = []
for j in node_encoding:
if("1" in j):
new_node_encoding.append(1)
else:
new_node_encoding.append(0)
node_encoding = new_node_encoding
for i in range(0,len(node_encoding),n):
same_person = []
for j in range(n):
if not(node_encoding[i+j] == 0):
ETN.add_edge("0*_"+(str(j)),str(i+1)+"_"+str(j))
same_person.append(str(i+1)+"_"+str(j))
if not (meta == None):
ETN.nodes()[str(i+1)+"_"+str(j)]["label"] = node_encoding_labels[i+j][0]
if len(same_person) > 1:
for k in range(len(same_person)-1):
ETN.add_edge(same_person[k],same_person[k+1])
return(ETN)
def get_ETNS(ETN,meta=None):
nodes = list(ETN.nodes())
nodes_no_ego = []
ids_no_ego = []
lenght_ETNS = 0
for n in nodes:
if not ("*" in n):
nodes_no_ego.append(n)
if not(n.split("_")[0] in ids_no_ego):
ids_no_ego.append(n.split("_")[0])
else:
ego = int(n.split("*")[0])
lenght_ETNS = lenght_ETNS + 1
node_encoding = get_node_encoding(ids_no_ego,nodes_no_ego,lenght_ETNS)
if not(meta == None):
node_encoding,ego_encoding = get_node_encoding_labeled(meta,node_encoding,ego)
for k in node_encoding.keys():
node_encoding[k] = '0b'+''.join(str(e) for e in node_encoding[k])
binary_node_encodings = list(node_encoding.values())
binary_node_encodings.sort()
ETNS = '0b'+''.join(e[2:] for e in binary_node_encodings)
# add ego label encoding
if not (meta == None):
ETNS = '0b'+''.join(str(e) for e in ego_encoding)+ETNS[2:]
return(ETNS)
def get_node_encoding(ids_no_ego,nodes_no_ego,lenght_ETNS):
node_encoding = dict()
for n in ids_no_ego:
enc = []
for k in range(lenght_ETNS):
if (str(n)+"_"+str(k) in nodes_no_ego):
enc.append(1)
else:
enc.append(0)
node_encoding[n]=enc
return(node_encoding)
def get_egocentric_neighborhood(g,v):
return([str(v)+"*"]+list(g.neighbors(v)))
def build_ETN(graphs,v):
if len(list(graphs[0].neighbors(v))) > 0 :
en_list = []
en_ids = []
for i in graphs:
en_list.append(get_egocentric_neighborhood(i,v))
en_ids.append(get_egocentric_neighborhood(i,v))
# add temporal step to en_list
for i in range(len(en_list)):
for j in range(len(en_list[i])):
en_list[i][j] = str(en_list[i][j])+"_"+str(i)
#buld graphs en
en_graph = []
for en in en_list:
g = nx.Graph()
for i in range(len(en)-1):
g.add_edge(en[0],en[i+1])
en_graph.append(g)
# compose an to get disconnected ETN
ETN = nx.Graph()
for g in en_graph:
ETN = nx.compose(ETN,g)
# merge en
en_list_long = []
for en in en_list:
en_list_long_tmp = []
for n in en:
en_list_long_tmp.append(n.split("_")[0])
en_list_long.append(en_list_long_tmp)
for k in range(len(en_list_long)-1):
for n in en_list_long[k]:
for en in range(len(en_list_long[k+1:])):
add = False
if (n in en_list_long[k+en+1]):
add = True
t = k + 1 + en
break
if (add == True):
u = str(n)+"_"+str(k)
v = str(n)+"_"+str(t)
ETN.add_edge(u,v)
return(ETN)
else:
return(None)
def draw_ETN(ETN,multiple=False):
ids,k = get_ids_and_k(ETN)
pos = dict()
id_ego = []
for t in range(k+1):
for i in ids:
if "*" in i:
id_ego.append(str(i)+"_"+str(t))
pos[str(i)+"_"+str(t)] = [t,int(i[0])]
else:
pos[str(i)+"_"+str(t)] = [t,int(i)]
node_label = dict()
nodes_data = dict(ETN.nodes(data=True))
for i in list(ETN.nodes()):
if not(nodes_data[i] == {}):
node_label[i] = nodes_data[i]["label"]
if (node_label == {}):
nx.draw(ETN,pos=pos,node_size=100,alpha=0.9,with_labels=False)
nx.draw_networkx_nodes(ETN, pos, nodelist=id_ego, node_size=300, node_color='red',alpha=0.5)
else:
nx.draw(ETN,pos=pos,node_size=100,alpha=0.5)
nx.draw_networkx_nodes(ETN, pos, nodelist=id_ego, node_size=300, node_color='red',alpha=0.5)
nx.draw_networkx_labels(ETN, pos, labels=node_label, font_size=12)
if not multiple:
plt.show()
# get unique ids (no time consideration)
def get_ids_and_k(ETN):
nodes = list(ETN.nodes())
ids = []
k = 0
for n in nodes:
id_n = (n.split("_")[0])
k_tmp = (n.split("_")[1])
if not (id_n in ids):
ids.append(id_n)
if int(k_tmp) > k:
k = int(k_tmp)
return(ids,k)