def test_karate_club(self):
pyl = PyLouvain.from_file("data/karate.txt")
partition, q = pyl.apply_method()
q_ = q * 10000
self.assertEqual(4, len(partition))
self.assertEqual(4298, math.floor(q_))
self.assertEqual(4299, math.ceil(q_))
def test_karate_club(self):
pyl = PyLouvain.from_file("data/karate.txt")
partition, q = pyl.apply_method()
q_ = q * 10000
self.assertEqual(4, len(partition))
self.assertEqual(4298, math.floor(q_))
self.assertEqual(4299, math.ceil(q_))
def test_karate_club():
nodes, edges = PyLouvain.from_file("data/karate.txt")
pyl = PyLouvain(nodes, edges)
partition, q = pyl.apply_method(gamma=1.0)
odds = bayes_model_selection(nodes, edges, partition)
print(partition, q, odds)
def test(graphname, gnc=None):
nodes, edges = PyLouvain.from_file("data/%s.txt" % graphname)
pyl = PyLouvain(nodes, edges)
name_pickle = 'fig/save_%s_%d.p' % (graphname, len(nodes))
if not os.path.isfile(name_pickle):
print("pickle file", name_pickle, "is missing. Recompute.")
start = time.time()
partition, q = pyl.apply_method()
print("Modularity Time", time.time() - start)
start = time.time()
partition2 = multiscale(nodes, edges, 0.5)
print("Multiscale Time", time.time() - start)
results = {"LV": partition, "MS": partition2}
sizes_distri = {
"Modularity": [len(p) for p in partition],
"MultiScale": [len(p) for p in partition2]
}
pickle.dump(results, open(name_pickle, 'wb'))
print("Pickle save", name_pickle)
else:
print("pickle file", name_pickle, "is found.")
results = pickle.load(open(name_pickle, "rb"))
sizes_distri = {
"Modularity": [len(p) for p in results["LV"]],
"MultiScale": [len(p) for p in results["MS"]]
}
if gnc:
gnc_fp = open(gnc, "r+")
gnc_map = {}
sizes_distri["Ground Truth"] = []
for i, line in enumerate(gnc_fp):
x = line.split()
sizes_distri["Ground Truth"].append(len(x))
for j in x:
gnc_map[int(j)] = i
gnc_list = [gnc_map[k] for k in nodes]
lv_map = {v: i for i, c in enumerate(partition) for v in c}
lv_list = [lv_map[k] for k in nodes]
ms_map = {v: i for i, c in enumerate(partition2) for v in c}
ms_list = [ms_map[k] for k in nodes]
print("Louvain NMI=", normalized_mutual_info_score(lv_list, gnc_list))
print("Multi-scale NMI=",
normalized_mutual_info_score(ms_list, gnc_list))
hist(sizes_distri, graphname)
def do(path, source_data):
start_time = time.time()
print 'start time: ',start_time
print 'louvain算法社团划分开始...'
pyl = PyLouvain.from_file(path, source_data)
partition, q = pyl.apply_method()
# print partition
out_file = open(path+"community_result.txt", 'w')
#读取节点信息文件
nodes_file = open(path+'nodes_tmp.txt', 'r')
nodes_lines = nodes_file.readlines()
nodes_file.close()
nodes = {} #保存 节点序号-节点名称 信息
for line in nodes_lines:
n = line.split()
if not n:
break
nodes[n[1]] = n[0]
#社团信息 格式：标记 成员数
print '统计社团信息 写入社团状态文件'
community_status = open(path+'community_status.txt', 'w')
i = 1
label = {} # 格式 节点名称-社团标记
for community in partition:
community_status.write(str(i)+'\t'+str(len(community))+'\n') #社团标记 成员数
#成员贴上社团标记
for per in community:
label[nodes[str(per)]] = str(i)
i += 1
community_status.close()
#关联用户互动
print '关联用户互动数据 写入结果文件'
relationship_file = open(path+source_data, 'r')
relationship_lines = relationship_file.readlines()
relationship_file.close()
for rela in relationship_lines:
r = rela.split()
if not r:
break
out_file.write('-\t'+r[0]+'\t'+label[r[0]]+'\t'+r[1]+'\t'+label[r[1]]+'\t'+r[2]+'\n')
out_file.close()
print 'end time: ',time.time()
print '花费时间: ',(time.time()-start_time)/60,' min'
def test_football2():
for gamma in np.linspace(0.4, 0.9, num=10):
print()
print("gamma=", gamma)
nodes, edges = PyLouvain.from_file("data/football.txt")
partition = multiscale(nodes, edges, gamma)
# load GNC ground truth by txt file (as defined by conference)
fconf = open("data/football.gnc.txt", "r")
gnc = {str(i): int(line.strip()) for i, line in enumerate(fconf)}
order_ = {i: stri for i, stri in enumerate(sorted(gnc.keys()))}
comm = {n: i for i, ns in enumerate(partition) for n in ns}
a = [comm[i] for i in nodes]
b = [gnc[order_[i]] for i in nodes]
print("NMI=", metrics.adjusted_mutual_info_score(a, b))
#test_football2()
def test_football():
# load GNC ground truth by txt file (as defined by conference)
fconf = open("data/football.gnc.txt", "r")
gnc = {str(i): int(line.strip()) for i, line in enumerate(fconf)}
order_ = {i: stri for i, stri in enumerate(sorted(gnc.keys()))}
x, y, z, r = [], [], [], []
for gamma in np.linspace(0.5, 8.5, num=35):
nodes, edges = PyLouvain.from_file("data/football.txt")
pyl = PyLouvain(nodes, edges)
partition, q = pyl.apply_method(gamma)
odds = bayes_model_selection(nodes, edges, partition)
print(len(partition), odds)
x.append(gamma)
y.append(odds)
z.append(len(partition))
comm = {n: i for i, ns in enumerate(partition) for n in ns}
a = [comm[i] for i in nodes]
b = [gnc[order_[i]] for i in nodes]
#print("NMI=", metrics.adjusted_mutual_info_score(a, b))
r.append(metrics.adjusted_mutual_info_score(a, b))
#r.append(metrics.adjusted_rand_score(a, b))
plt.plot(x, y, 'r-*', markersize=10)
ax1 = plt.gca()
ax1.tick_params(axis='x', labelsize=18)
ax1.tick_params(axis='y', labelcolor='r', labelsize=15)
ax2 = ax1.twinx()
ax2.plot(x, z, 'm-^', markersize=10)
ax2.tick_params(axis='y', labelcolor='m', labelsize=15)
plt.tight_layout()
plt.savefig("fig/football2.png")
def test_citations(self):
pyl = PyLouvain.from_file("data/hep-th-citations")
partition, q = pyl.apply_method()
def test_arxiv(self):
pyl = PyLouvain.from_file("data/arxiv.txt")
partition, q = pyl.apply_method()
def test_arxiv(self):
pyl = PyLouvain.from_file("data/facebook_combined.txt", 0.5)
partition, q = pyl.apply_method()
print(len(partition), q)
from pylouvain import PyLouvain
import math
from matplotlib import pyplot as plt
import networkx as nx
filepath = 'out.txt'
# 获取社区划分
pyl = PyLouvain.from_file(filepath)
node_dict = pyl.node_dict # key是253916-2的形式，value是编号的形式
reverse_node_dict = dict(zip(node_dict.values(),
node_dict.keys())) # key是编号的形式，value是253916-2的形式
partition, q = pyl.apply_method()
print(partition)
print("模块度：", q)
# 给各个社区节点分配颜色
community_num = len(partition)
print('community_num:', community_num)
color_board = ['red', 'green', 'blue', 'pink', 'orange', 'purple', 'scarlet']
color = {}
for index in range(community_num):
print("社区" + str(index + 1) + ":" + str(len(partition[index])))
for node_id in partition[index]:
color[node_id] = color_board[
index] # color为一个字典，key为编号形式的节点，value为所属社区的颜色
new_color_dict = sorted(color.items(), key=lambda d: d[0],
reverse=False) # 将color字典按照key的大小排序，并返回一个list
node_list = [reverse_node_dict[item[0]]
for item in new_color_dict] #存储编号从小到大顺序对应的253916-2的形式的节点
color_list = [item[1] for item in new_color_dict] #存储node_list中对应的节点颜色
def test_citations():
nodes, edges = PyLouvain.from_file("data/hep-th-citations")
pyl = PyLouvain(nodes, edges)
partition, q = pyl.apply_method()
print(partition, q)
import matplotlib
matplotlib.use("Agg")
from matplotlib import pyplot as plt
import pickle
import time
import numpy as np
from sklearn import metrics
from pylouvain import PyLouvain
from run import multiscale, bayes_model_selection
x, y, z = [], [], []
for gamma in np.linspace(0.2, 0.9, num=20):
print("gamma=", gamma)
pyl = PyLouvain.from_file("data/football.txt")
partition = multiscale(pyl.nodes, pyl.edges, gamma)
# load GNC ground truth by txt file (as defined by conference)
fconf = open("data/football.gnc.txt", "r")
gnc = {str(i): int(line.strip()) for i, line in enumerate(fconf)}
order_ = {i: stri for i, stri in enumerate(sorted(gnc.keys()))}
comm = {n: i for i, ns in enumerate(partition) for n in ns}
a = [comm[i] for i in pyl.nodes]
b = [gnc[order_[i]] for i in pyl.nodes]
x.append(gamma)
y.append(len(partition))
z.append(metrics.adjusted_mutual_info_score(a, b))
print("#comm=", len(partition), "NMI=",
metrics.adjusted_mutual_info_score(a, b))
import matplotlib
from matplotlib import pyplot as plt
from sklearn import metrics
from pylouvain import PyLouvain
from sklearn import metrics
import numpy as np
nodes, edges = PyLouvain.from_file("data/karate.txt")
gamma0 = 0.78
#nodes, edges = PyLouvain.from_gml_file("data/lesmis.gml")
#nodes, edges = PyLouvain.from_gml_file("data/polbooks.gml")
def cmap(nodes, partition):
m = {n: i for i, _ in enumerate(partition) for n in _}
return [m[i] for i in nodes]
def test_small_networks(nodes, edges, gamma0):
pyl = PyLouvain(nodes, edges)
partition0, q0 = pyl.apply_method(gamma0)
c0 = cmap(nodes, partition0)
NMI = []
gamma_list = np.linspace(0.2, 3.5, num=200)
for gamma in gamma_list:
partition, q = PyLouvain(nodes, edges).apply_method(gamma)
c = cmap(nodes, partition)
NMI.append(metrics.normalized_mutual_info_score(c0, c))
def test_arxiv(self):
pyl = PyLouvain.from_file("data/arxiv.txt")
partition, q = pyl.apply_method()
def test_test(self):
pyl = PyLouvain.from_file("data/year1990.txt")
partition, q = pyl.apply_method()
return partition
def test_citations(self):
pyl = PyLouvain.from_file("data/hep-th-citations")
partition, q = pyl.apply_method()
