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import os
from shutil import copyfile
import subprocess
from save_embedded_graph27 import main_hierarchical as embed_main
from spearmint_ghsom import main as ghsom_main
import numpy as np
import pickle
from time import time
def save_obj(obj, name):
with open(name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(name):
with open(name + '.pkl', 'rb') as f:
return pickle.load(f)
#root dir
os.chdir("C:\Miniconda3\Jupyter\GHSOM_simplex_dsd")
#save directory
dir = os.path.abspath("synthetic_benchmarks")
#number of times to repeat
num_repeats = 100
#number of micro communities
k1 = 16
#number of macro communities
k2 = 4
#number of nodes in micro communitiy
s1 = 32
#number of nodes in macro community = s1 * s2
s2 = 4
#number of nodes in the network
N = s1 * s2 * k2
#number of links to same micro community
z1 = 16
#number of links to same macro community
z2 = 16
#nuber of nodes in micro community
minc = s1
maxc = s1
#number of nodes in macro community
minC = s1 * s2
maxC = s1 * s2
#make save directory
if not os.path.isdir(dir):
os.mkdir(dir)
#change to dir
os.chdir(dir)
#network file names -- output of network generator
network = "network.dat"
first_level = "community_first_level.dat"
second_level = "community_second_level.dat"
#community labels
labels = 'firstlevelcommunity,secondlevelcommunity'
#ghsom parameters
params = {'w': 0.0001,
'eta': 0.0001,
'sigma': 1,
'e_sg': 0.8,
'e_en': 0.8}
mixing_factors = [16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36]
overall_nmi_scores = np.zeros((len(mixing_factors), num_repeats, 2))
for i in range(len(mixing_factors)):
z3 = mixing_factors[i]
#node degree
k = z1 + z2 + z3
maxk = k
#mixing factors
mu1 = float(z3) / k
mu2 = float(z2) / k
#create directory
dir_string = os.path.join(dir, str(z3))
if not os.path.isdir(dir_string):
os.mkdir(dir_string)
#change working directory
os.chdir(dir_string)
if os.path.isfile('nmi_scores.csv'):
print 'already completed {}, loading nmi scores and continuing'.format(z3)
nmi_scores = np.genfromtxt('nmi_scores.csv', delimiter=',')
overall_nmi_scores[i] = nmi_scores
continue
#copy executable
ex = "hbenchmark.exe"
if not os.path.isfile(ex):
source = "C:\Users\davem\Documents\PhD\Benchmark Graph Generators\hierarchical_bench2_2\hbenchmark.exe"
copyfile(source, ex)
#make benchmark parameter file
filename = "benchmark_flags_{}.dat".format(z3)
if not os.path.isfile(filename):
with open(filename,"w") as f:
f.write("-N {} -k {} -maxk {} -minc {} -maxc {} -minC {} -maxC {} -mu1 {} -mu2 {}".format(N, k, maxk, minc, maxc, minC, maxC, mu1, mu2))
#cmd strings
change_dir_cmd = "cd {}".format(dir_string)
generate_network_cmd = "hbenchmark -f {}".format(filename)
#output of cmd
output_file = open("cmd_output.out", 'w')
#record NMI scores
if not os.path.isfile('nmi_scores.pkl'):
print 'creating new nmi scores array'
nmi_scores = np.zeros((num_repeats, len(labels.split(','))))
else:
print 'loading nmi score progress'
nmi_scores = load_obj('nmi_scores')
#record running times
if not os.path.isfile('running_times.pkl'):
print 'creating new running time array'
running_times = np.zeros(num_repeats)
else:
print 'loading running time progress'
running_times = load_obj('running_times')
#generate networks
for r in range(1, num_repeats+1):
network_rename = "{}_{}".format(r,network)
first_level_rename = "{}_{}".format(r,first_level)
second_level_rename = "{}_{}".format(r,second_level)
gml_filename = 'embedded_network_{}.gml'.format(r)
if not os.path.isfile(network_rename):
process = subprocess.Popen(change_dir_cmd + " && " + generate_network_cmd,
stdout=output_file,
stderr=output_file,
shell=True)
process.wait()
os.rename(network, network_rename)
os.rename(first_level, first_level_rename)
os.rename(second_level, second_level_rename)
if not os.path.isfile(gml_filename):
##embed graph
embed_main(network_rename, first_level_rename, second_level_rename)
print 'created {} in {}'.format(gml_filename, os.getcwd())
##score for this network
if not np.all(nmi_scores[r-1]):
start_time = time()
print 'starting ghsom for: {}/{}'.format(z3, gml_filename)
nmi_score, communities_detected = ghsom_main(params, gml_filename, labels, 10000)
nmi_scores[r-1] = nmi_score
running_time = time() - start_time
print 'running time of algorithm: {}'.format(running_time)
running_times[r-1] = running_time
#save
save_obj(nmi_scores, 'nmi_scores')
save_obj(running_times, 'running_times')
print 'saved nmi score for network {}: {}'.format(gml_filename, nmi_score)
print
##output nmi scores to csv file
print 'writing nmi scores and running times to file'
np.savetxt('nmi_scores.csv',nmi_scores,delimiter=',')
np.savetxt('running_times.csv',running_times,delimiter=',')
print 'DONE'
print 'OVERALL NMI SCORES'
print overall_nmi_scores
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import matplotlib.pyplot as plt
import numpy as np
os.chdir("C:\Miniconda3\Jupyter\GHSOM_simplex_dsd")
first_level = np.genfromtxt('first_level.csv',delimiter=',')
first_level = first_level[1:]
first_level[:,0] = np.rint(first_level[:,0])
first_level_m = np.zeros((len(first_level) / 3, 4))
for i in range(len(first_level) / 3):
first_level_m[i] = np.mean(first_level[3*i:3*i+2], axis=0)
first_level_m[first_level_m > 1] = 1
second_level = np.genfromtxt('second_level.csv',delimiter=',')
second_level = second_level[1:]
second_level[:,0] = np.rint(second_level[:,0])
second_level_m = np.zeros((len(second_level) / 3, 4))
for i in range(len(second_level) / 3):
second_level_m[i] = np.mean(second_level[3*i:3*i+2], axis=0)
second_level_m[second_level_m > 1] = 1
means = np.zeros((len(mixing_factors), 2))
ses = np.zeros((len(mixing_factors), 2))
for i in range(len(mixing_factors)):
# for score in overall_nmi_scores:
score = overall_nmi_scores[i]
m = np.mean(score, axis=0)
means[i] = m
print m
sd = np.std(score, axis=0)
se = sd / np.sqrt(num_repeats)
ses[i] = se
print se
print
plt.errorbar(mixing_factors, means[:, 0], yerr=ses[:, 0], fmt='-o')
plt.errorbar(mixing_factors, means[:, 1], yerr=ses[:, 1], fmt='-o')
plt.axis([16, 36, 0.95, 1])
plt.legend(['First level','Second level'], loc=3)
plt.xlabel('Mixing parameter $z_3$')
plt.ylabel('Normalized mutual information')
plt.show()
##first level
plt.errorbar(mixing_factors, means[:, 0], yerr=ses[:, 0], fmt='-o')
plt.plot(mixing_factors, first_level_m[:, 1], '-rx')
plt.plot(mixing_factors, first_level_m[:, 2], '-gx')
plt.plot(mixing_factors, first_level_m[:, 3], '-bx')
plt.axis([16, 36, 0.4, 1])
plt.legend(['FM', 'FUC', 'PMC', 'GHSOM'], loc=3)
plt.xlabel('Mixing parameter $z_3$')
plt.ylabel('Normalized mutual information')
plt.title('First level')
# plt.title('mixing factor vs. NMI score for both levels of community')
plt.show()
#second level
plt.errorbar(mixing_factors, means[:, 1], yerr=ses[:, 0], fmt='-o')
plt.plot(mixing_factors, second_level_m[:, 1], '-rx')
plt.plot(mixing_factors, second_level_m[:, 2], '-gx')
plt.plot(mixing_factors, second_level_m[:, 3], '-bx')
plt.axis([16, 36, 0.4, 1])
plt.legend(['FM', 'FUC', 'PMC', 'GHSOM'], loc=3)
plt.xlabel('Mixing parameter $z_3$')
plt.ylabel('Normalized mutual information')
plt.title('Second level')
plt.show()
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import numpy as np
first_level = np.genfromtxt('first_level.csv',delimiter=',')
first_level = first_level[1:]
first_level[:,0] = np.rint(first_level[:,0])
print first_level
first_level_m = np.zeros((len(first_level) / 3, 4))
for i in range(len(first_level) / 3):
first_level_m[i] = np.mean(first_level[3*i:3*i+2], axis=0)
first_level_m[first_level_m > 1] = 1
print first_level_m
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for score in overall_nmi_scores:
m = np.mean(score, axis=0)
print m
sd = np.std(score, axis=0)
se = sd / np.sqrt(100)
print se
print
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os.chdir("C:\Miniconda3\Jupyter\GHSOM_simplex_dsd")
gml_filename = 'embedded_network_69.gml'
nmi_scores, num_communities = ghsom_main(params, gml_filename, labels, 10000)
print nmi_scores
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