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from __future__ import division
import os
from shutil import copyfile
import subprocess
from save_embedded_graph27 import main_binary as embed_main
from spearmint_ghsom import main as ghsom_main
import numpy as np
import pickle
from time import time
import networkx as nx
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)
def graph_measures(gml_filename):
G = nx.read_gml(gml_filename)
density = nx.density(G)
arr = np.array([val for key, val in nx.degree_centrality(G).iteritems()])
arr = np.sort(arr)[::-1]
k = len(arr)
k1 = k
sum = np.sum(arr)
var = 1
while var > 0.95:
k -= 1
var = np.sum(arr[:k]) / sum
centrality = k / k1
assortativity = nx.degree_assortativity_coefficient(G)
connectivity = nx.node_connectivity(G)
return np.array([density, centrality, assortativity, connectivity])
#root dir
os.chdir("C:\Miniconda3\Jupyter\GHSOM_simplex_dsd")
#save directory
dir = os.path.abspath("parameter_tests_density")
#number of networks to generate
num_networks = 100
#number of times to repeat
num_repeats = 10
#number of nodes in the graph
N = 64
#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.dat"
#community labels
labels = 'firstlevelcommunity'
#mixing factors
mu = 0.3
parameter_settings = [0.5, 0.6, 0.7, 0.8, 0.9, 1]
# densities = np.zeros(num_networks)
overall_nmi_scores = np.zeros((num_networks, len(parameter_settings)))
MAX_EDGES = 2017
MIN_DENSITY = 0.05
MAX_DENSITY = 0.2
for i in range(num_networks):
#create directory
dir_string = os.path.join(dir, str(i))
if not os.path.isdir(dir_string):
os.mkdir(dir_string)
#change working directory
os.chdir(dir_string)
#make benchmark parameter file
filename = "benchmark_flags_{}.dat".format(i)
if os.path.isfile('density.txt'):
density = np.genfromtxt('density.txt')
else:
#number of edges
num_edges = np.random.randint(MAX_EDGES * MIN_DENSITY, 2017 * 0.8)
#number of communities
num_communities = np.random.randint(1, 5)
#number of nodes in micro community
minc = 1
maxc = np.random.randint(16, 30)
#average number of edges
k = float(num_edges) / N
#max number of edges
maxk = 2 * k
##calculate density
density = 2 * float(num_edges) / (N * (N-1))
with open('density.txt','w') as f:
f.write('{}\n'.format(density))
if not os.path.isfile(filename):
print 'density: {}'.format(density)
print '-N {} -k {} -maxk {} -minc {} -maxc {} -mu {}'.format(N, k, maxk, minc, maxc, mu)
with open(filename,"w") as f:
f.write("-N {} -k {} -maxk {} -minc {} -maxc {} -mu {}".format(N, k, maxk, minc, maxc, mu))
print 'written flag file: {}'.format(filename)
# if not os.path.isfile('data.pkl'):
# data = np.zeros((len(parameter_settings), 4, num_repeats))
# else:
# data = load_obj('data')
data = np.zeros((len(parameter_settings), 4, num_repeats))
for j in range(len(parameter_settings)):
#setting fo e_sg
p = parameter_settings[j]
#ghsom parameters
params = {'w': 0.0001,
'eta': 0.0001,
'sigma': 1,
'e_sg': p,
'e_en': 0.8}
#create directory
dir_string_p = os.path.join(dir_string, str(p))
if not os.path.isdir(dir_string_p):
os.mkdir(dir_string_p)
#change working directory
os.chdir(dir_string_p)
# if os.path.isfile('nmi_scores.csv') and :
# print 'already completed {}/{}, loading scores and continuing'.format(i, p)
# nmi_scores = np.genfromtxt('nmi_scores.csv', delimiter=',')
# overall_nmi_scores[i,j] = np.mean(nmi_scores, axis=0)
# continue
#record NMI scores
if not os.path.isfile('nmi_scores.pkl'):
print 'creating new nmi scores array'
nmi_scores = np.zeros(num_repeats)
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')
print
#copy executable
ex = "benchmark.exe"
if not os.path.isfile(ex):
source = "C:\\Users\\davem\\Documents\\PhD\\Benchmark Graph Generators\\binary_networks\\benchmark.exe"
copyfile(source, ex)
#copy flag file
if not os.path.isfile(filename):
source = os.path.join(dir_string, filename)
copyfile(source, filename)
print 'copied flag file {} to {}'.format(filename, os.getcwd())
#cmd strings
change_dir_cmd = "cd {}".format(dir_string_p)
generate_network_cmd = "benchmark -f {}".format(filename)
#output of cmd
output_file = open("cmd_output.out", 'w')
for r in range(1, num_repeats+1):
network_rename = "{}_{}".format(r,network)
first_level_rename = "{}_{}".format(r,first_level)
gml_filename = 'embedded_network_{}.gml'.format(r)
#generate network and rename
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()
print 'generated graph {}'.format(r)
os.rename(network, network_rename)
os.rename(first_level, first_level_rename)
print 'renamed graph {}'.format(r)
#embed into gml file
if not os.path.isfile(gml_filename):
##embed graph
embed_main(network_rename, first_level_rename, gml_filename)
print 'embedded graph {} as {} in {}'.format(r, gml_filename, os.getcwd())
##graph measures
# data[j, :, r-1] = graph_measures(gml_filename)
# print 'graph measures of network {}: {}'.format(i, data[j, :, r-1])
##score for this network
# if not np.all(nmi_scores[r-1]):
if nmi_scores[r-1] == 0:
start_time = time()
print 'starting ghsom for: {}/{}/{}'.format(i, p, gml_filename)
nmi_score, communities_detected = ghsom_main(params, gml_filename, labels, 1000)
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
#odd to overall list
overall_nmi_scores[i,j] = np.mean(nmi_scores, axis=0)
##save data
os.chdir(dir_string)
save_obj(data, 'data')
print 'DONE'
print 'OVERALL NMI SCORES'
print overall_nmi_scores
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starting ghsom for: 86/1/embedded_network_3.gml
running time of algorithm: 11.6360001564
saved nmi score for network embedded_network_3.gml: [ 0.24378803]
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starting ghsom for: 88/1/embedded_network_4.gml
running time of algorithm: 12.6510000229
saved nmi score for network embedded_network_4.gml: [ 0.89473409]
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starting ghsom for: 99/1/embedded_network_10.gml
running time of algorithm: 9.88899993896
saved nmi score for network embedded_network_10.gml: [ 0.]
writing nmi scores and running times to file
DONE
OVERALL NMI SCORES
[[ 0.54952507 0.56748932 0.62117634 0.8689669 0.90786313 0.61891829]
[ 0.49040801 0.52003907 0.55385055 0.68342899 0.75099239 0.20930235]
[ 0.74189169 0.7927954 0.91430549 0.77401103 0.51242947 0.30388959]
[ 0.86975907 0.90847255 0.94223418 0.76437275 0.49650535 0.26940284]
[ 0.59114813 0.6314837 0.6413286 0.79549181 0.89866097 0.44922438]
[ 0.77742183 0.79794609 0.81828777 0.81413612 0.77374776 0.04583205]
[ 0.56573641 0.65302357 0.66161511 0.76606045 0.85220675 0.2584614 ]
[ 0.67196792 0.70070307 0.76196421 0.78278838 0.90585361 0.54734927]
[ 0.71442567 0.73101836 0.73740175 0.83227564 0.90810815 0.17938423]
[ 0.39265984 0.41185519 0.42299292 0.44501771 0.46549164 0.21294819]
[ 0.51729484 0.52877433 0.57226428 0.68004549 0.85490593 0.34548417]
[ 0.69599862 0.73971083 0.75569862 0.88054738 0.907541 0.53787565]
[ 0.66178272 0.68701664 0.71348357 0.7359724 0.86818984 0.63783515]
[ 0.66916629 0.70154855 0.72716467 0.80823427 0.89660667 0.54878722]
[ 0.6766697 0.74272099 0.80111974 0.85289248 0.89200511 0.53935135]
[ 0.43036159 0.42974014 0.41008955 0.45686715 0.46698894 0.32971662]
[ 0.46224051 0.5239402 0.55973181 0.55407724 0.47730452 0.22830577]
[ 0.43790651 0.44167666 0.46434119 0.45471284 0.45866686 0.19605661]
[ 0.50409803 0.52188909 0.59000313 0.60940083 0.64128171 0.56599709]
[ 0.58249127 0.60712079 0.67087225 0.76685493 0.88429845 0.70463111]
[ 0.73512153 0.80380315 0.79303377 0.86058764 0.76931769 0.14101088]
[ 0.52432465 0.52685064 0.56583541 0.69664756 0.85627336 0.42335175]
[ 0.8685156 0.88487633 0.89449922 0.92202488 0.53305698 0.18598665]
[ 0.43143541 0.45036984 0.49538811 0.65719984 0.72592287 0.38357852]
[ 0.6727648 0.70089606 0.72449522 0.78389157 0.89693662 0.54845905]
[ 0.86578841 0.88629416 0.94215701 0.81481251 0.48257458 0.13512311]
[ 0.71510114 0.72506788 0.74158547 0.78940905 0.86478929 0.66856926]
[ 0.4788749 0.51388749 0.54773472 0.67065553 0.82326338 0.25808119]
[ 0.60175011 0.68068056 0.6955397 0.76101609 0.89916926 0.35968311]
[ 0.67026661 0.71764841 0.72965867 0.85628396 0.89688288 0.44804714]
[ 0.43706169 0.47310536 0.50828678 0.6094256 0.78458666 0.60977995]
[ 0.68472119 0.67462261 0.71686832 0.72909237 0.84626626 0.4092297 ]
[ 0.7558466 0.77093929 0.80673139 0.8535845 0.71086114 0.55441457]
[ 0.79408313 0.79904343 0.85742581 0.8428316 0.62844791 0.34590136]
[ 0.42912769 0.48695157 0.50954179 0.49759907 0.55888392 0.48890269]
[ 0.61534255 0.6520512 0.67686749 0.78657334 0.88408489 0.45062018]
[ 0.61220301 0.64474238 0.67866419 0.76588344 0.89397077 0.72583468]
[ 0.70427041 0.69100481 0.72923623 0.81265725 0.89599084 0.358284 ]
[ 0.65237501 0.67344766 0.67759013 0.75710735 0.89223168 0.62566046]
[ 0.72913538 0.75581506 0.76308175 0.80277497 0.90785414 0.44840138]
[ 0.64361903 0.67975856 0.71380516 0.76328574 0.89303927 0.71472595]
[ 0.66093933 0.70151879 0.73460762 0.78296666 0.8947692 0.64730623]
[ 0.46689171 0.4659103 0.50331961 0.66440175 0.68762023 0.297966 ]
[ 0.56740454 0.61620314 0.64066982 0.80461263 0.90698445 0.52779473]
[ 0.69789711 0.73175138 0.77443267 0.78267117 0.8968744 0.44861129]
[ 0.6842159 0.70155859 0.76323417 0.8116452 0.86406129 0.45815237]
[ 0.56614448 0.57753163 0.62302067 0.64928133 0.69363532 0.42002466]
[ 0.75317611 0.7136595 0.64840915 0.60801226 0.47886036 0.18344557]
[ 0.8625339 0.86324523 0.84857976 0.74212945 0.50580999 0.12299423]
[ 0.70722337 0.75093061 0.77465986 0.78292685 0.85728533 0.36928518]
[ 0.76084206 0.79685743 0.80715672 0.82722595 0.89766646 0.28980628]
[ 0.83731322 0.85010262 0.87957149 0.80641364 0.44375914 0.31934683]
[ 0.79876243 0.80649439 0.82271547 0.83520206 0.69007944 0.34115843]
[ 0.8248597 0.78721335 0.83300509 0.77654594 0.6020407 0.27704426]
[ 0.6399694 0.72260669 0.78404524 0.75524357 0.91796948 0.5486662 ]
[ 0.73435074 0.75213578 0.79013807 0.79614173 0.78898045 0.24545142]
[ 0.85685709 0.8578449 0.82091753 0.79542476 0.63863615 0.29615394]
[ 0.64136285 0.68659274 0.67674475 0.720359 0.74281426 0.42472824]
[ 0.8084302 0.81164128 0.85075465 0.80842885 0.59248826 0.2541637 ]
[ 0.80267374 0.7492153 0.70632592 0.65755461 0.47720788 0.24925608]
[ 0.84590507 0.84649262 0.87274696 0.88658659 0.71091225 0.14746158]
[ 0.76976603 0.78235066 0.75965799 0.7498199 0.59059672 0.2916559 ]
[ 0.60038 0.61252167 0.63584 0.65715981 0.74072097 0.42108186]
[ 0.62638449 0.6530487 0.7319694 0.71705946 0.88892576 0.2801512 ]
[ 0.61641814 0.67391002 0.71501202 0.72437325 0.81318462 0.44912298]
[ 0.60791793 0.63956886 0.71967724 0.7022293 0.89563493 0.44790805]
[ 0.8181593 0.80948791 0.8485548 0.85773862 0.75089196 0.34042436]
[ 0.59856681 0.63139099 0.71782715 0.69740854 0.87191473 0.37839586]
[ 0.72637538 0.74250726 0.77264405 0.77401104 0.80115311 0.31120285]
[ 0.77670652 0.74522905 0.70089113 0.57646704 0.47504081 0.17607441]
[ 0.79174193 0.7555183 0.70000471 0.63272893 0.47746115 0.14575981]
[ 0.8053393 0.7499825 0.73614648 0.64512311 0.48459734 0.09722981]
[ 0.82625864 0.85067941 0.79787576 0.67037047 0.49684078 0.25735227]
[ 0.66688711 0.66666619 0.75028918 0.73468054 0.72370009 0.47098527]
[ 0.59908463 0.57598744 0.51710369 0.45597234 0.37104299 0.1679981 ]
[ 0.7711235 0.76912794 0.68395538 0.56620825 0.45498247 0.20605832]
[ 0.73409469 0.72750921 0.69103044 0.58755294 0.47810317 0.24788601]
[ 0.76725277 0.79915449 0.80136854 0.82909858 0.74453964 0.33343368]
[ 0.69782823 0.67502995 0.65677351 0.59980177 0.40928058 0.10149589]
[ 0.65011018 0.61048631 0.72456986 0.70863219 0.87596625 0.60786757]
[ 0.61088287 0.57505597 0.53767897 0.48511584 0.35918304 0.11345659]
[ 0.58917786 0.605467 0.7250835 0.70696132 0.91650798 0.26839953]
[ 0.69831313 0.6162117 0.61035977 0.4793488 0.3452575 0.13722418]
[ 0.81486034 0.80294798 0.7418346 0.6811155 0.4451345 0.13145958]
[ 0.77144422 0.77953852 0.80482346 0.8117083 0.67209845 0.41409052]
[ 0.81174832 0.79939028 0.80285814 0.69078983 0.55825099 0.16391824]
[ 0.58982492 0.54904596 0.53509968 0.4945495 0.36657169 0.11542566]
[ 0.8288504 0.8221208 0.8578931 0.7863148 0.51187383 0.12900125]
[ 0.66945223 0.67920425 0.70096102 0.71667352 0.78389768 0.52914696]
[ 0.71434567 0.70863833 0.76450924 0.76982958 0.82680669 0.38021429]
[ 0.78990647 0.8146741 0.82189723 0.8168652 0.6989619 0.20465515]
[ 0.68857028 0.67125901 0.65039418 0.55529524 0.41534553 0.21349388]
[ 0.83280633 0.84535158 0.8656412 0.832823 0.6544874 0.12947269]
[ 0.7805793 0.79853232 0.81739112 0.82611417 0.71671765 0.17729986]
[ 0.69434991 0.67756319 0.63619565 0.5451263 0.39579527 0.15423876]
[ 0.76373613 0.76655354 0.82576943 0.84426464 0.64264467 0.21106216]
[ 0.7559443 0.77042208 0.81552976 0.82898259 0.79994685 0.34616979]
[ 0.79544854 0.77184386 0.73012865 0.66283832 0.54984685 0.09519321]
[ 0.71889643 0.66853765 0.65314659 0.59153232 0.38862889 0.22591419]
[ 0.61416811 0.66571283 0.72491661 0.72400041 0.89553419 0.54820093]]
In [17]:
data = np.zeros((num_networks, 4))
best_settings = np.zeros(num_networks)
for i in range(len(overall_nmi_scores)):
scores = overall_nmi_scores[i]
idx = np.argsort(scores)[::-1]
best_settings[i] = parameter_settings[idx[0]]
os.chdir('C:\Miniconda3\Jupyter\GHSOM_simplex_dsd\parameter_tests_density\{}\{}'.format(i, best_settings[i]))
measures = np.zeros((num_repeats, 4))
for r in range(num_repeats):
gml = 'embedded_network_{}.gml'.format(r+1)
measures[r] = graph_measures(gml)
data[i] = np.mean(measures, axis=0)
print 'data={}'.format(data[i])
# print densities[i]
# print best_settings[i]
data=[ 0.1577381 0.90625 -0.14080981 5.7 ]
data=[ 0.075 0.9125 -0.06205974 2.9 ]
data=[ 0.12688492 0.9109375 -0.14340794 5. ]
data=[ 0.18159722 0.9078125 -0.20372251 7. ]
data=[ 0.12668651 0.9109375 -0.06859236 4.7 ]
data=[ 0.17802579 0.9078125 -0.16647651 6.8 ]
data=[ 0.12633929 0.9109375 -0.0606638 4.9 ]
data=[ 0.20917659 0.90625 -0.1398512 7.6 ]
data=[ 0.1749504 0.9140625 -0.13161544 7. ]
data=[ 0.04915675 0.9171875 -0.02349667 1.7 ]
data=[ 0.10059524 0.9125 -0.05323686 4. ]
data=[ 0.14910714 0.9140625 -0.11013808 5.8 ]
data=[ 0.21200397 0.90625 -0.12518589 7.7 ]
data=[ 0.18397817 0.90625 -0.17237283 6.8 ]
data=[ 0.15277778 0.909375 -0.12212552 5.9 ]
data=[ 0.04915675 0.9171875 -0.05022903 1.6 ]
data=[ 0.0437004 0.921875 -0.0669984 1.5 ]
data=[ 0.0437004 0.921875 -0.02737399 1.7 ]
data=[ 0.06889881 0.9203125 -0.03867557 2.8 ]
data=[ 0.1297123 0.909375 -0.12558291 4.8 ]
data=[ 0.18060516 0.9078125 -0.13328277 6.9 ]
data=[ 0.1030754 0.909375 -0.08411983 3.6 ]
data=[ 0.18769841 0.90625 -0.18277893 7. ]
data=[ 0.0719246 0.915625 -0.03287666 2.6 ]
data=[ 0.20892857 0.90625 -0.15655046 7.5 ]
data=[ 0.18784722 0.90625 -0.07179581 7. ]
data=[ 0.21463294 0.90625 -0.13646402 7.7 ]
data=[ 0.10034722 0.9109375 -0.05672818 3.7 ]
data=[ 0.12366071 0.915625 -0.07777758 4.9 ]
data=[ 0.15223214 0.9078125 -0.10447013 5.9 ]
data=[ 0.07207341 0.9171875 -0.0298948 2.9 ]
data=[ 0.21215278 0.90625 -0.1375781 7.4 ]
data=[ 0.21274802 0.90625 -0.12180549 7.8 ]
data=[ 0.13258929 0.90625 -0.15305189 4.9 ]
data=[ 0.0437004 0.921875 -0.04133827 1.5 ]
data=[ 0.20302579 0.909375 -0.14663542 7.6 ]
data=[ 0.20525794 0.90625 -0.16923193 7.6 ]
data=[ 0.2093254 0.90625 -0.15477288 7.4 ]
data=[ 0.23581349 0.90625 -0.09938762 8.4 ]
data=[ 0.20972222 0.90625 -0.16281232 7.8 ]
data=[ 0.29077381 0.90625 -0.04759838 10.3 ]
data=[ 0.2312996 0.9078125 -0.12512121 8.6 ]
data=[ 0.07777778 0.9078125 -0.05700868 3. ]
data=[ 0.12594246 0.9109375 -0.09827548 4.7 ]
data=[ 0.28462302 0.9078125 -0.13154085 10.5 ]
data=[ 0.23784722 0.90625 -0.14933069 8.5 ]
data=[ 0.0750496 0.9140625 -0.03613751 2.8 ]
data=[ 0.10362103 0.909375 0.17922952 2.3 ]
data=[ 0.18154762 0.9078125 -0.04515814 6.9 ]
data=[ 0.25798611 0.9078125 -0.10510886 9.7 ]
data=[ 0.26949405 0.90625 -0.18622398 9.7 ]
data=[ 0.18467262 0.90625 -0.01075829 6.9 ]
data=[ 0.26185516 0.90625 -0.05688427 9.9 ]
data=[ 0.2109623 0.90625 0.04574404 8. ]
data=[ 0.37713294 0.9109375 -0.13048315 14.3 ]
data=[ 0.31026786 0.9078125 -0.0445929 11.9 ]
data=[ 0.2046627 0.909375 -0.02733698 8. ]
data=[ 0.28467262 0.9078125 -0.11060863 10.5 ]
data=[ 0.21051587 0.90625 -0.04447615 8. ]
data=[ 0.12361111 0.915625 0.1011259 5. ]
data=[ 0.23660714 0.90625 0.05042981 9. ]
data=[ 0.18154762 0.9078125 0.00981767 7. ]
data=[ 0.28501984 0.90625 -0.12098013 10.3 ]
data=[ 0.28318452 0.9078125 -0.0871737 10.9 ]
data=[ 0.33988095 0.90625 -0.19125458 12.3 ]
data=[ 0.3750496 0.9109375 -0.12145297 14.7 ]
data=[ 0.23650794 0.90625 -0.07546205 9. ]
data=[ 0.33328373 0.909375 -0.10597067 12.3 ]
data=[ 0.26170635 0.9078125 -0.09666098 9.5 ]
data=[ 0.13000992 0.9078125 0.0440029 5. ]
data=[ 0.12678571 0.9109375 0.07177607 5. ]
data=[ 0.15297619 0.909375 0.10798277 6. ]
data=[ 0.16185516 0.90625 0.02650372 6. ]
data=[ 0.28864087 0.90625 -0.07852581 10.6 ]
data=[ 0.07202381 0.91875 0.044311 2.9 ]
data=[ 0.1062004 0.90625 0.11925376 3.8 ]
data=[ 0.12678571 0.909375 0.10321317 4.9 ]
data=[ 0.28998016 0.9078125 -0.06676234 10.6 ]
data=[ 0.12683532 0.9109375 0.01839583 5. ]
data=[ 0.35401786 0.9109375 -0.07634657 13.6 ]
data=[ 0.07772817 0.909375 -0.01026146 2.9 ]
data=[ 0.35491071 0.9109375 -0.13573923 13.4 ]
data=[ 0.09791667 0.9171875 0.02145155 4. ]
data=[ 0.15297619 0.909375 0.10603514 6. ]
data=[ 0.28685516 0.9078125 -0.05271674 10.6 ]
data=[ 0.18501984 0.90625 -0.0133152 6.9 ]
data=[ 0.07197421 0.91875 -0.04122683 2.8 ]
data=[ 2.10615079e-01 9.06250000e-01 -7.83400279e-03 7.90000000e+00]
data=[ 0.30828373 0.9078125 -0.09316521 11.4 ]
data=[ 0.31205357 0.90625 -0.04147372 11.6 ]
data=[ 0.2391369 0.90625 -0.03231725 8.9 ]
data=[ 0.09454365 0.91875 0.0361221 3.9 ]
data=[ 0.2078373 0.9078125 0.06683772 8. ]
data=[ 0.26121032 0.9078125 -0.01568238 9.9 ]
data=[ 0.09791667 0.9171875 0.02145155 4. ]
data=[ 0.2421131 0.90625 -0.06022925 9. ]
data=[ 0.26418651 0.90625 -0.043744 10. ]
data=[ 0.15297619 0.909375 0.04801643 6. ]
data=[ 0.10034722 0.9109375 0.10093667 3.8 ]
data=[ 0.32698413 0.9109375 -0.13074014 12.1 ]
In [18]:
os.chdir('C:\Miniconda3\Jupyter\GHSOM_simplex_dsd\parameter_tests_density')
save_obj(data, 'data')
for i in range(num_networks):
print data[i]
print best_settings[i]
print
[ 0.1577381 0.90625 -0.14080981 5.7 ]
0.9
[ 0.075 0.9125 -0.06205974 2.9 ]
0.9
[ 0.12688492 0.9109375 -0.14340794 5. ]
0.7
[ 0.18159722 0.9078125 -0.20372251 7. ]
0.7
[ 0.12668651 0.9109375 -0.06859236 4.7 ]
0.9
[ 0.17802579 0.9078125 -0.16647651 6.8 ]
0.7
[ 0.12633929 0.9109375 -0.0606638 4.9 ]
0.9
[ 0.20917659 0.90625 -0.1398512 7.6 ]
0.9
[ 0.1749504 0.9140625 -0.13161544 7. ]
0.9
[ 0.04915675 0.9171875 -0.02349667 1.7 ]
0.9
[ 0.10059524 0.9125 -0.05323686 4. ]
0.9
[ 0.14910714 0.9140625 -0.11013808 5.8 ]
0.9
[ 0.21200397 0.90625 -0.12518589 7.7 ]
0.9
[ 0.18397817 0.90625 -0.17237283 6.8 ]
0.9
[ 0.15277778 0.909375 -0.12212552 5.9 ]
0.9
[ 0.04915675 0.9171875 -0.05022903 1.6 ]
0.9
[ 0.0437004 0.921875 -0.0669984 1.5 ]
0.7
[ 0.0437004 0.921875 -0.02737399 1.7 ]
0.7
[ 0.06889881 0.9203125 -0.03867557 2.8 ]
0.9
[ 0.1297123 0.909375 -0.12558291 4.8 ]
0.9
[ 0.18060516 0.9078125 -0.13328277 6.9 ]
0.8
[ 0.1030754 0.909375 -0.08411983 3.6 ]
0.9
[ 0.18769841 0.90625 -0.18277893 7. ]
0.8
[ 0.0719246 0.915625 -0.03287666 2.6 ]
0.9
[ 0.20892857 0.90625 -0.15655046 7.5 ]
0.9
[ 0.18784722 0.90625 -0.07179581 7. ]
0.7
[ 0.21463294 0.90625 -0.13646402 7.7 ]
0.9
[ 0.10034722 0.9109375 -0.05672818 3.7 ]
0.9
[ 0.12366071 0.915625 -0.07777758 4.9 ]
0.9
[ 0.15223214 0.9078125 -0.10447013 5.9 ]
0.9
[ 0.07207341 0.9171875 -0.0298948 2.9 ]
0.9
[ 0.21215278 0.90625 -0.1375781 7.4 ]
0.9
[ 0.21274802 0.90625 -0.12180549 7.8 ]
0.8
[ 0.13258929 0.90625 -0.15305189 4.9 ]
0.7
[ 0.0437004 0.921875 -0.04133827 1.5 ]
0.9
[ 0.20302579 0.909375 -0.14663542 7.6 ]
0.9
[ 0.20525794 0.90625 -0.16923193 7.6 ]
0.9
[ 0.2093254 0.90625 -0.15477288 7.4 ]
0.9
[ 0.23581349 0.90625 -0.09938762 8.4 ]
0.9
[ 0.20972222 0.90625 -0.16281232 7.8 ]
0.9
[ 0.29077381 0.90625 -0.04759838 10.3 ]
0.9
[ 0.2312996 0.9078125 -0.12512121 8.6 ]
0.9
[ 0.07777778 0.9078125 -0.05700868 3. ]
0.9
[ 0.12594246 0.9109375 -0.09827548 4.7 ]
0.9
[ 0.28462302 0.9078125 -0.13154085 10.5 ]
0.9
[ 0.23784722 0.90625 -0.14933069 8.5 ]
0.9
[ 0.0750496 0.9140625 -0.03613751 2.8 ]
0.9
[ 0.10362103 0.909375 0.17922952 2.3 ]
0.5
[ 0.18154762 0.9078125 -0.04515814 6.9 ]
0.6
[ 0.25798611 0.9078125 -0.10510886 9.7 ]
0.9
[ 0.26949405 0.90625 -0.18622398 9.7 ]
0.9
[ 0.18467262 0.90625 -0.01075829 6.9 ]
0.7
[ 0.26185516 0.90625 -0.05688427 9.9 ]
0.8
[ 0.2109623 0.90625 0.04574404 8. ]
0.7
[ 0.37713294 0.9109375 -0.13048315 14.3 ]
0.9
[ 0.31026786 0.9078125 -0.0445929 11.9 ]
0.8
[ 0.2046627 0.909375 -0.02733698 8. ]
0.6
[ 0.28467262 0.9078125 -0.11060863 10.5 ]
0.9
[ 0.21051587 0.90625 -0.04447615 8. ]
0.7
[ 0.12361111 0.915625 0.1011259 5. ]
0.5
[ 0.23660714 0.90625 0.05042981 9. ]
0.8
[ 0.18154762 0.9078125 0.00981767 7. ]
0.6
[ 0.28501984 0.90625 -0.12098013 10.3 ]
0.9
[ 0.28318452 0.9078125 -0.0871737 10.9 ]
0.9
[ 0.33988095 0.90625 -0.19125458 12.3 ]
0.9
[ 0.3750496 0.9109375 -0.12145297 14.7 ]
0.9
[ 0.23650794 0.90625 -0.07546205 9. ]
0.8
[ 0.33328373 0.909375 -0.10597067 12.3 ]
0.9
[ 0.26170635 0.9078125 -0.09666098 9.5 ]
0.9
[ 0.13000992 0.9078125 0.0440029 5. ]
0.5
[ 0.12678571 0.9109375 0.07177607 5. ]
0.5
[ 0.15297619 0.909375 0.10798277 6. ]
0.5
[ 0.16185516 0.90625 0.02650372 6. ]
0.6
[ 0.28864087 0.90625 -0.07852581 10.6 ]
0.7
[ 0.07202381 0.91875 0.044311 2.9 ]
0.5
[ 0.1062004 0.90625 0.11925376 3.8 ]
0.5
[ 0.12678571 0.909375 0.10321317 4.9 ]
0.5
[ 0.28998016 0.9078125 -0.06676234 10.6 ]
0.8
[ 0.12683532 0.9109375 0.01839583 5. ]
0.5
[ 0.35401786 0.9109375 -0.07634657 13.6 ]
0.9
[ 0.07772817 0.909375 -0.01026146 2.9 ]
0.5
[ 0.35491071 0.9109375 -0.13573923 13.4 ]
0.9
[ 0.09791667 0.9171875 0.02145155 4. ]
0.5
[ 0.15297619 0.909375 0.10603514 6. ]
0.5
[ 0.28685516 0.9078125 -0.05271674 10.6 ]
0.8
[ 0.18501984 0.90625 -0.0133152 6.9 ]
0.5
[ 0.07197421 0.91875 -0.04122683 2.8 ]
0.5
[ 2.10615079e-01 9.06250000e-01 -7.83400279e-03 7.90000000e+00]
0.7
[ 0.30828373 0.9078125 -0.09316521 11.4 ]
0.9
[ 0.31205357 0.90625 -0.04147372 11.6 ]
0.9
[ 0.2391369 0.90625 -0.03231725 8.9 ]
0.7
[ 0.09454365 0.91875 0.0361221 3.9 ]
0.5
[ 0.2078373 0.9078125 0.06683772 8. ]
0.7
[ 0.26121032 0.9078125 -0.01568238 9.9 ]
0.8
[ 0.09791667 0.9171875 0.02145155 4. ]
0.5
[ 0.2421131 0.90625 -0.06022925 9. ]
0.8
[ 0.26418651 0.90625 -0.043744 10. ]
0.8
[ 0.15297619 0.909375 0.04801643 6. ]
0.5
[ 0.10034722 0.9109375 0.10093667 3.8 ]
0.5
[ 0.32698413 0.9109375 -0.13074014 12.1 ]
0.9
In [21]:
import matplotlib.pyplot as plt
plt.plot(data[:,2], best_settings, 'x')
plt.xlabel('density')
plt.ylabel('best setting')
plt.show()
In [14]:
os.chdir("C:\\Miniconda3\\Jupyter\\GHSOM_simplex_dsd\\parameter_tests_density\\0\\1")
nmi_scores = load_obj('nmi_scores')
print nmi_scores
[ 0.00000000e+00 8.93502361e-01 8.94734085e-01 -2.77555756e-06
8.93502361e-01 8.94734085e-01 0.00000000e+00 0.00000000e+00
-2.77555756e-06 -2.77555756e-06]
In [ ]:
Content source: DavidMcDonald1993/ghsom
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