def test_trend_comparison(self):
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = sir.SIRModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter('gamma', 0.02)
cfg.add_model_parameter("percentage_infected", 0.01)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
model1 = si.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter("percentage_infected", 0.01)
model1.set_initial_status(cfg)
iterations = model1.iteration_bunch(200)
trends1 = model1.build_trends(iterations)
viz = DiffusionTrendComparison([model, model1], [trends, trends1])
viz.plot("trend_comparison.pdf")
os.remove("trend_comparison.pdf")
def test_trend_comparison(self):
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = epd.SIRModel(g)
# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter('gamma', 0.02)
cfg.add_model_parameter("percentage_infected", 0.01)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
model1 = epd.SIModel(g)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter("percentage_infected", 0.01)
model1.set_initial_status(cfg)
iterations = model1.iteration_bunch(200)
trends1 = model1.build_trends(iterations)
viz = DiffusionTrendComparison([model, model1], [trends, trends1])
viz.plot("trend_comparison.pdf")
os.remove("trend_comparison.pdf")
def sir_model(g, er_g, ba_g, g_stat):
print '\nSIR MODEL SIMULATION WITH GAMMA ' + g_stat.upper() + ' THAN BETA'
print 'MODEL CONFIGURATION'
beta = float(raw_input('INFECTION PROBABILITY: '))
gamma = float(raw_input('REMOVAL PROBABILITY: '))
if g_stat == 'smaller':
while gamma >= beta:
print 'ERROR! GAMMA HAS TO BE SMALLER THAN BETA!'
gamma = float(raw_input('REMOVAL PROBABILITY: '))
else:
while gamma <= beta:
print 'ERROR! GAMMA HAS TO BE GREATER THAN BETA!'
gamma = float(raw_input('REMOVAL PROBABILITY: '))
cfg = mc.Configuration()
cfg.add_model_parameter('beta', beta)
cfg.add_model_parameter('gamma', gamma)
cfg.add_model_parameter('percentage_infected',
float(raw_input('PERCENTAGE INFECTED: ')))
for_comparison = dict()
for network in [g, er_g, ba_g]:
model_sir = sir.SIRModel(network)
model_sir.set_initial_status(cfg)
iterations = model_sir.iteration_bunch(200)
trends_sir = model_sir.build_trends(iterations)
if network is g:
viz = DiffusionTrend(model_sir, trends_sir)
viz.plot("../../report/images/spreading/sir/diffusion_"
+ g_stat + ".pdf")
for_comparison['original_sir'] = [model_sir, trends_sir]
elif network is er_g:
viz = DiffusionTrend(model_sir, trends_sir)
viz.plot("../../report/images/spreading/sir/diffusion_er_"
+ g_stat + ".pdf")
for_comparison['er_sir'] = [model_sir, trends_sir]
else:
viz = DiffusionTrend(model_sir, trends_sir)
viz.plot("../../report/images/spreading/sir/diffusion_ba_"
+ g_stat + ".pdf")
for_comparison['ba_sir'] = [model_sir, trends_sir]
viz = DiffusionTrendComparison([
for_comparison['original_sir'][0],
for_comparison['er_sir'][0],
for_comparison['ba_sir'][0]
],
[
for_comparison['original_sir'][1],
for_comparison['er_sir'][1],
for_comparison['ba_sir'][1]
])
viz.plot("../../report/images/spreading/sir/trend_comparison_"
+ g_stat + ".pdf")
def si_model(g, er_g, ba_g):
print '\nSI MODEL SIMULATION'
print 'MODEL CONFIGURATION'
cfg = mc.Configuration()
beta = float(raw_input('INFECTION RATE: '))
cfg.add_model_parameter('beta', beta )
cfg.add_model_parameter('percentage_infected',
float(raw_input('PERCENTAGE INFECTED: ')))
for_comparison = dict()
for network in [g, er_g, ba_g]:
model_si = si.SIModel(network)
model_si.set_initial_status(cfg)
iterations = model_si.iteration_bunch(200)
trends_si = model_si.build_trends(iterations)
if network is g:
print 'Original graph'
model_si.name = 'G'
viz = DiffusionTrend(model_si, trends_si)
viz.plot('../../report/images/spreading/si/diffusion.pdf')
viz = DiffusionPrevalence(model_si, trends_si)
viz.plot('../../report/images/spreading/si/prevalence.pdf')
for_comparison['original_si'] = [model_si, trends_si]
elif network is er_g:
print 'ER graph'
model_si.name = 'ER'
viz = DiffusionTrend(model_si, trends_si)
viz.plot('../../report/images/spreading/si/diffusion_er.pdf')
viz = DiffusionPrevalence(model_si, trends_si)
viz.plot('../../report/images/spreading/si/prevalence_er.pdf')
for_comparison['er_si'] = [model_si, trends_si]
else:
print 'BA graph'
model_si.name = 'BA'
viz = DiffusionTrend(model_si, trends_si)
viz.plot('../../report/images/spreading/si/diffusion_ba.pdf')
viz = DiffusionPrevalence(model_si, trends_si)
viz.plot('../../report/images/spreading/si/prevalence_ba.pdf')
for_comparison['ba_si'] = [model_si, trends_si]
viz = DiffusionTrendComparison([
for_comparison['original_si'][0],
for_comparison['er_si'][0],
for_comparison['ba_si'][0]
],
[
for_comparison['original_si'][1],
for_comparison['er_si'][1],
for_comparison['ba_si'][1]
])
viz.plot("../../report/images/spreading/si/trend_comparison_beta{}.pdf".format(beta))
def sis_model(net, graph_name):
print '\nSIS MODEL SIMULATION - ' + graph_name + ' network'
for_comparison = dict()
degrees = [d for n, d in net.degree]
mean_k = sum(degrees)/len(degrees)
for state in ['ENDEMIC', 'FREE']:
print 'MODEL CONFIGURATION - ' + state + ' STATE'
if state == 'ENDEMIC':
beta = float(raw_input('INFECTION PROBABILITY: '))
print 'MU HAS TO BE LESS THAN ' + str(beta*(mean_k+1))
mu = float(raw_input('RECOVERY PROBABILITY: '))
while mu >= (beta*(mean_k+1)):
print 'ERROR! MU HAS TO BE LESS THAN ' + str(beta*(mean_k+1))
mu = float(raw_input('RECOVERY PROBABILITY: '))
else:
print 'MU HAS TO BE GREATER THAN ' + str(beta*(mean_k+1))
mu = float(raw_input('RECOVERY PROBABILITY: '))
while mu <= beta*(mean_k+1):
print 'ERROR! MU IS NOT GREATER THAN ' + str(beta*(mean_k+1))
mu = float(raw_input('RECOVERY PROBABILITY: '))
cfg = mc.Configuration()
cfg.add_model_parameter('beta', beta)
cfg.add_model_parameter('lambda', mu)
cfg.add_model_parameter('percentage_infected',
float(raw_input('PERCENTAGE INFECTED: ')))
model_sis = sis.SISModel(net)
model_sis.set_initial_status(cfg)
model_sis.name = str(graph_name)+"-"+state.lower()
print model_sis.name
iterations = model_sis.iteration_bunch(200)
trends_sis = model_sis.build_trends(iterations)
viz = DiffusionTrend(model_sis, trends_sis)
viz.plot('../../report/images/spreading/sis/diffusion_' +
graph_name + '_' + state.lower() + '.pdf')
for_comparison[state.lower() + '_state'] = [model_sis, trends_sis]
viz = DiffusionTrendComparison([for_comparison['endemic_state'][0],
for_comparison['free_state'][0]],
[for_comparison['endemic_state'][1],
for_comparison['free_state'][1]])
viz.plot('../../report/images/spreading/sis/diffusion_' + graph_name +
'_comparison.pdf')
def thr_model(g, er_g, ba_g):
print '\nTHRESHOLD MODEL SIMULATION'
print 'MODEL CONFIGURATION'
cfg = mc.Configuration()
cfg.add_model_parameter('percentage_infected',
float(raw_input('PERCENTAGE INFECTED: ')))
thr = float(raw_input('THRESHOLD: '))
for_comparison = dict()
count = 0
labels = ['G', 'ER', 'BA']
for network in [g, er_g, ba_g]:
model_thr = threshold.ThresholdModel(network)
model_thr.name = labels[count]
print model_thr.name
count+=1
for i in network.nodes():
cfg.add_node_configuration("threshold", i, thr)
model_thr.set_initial_status(cfg)
iterations = model_thr.iteration_bunch(100)
trends_thr = model_thr.build_trends(iterations)
if network is g:
viz = DiffusionTrend(model_thr, trends_thr)
viz.plot("../../report/images/spreading/threshold/diffusion.pdf")
for_comparison['original_thr'] = [model_thr, trends_thr]
elif network is er_g:
viz = DiffusionTrend(model_thr, trends_thr)
viz.plot(
"../../report/images/spreading/threshold/diffusion_er.pdf")
for_comparison['er_thr'] = [model_thr, trends_thr]
else:
viz = DiffusionTrend(model_thr, trends_thr)
viz.plot(
"../../report/images/spreading/threshold/diffusion_ba.pdf")
for_comparison['ba_thr'] = [model_thr, trends_thr]
viz = DiffusionTrendComparison([for_comparison['original_thr'][0],
for_comparison['er_thr'][0],
for_comparison['ba_thr'][0]], [
for_comparison['original_thr'][1],
for_comparison['er_thr'][1],
for_comparison['ba_thr'][1]])
viz.plot("../../report/images/spreading/threshold/trend_comparison.pdf")
cfg.add_model_parameter('beta', b)
infected_nodes3 = [37]
cfg.add_model_initial_configuration("Infected", infected_nodes3)
model2.set_initial_status(cfg)
# 3° Simulation execution
iterations = model2.iteration_bunch(it)
trends3 = model2.build_trends(iterations)
#trends3 = multi_runs(model2, execution_number=ex, iteration_number=it, nprocesses=4)
#--------------------------------------------------------------
# In[2]:
from ndlib.viz.mpl.TrendComparison import DiffusionTrendComparison
viz = DiffusionTrendComparison([model, model1, model2], [trends1, trends2,trends3], statuses=['Infected'])
viz.plot(percentile=90)
#viz.plot("img1.png",percentile=90)
# In[3]:
from ndlib.viz.mpl.PrevalenceComparison import DiffusionPrevalenceComparison
viz = DiffusionPrevalenceComparison([model, model1, model2], [trends1, trends2, trends3], statuses=['Infected'])
viz.plot(percentile=90)
#viz.plot("img2.png",percentile=90)
nx.barabasi_albert_graph(1000, 8, seed=0),
nx.newman_watts_strogatz_graph(1000, k=4, p=0.12, seed=0),
nx.fast_gnp_random_graph(n=1000, p=0.20)
]
model_list = []
iteration_list = []
trend_list = []
for graph in G_list:
model = ep.SIRModel(graph)
model_list.append(model)
config = mc.Configuration()
config.add_model_parameter('beta', 0.1)
config.add_model_parameter('gamma', 0.1)
config.add_model_parameter('fraction_infected', 0.01)
model.set_initial_status(config)
for i in range(3):
iterations = model_list[i].iteration_bunch(100)
iteration_list.append(iterations)
trends = model.build_trends(iterations)
trend_list.append(trends)
visual = DiffusionTrendComparison(
[model_list[0], model_list[1], model_list[2]],
[trend_list[0], trend_list[1], trend_list[2]],
statuses=["Susceptible", "Infected", "Removed"])
plt.title(" BA vs SW vs Random")
result = visual.plot()