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 test_visualize_prevalence(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = sir.SIRModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter('gamma', 0.01)
config.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(config)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
# Visualization
viz = DiffusionPrevalence(model, trends)
viz.plot("prev.pdf")
os.remove("prev.pdf")
def test_visualize_prevalence(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.SIRModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter('gamma', 0.01)
config.add_model_parameter("percentage_infected", 0.05)
model.set_initial_status(config)
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)
# Visualization
viz = DiffusionPrevalence(model, trends)
viz.plot("prev.pdf")
os.remove("prev.pdf")
def diffusion_trend(ind):
iterations, iterations_mitigations = evaluate.evaluate_individual(
toolbox.compile(ind),
m=model,
traveler_set=travelers,
mvc_set=minimal_vertex_cover,
vert_avg_dist=vertex_average_distance,
number_vertex_shortest=number_shortest_paths,
Page_Rank=page_rank,
Cluster_Coeff=cluster_coef,
avg_degree=average_degree,
short_dist=shortest_distances,
avg_dist=average_distance,
total_iterations=ITERATIONS,
measure_every=MEASURE_EVERY,
mitigations_per_measure=MITIGATIONS_PER_MEASURE,
rollover=ROLLOVER,
use_all=USE_ALL)
trends = model.build_trends(iterations)
# Visualization
viz = DiffusionTrend(model, trends)
viz.plot()
viz = DiffusionPrevalence(model, trends)
viz.plot()
return iterations, trends
def test_visualize_dynamic(self):
dg = dn.DynGraph()
for t in past.builtins.xrange(0, 4):
g = nx.erdos_renyi_graph(200, 0.05)
dg.add_interactions_from(g.edges(), t)
model = dsi.DynSIModel(dg)
config = mc.Configuration()
config.add_model_parameter('beta', 0.1)
config.add_model_parameter("percentage_infected", 0.1)
model.set_initial_status(config)
iterations = model.execute_snapshots()
trends = model.build_trends(iterations)
# Visualization
viz = DiffusionPrevalence(model, trends)
viz.plot("prevd.pdf")
os.remove("prevd.pdf")
def test_visualize_dynamic(self):
dg = dn.DynGraph()
for t in past.builtins.xrange(0, 4):
g = nx.erdos_renyi_graph(200, 0.05)
dg.add_interactions_from(g.edges(), t)
model = dyn.DynSIModel(dg)
config = mc.Configuration()
config.add_model_parameter('beta', 0.1)
config.add_model_parameter("percentage_infected", 0.1)
model.set_initial_status(config)
iterations = model.execute_snapshots()
trends = model.build_trends(iterations)
# Visualization
viz = DiffusionPrevalence(model, trends)
viz.plot("prevd.pdf")
os.remove("prevd.pdf")
def simulate_spread(G, steps, threshold, infected_probability, simulation_beta,
simulation_gamma, simulation_alpha, output):
infected_nodes = []
exposed_nodes = [
node[0] for node in G.nodes(data=True) if node[1]['flaws'] > threshold
]
# Take 1 at random and the rest with probability
idx = np.random.choice(list(range(len(exposed_nodes))),
size=1,
replace=False)[0]
infected_nodes.append(exposed_nodes.pop(idx))
infected_nodes_count = int(len(exposed_nodes) * infected_probability)
if infected_nodes_count != 0:
infected_idxs = np.random.choice(list(range(len(exposed_nodes))),
infected_nodes_count)
infected_nodes.extend(list(np.take(exposed_nodes, infected_idxs)))
exposed_nodes = list(np.delete(exposed_nodes, infected_idxs))
susceptible_nodes = [
node for node_idx, node in enumerate(G.nodes())
if node_idx not in [*exposed_nodes, *infected_nodes]
]
# Create model
model = ep.SEIRModel(G)
# Create configuration
cfg = mc.Configuration()
# Infection probability.
cfg.add_model_parameter('beta', simulation_beta)
# Infected -> Remove probability
cfg.add_model_parameter('gamma', simulation_gamma)
# Latent period
cfg.add_model_parameter('alpha', simulation_alpha)
# Set both infected and exposed nodes.
# cfg.add_model_parameter('fraction_infected', 0.01)
cfg.add_model_initial_configuration("Susceptible", susceptible_nodes)
cfg.add_model_initial_configuration("Exposed", exposed_nodes)
cfg.add_model_initial_configuration("Infected", infected_nodes)
# Set the configuration
model.set_initial_status(cfg)
# Run multiple models
trends = multi_runs(model,
execution_number=10,
iteration_number=steps,
infection_sets=None,
nprocesses=5)
dt = DiffusionTrend(model, trends)
dt.plot(filename=os.path.join(output, 'diffusion_trend.png'))
dp = DiffusionPrevalence(model, trends)
dp.plot(filename=os.path.join(output, 'diffusion_prevalence.png'))
# Other run to show
infected_nodes = list()
for _ in range(10):
model.reset()
iterations = model.iteration_bunch(steps)
for iteration in iterations:
for k, v in iteration['status'].items():
if v == 3:
infected_nodes.append(k)
return Counter(infected_nodes)
import matplotlib.pyplot as plt
import networkx as nx
import json
import warnings
n = 271269 # n nodes
p = 0.007286
w = [p * n for i in range(n)] # w = p*n for all nodes
g = expected_degree_graph(w) # configuration model
largest_subgraph1 = max(nx.connected_component_subgraphs(g), key=len)
pos = nx.spring_layout(largest_subgraph1, k=0.05)
nx.draw(largest_subgraph1, pos=pos, cmap=plt.cm.PiYG, edge_color="black", linewidths=0.3, node_size=60, alpha=0.6, with_labels=False)
nx.draw_networkx(largest_subgraph1, pos=pos)
plt.savefig('graphfinal2.png')
model = ep.SIRModel(largest_subgraph1)
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.05) # infection rate
cfg.add_model_parameter('gamma', 0.05) # recovery rate
cfg.add_model_parameter("percentage_infected", 0.01)
model.set_initial_status(cfg)
iterations = model.iteration_bunch(200, node_status=True)
trends = model.build_trends(iterations)
viz = DiffusionTrend(model, trends)
viz.plot()
viz = DiffusionPrevalence(model, trends)
viz.plot()