def test_profile_threshold_model(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
threshold = 0.2
profile = 0.1
for i in g.nodes():
config.add_node_configuration("threshold", i, threshold)
config.add_node_configuration("profile", i, profile)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
config.add_model_parameter("blocked", 0.1)
config.add_model_parameter("adopter_rate", 0.001)
threshold = 0.2
profile = 0.1
for i in g.nodes():
config.add_node_configuration("threshold", i, threshold)
config.add_node_configuration("profile", i, profile)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_profile_threshold_model(self):
for g in get_graph(True):
model = epd.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
threshold = 0.2
profile = 0.1
if isinstance(g, nx.Graph):
nodes = g.nodes
else:
nodes = g.vs['name']
for i in nodes:
config.add_node_configuration("threshold", i, threshold)
config.add_node_configuration("profile", i, profile)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
config.add_model_parameter("blocked", 0.1)
config.add_model_parameter("adopter_rate", 0.001)
threshold = 0.2
profile = 0.1
if isinstance(g, nx.Graph):
nodes = g.nodes
else:
nodes = g.vs['name']
for i in nodes:
config.add_node_configuration("threshold", i, threshold)
config.add_node_configuration("profile", i, profile)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
iterations = model.iteration_bunch(10, node_status=False)
self.assertEqual(len(iterations), 10)
def test_optional_parameters(self):
for g in get_graph(True):
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
if isinstance(g, nx.Graph):
config.add_node_set_configuration("test", {n: 1 for n in g.nodes})
config.add_edge_set_configuration("etest", {e: 1 for e in g.edges})
else:
config.add_node_set_configuration("test", {n: 1 for n in g.vs['name']})
edges = [(g.vs[e.tuple[0]]['name'], g.vs[e.tuple[1]]['name']) for e in g.es]
config.add_edge_set_configuration("etest", {e: 1 for e in edges})
self.assertEqual(len(iterations), 10)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
predefined_blocked = [0, 1, 2, 3, 4, 5]
config.add_model_initial_configuration("Blocked", predefined_blocked)
config.add_model_parameter('percentage_infected', 0.1)
model.set_initial_status(config)
iteration = model.iteration()
blocked = [x for x, v in future.utils.iteritems(iteration["status"]) if v == -1]
self.assertEqual(blocked, predefined_blocked)
model = epd.IndependentCascadesModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileModel(g)
config = mc.Configuration()
config.add_model_parameter('percentage_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
config.add_model_parameter('percentage_blocked', 0.1)
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
def test_optional_parameters(self):
g = nx.erdos_renyi_graph(1000, 0.1)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
config.add_node_set_configuration("test", {n: 1 for n in g.nodes()})
config.add_edge_set_configuration("etest", {e: 1 for e in g.edges()})
self.assertEqual(len(iterations), 10)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
predefined_blocked = [0, 1, 2, 3, 4, 5]
config.add_model_initial_configuration("Blocked", predefined_blocked)
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iteration = model.iteration()
blocked = [
x for x, v in future.utils.iteritems(iteration["status"])
if v == -1
]
self.assertEqual(blocked, predefined_blocked)
model = epd.IndependentCascadesModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ProfileThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.ThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)
model = epd.KerteszThresholdModel(g)
config = mc.Configuration()
config.add_model_parameter('adopter_rate', 0.4)
config.add_model_parameter('percentage_blocked', 0.1)
config.add_model_parameter('fraction_infected', 0.1)
model.set_initial_status(config)
iterations = model.iteration_bunch(10)
self.assertEqual(len(iterations), 10)