def __init__(self, graph, seed=None):
"""
Model Constructor
:param graph: A networkx graph object
"""
np.random.seed(seed)
self.discrete_state = True
self.params = {'nodes': {}, 'edges': {}, 'model': {}, 'status': {}}
self.available_statuses = {
"Susceptible": 0,
"Infected": 1,
"Recovered": 2,
"Blocked": -1
}
self.name = ""
self.parameters = {"model": {}, "nodes": {}, "edges": {}}
self.actual_iteration = 0
self.graph = AGraph(graph)
self.status = {n: 0 for n in self.graph.nodes}
self.initial_status = {}
def __init__(self, graph, seed=None):
"""
Model Constructor
:param graph: A networkx graph object
"""
np.random.seed(seed)
self.discrete_state = True
self.params = {
'nodes': {},
'edges': {
"threshold_1": {
"descr": "Infection rate for contagion 1.",
"range": [0, 1],
"optional": False
},
"threshold_2": {
"descr": "Infection rate for contagion 2.",
"range": [0, 1],
"optional": False
},
"infection_1_given_2": {
"descr":
"Infection rate for contagion 1 given infected with contagion 2.",
"range": [0, 1],
"optional": False
},
"infection_2_given_1": {
"descr":
"Infection rate for contagion 2 given infected with contagion 1.",
"range": [0, 1],
"optional": False
},
},
'model': {},
'status': {}
}
self.available_statuses = {}
cnt = 0
for i in range(3):
for j in range(3):
self.available_statuses[(i, j)] = cnt
cnt += 1
self.name = "multiple_IC"
self.actual_iteration = 0
self.graph = AGraph(graph)
self.status = {n: 0 for n in self.graph.nodes}
self.initial_status = {}
def test_has_edge(self):
g = nx.karate_club_graph()
ag = AGraph(g)
n1 = ag.has_edge(0, 1)
g = from_nx_to_igraph(g)
ag = AGraph(g)
n2 = ag.has_edge(0, 1)
self.assertEqual(n1, n2)
def test_neighbors(self):
g = nx.karate_club_graph()
ag = AGraph(g)
n1 = ag.neighbors(1)
g = from_nx_to_igraph(g)
ag = AGraph(g)
n2 = ag.neighbors(1)
self.assertListEqual(n1, n2)
def test_number_of_edges(self):
g = nx.karate_club_graph()
ag = AGraph(g)
n1 = ag.number_of_edges()
g = from_nx_to_igraph(g)
ag = AGraph(g)
n2 = ag.number_of_edges()
self.assertEqual(n1, n2)
def test_successors(self):
g = nx.karate_club_graph()
g1 = nx.to_directed(g)
ag = AGraph(g1)
n1 = ag.successors(1)
g = from_nx_to_igraph(g1, directed=True)
ag = AGraph(g)
n2 = ag.successors(1)
self.assertListEqual(n1, n2)
class DiffusionModel(object):
"""
Partial Abstract Class that defines Diffusion Models
"""
# __metaclass__ = abc.ABCMeta
def __init__(self, graph, seed=None):
"""
Model Constructor
:param graph: A networkx graph object
"""
np.random.seed(seed)
self.discrete_state = True
self.params = {
'nodes': {},
'edges': {},
'model': {},
'status': {}
}
self.available_statuses = {
"Susceptible": 0,
"Infected": 1,
"Recovered": 2, #remove
"Blocked": -1 #remove
}
self.name = ""
self.parameters = {
"model": {},
"nodes": {},
"edges": {}
}
self.actual_iteration = 0
self.graph = AGraph(graph)
self.status = {n: 0 for n in self.graph.nodes}
self.attempt = {n: 0 for n in self.graph.nodes}
self.initial_status = {}
self.stop = False
def __validate_configuration(self, configuration):
"""
Validate the consistency of a Configuration object for the specific model
:param configuration: a Configuration object instance
"""
if "Infected" not in self.available_statuses:
raise ConfigurationException("'Infected' status not defined.")
# Checking mandatory parameters
omp = set([k for k in list(self.parameters['model'].keys()) if not self.parameters['model'][k]['optional']])
onp = set([k for k in list(self.parameters['nodes'].keys()) if not self.parameters['nodes'][k]['optional']])
oep = set([k for k in list(self.parameters['edges'].keys()) if not self.parameters['edges'][k]['optional']])
# print(onp)
# print(oep)
mdp = set(configuration.get_model_parameters().keys())
ndp = set(configuration.get_nodes_configuration().keys())
edp = set(configuration.get_edges_configuration().keys())
# print(ndp) #somehow in second influencer selection onwards the param attempt comes up even tho i reset the model and config
# print(edp)
if len(omp) > 0:
if len(omp & mdp) != len(omp):
raise ConfigurationException({"message": "Missing mandatory model parameter(s)", "parameters": omp-mdp})
if len(onp) > 0:
if len(onp & ndp) != len(onp):
raise ConfigurationException({"message": "Missing mandatory node parameter(s)", "parameters": onp-ndp})
if len(oep) > 0:
if len(oep & edp) != len(oep):
raise ConfigurationException({"message": "Missing mandatory edge parameter(s)", "parameters": oep-edp})
# Checking optional parameters
omp = set([k for k in list(self.parameters['model'].keys()) if self.parameters['model'][k]['optional']])
onp = set([k for k in list(self.parameters['nodes'].keys()) if self.parameters['nodes'][k]['optional']])
oep = set([k for k in list(self.parameters['edges'].keys()) if self.parameters['edges'][k]['optional']])
# print(onp)
# print(oep)
if len(omp) > 0:
for param in omp:
if param not in mdp:
configuration.add_model_parameter(param, self.parameters['model'][param]['default'])
if len(onp) > 0:
for param in onp:
# print(param)
# print(ndp)
if param not in ndp:
for nid in self.graph.nodes:
configuration.add_node_configuration(param, nid, self.parameters['nodes'][param]['default'])
# print(nid)
# print(self.parameters['nodes'][param]['default'])
# print('node done')
if len(oep) > 0:
for param in oep:
if param not in edp:
for eid in self.graph.edges:
configuration.add_edge_configuration(param, eid, self.parameters['edges'][param]['default'])
# print(eid)
# print(self.parameters['edges'][param]['default'])
# print('edge done')
# Checking initial simulation status
sts = set(configuration.get_model_configuration().keys())
if self.discrete_state and "Infected" not in sts and "fraction_infected" not in mdp \
and "percentage_infected" not in mdp:
warnings.warn('Initial infection missing: a random sample of 5% of graph nodes will be set as infected')
self.params['model']["fraction_infected"] = 0.05
def set_initial_status(self, configuration):
"""
Set the initial model configuration
:param configuration: a ```ndlib.models.ModelConfig.Configuration``` object
"""
self.__validate_configuration(configuration)
nodes_cfg = configuration.get_nodes_configuration()
# Set additional node information
for param, node_to_value in future.utils.iteritems(nodes_cfg):
if len(node_to_value) < len(self.graph.nodes):
raise ConfigurationException({"message": "Not all nodes have a configuration specified"})
self.params['nodes'][param] = node_to_value
edges_cfg = configuration.get_edges_configuration()
# Set additional edges information
for param, edge_to_values in future.utils.iteritems(edges_cfg):
if len(edge_to_values) == len(self.graph.edges):
self.params['edges'][param] = {}
for e in edge_to_values:
self.params['edges'][param][e] = edge_to_values[e]
# Set initial status
model_status = configuration.get_model_configuration()
for param, nodes in future.utils.iteritems(model_status):
self.params['status'][param] = nodes
for node in nodes:
self.status[node] = self.available_statuses[param]
# Set model additional information
model_params = configuration.get_model_parameters()
for param, val in future.utils.iteritems(model_params):
self.params['model'][param] = val
# Handle initial infection
if 'Infected' not in self.params['status']:
if 'percentage_infected' in self.params['model']:
self.params['model']['fraction_infected'] = self.params['model']['percentage_infected']
if 'fraction_infected' in self.params['model']:
number_of_initial_infected = self.graph.number_of_nodes() * float(self.params['model']['fraction_infected'])
if number_of_initial_infected < 1:
warnings.warn(
"The fraction_infected value is too low given the number of nodes of the selected graph: a "
"single node will be set as infected")
number_of_initial_infected = 1
available_nodes = [n for n in self.status if self.status[n] == 0]
sampled_nodes = np.random.choice(available_nodes, int(number_of_initial_infected), replace=False)
for k in sampled_nodes:
self.status[k] = self.available_statuses['Infected']
self.initial_status = self.status
def clean_initial_status(self, valid_status=None):
"""
Check the consistency of initial status
:param valid_status: valid node configurations
"""
for n, s in future.utils.iteritems(self.status):
if s not in valid_status:
self.status[n] = 0
# def iteration_bunch(self, bunch_size, node_status=True):
def iteration_bunch(self, node_status=True):
"""
Execute a bunch of model iterations
:param bunch_size: the number of iterations to execute
:param node_status: if the incremental node status has to be returned.
:return: a list containing for each iteration a dictionary {"iteration": iteration_id, "status": dictionary_node_to_status}
"""
system_status = []
all_activated_nodes = []
step_activated_nodes = []
while not self.stop:
its = self.iteration(node_status)
all_activated_nodes.extend(list(its[1].keys()))
step_activated_nodes.append(list(its[1].keys()))
active_set_size = its[0]
return active_set_size, all_activated_nodes, step_activated_nodes
def random_deactivation(self, activated_nodes):
"""
Randomly deactivate nodes from the set of activated nodes
"""
if activated_nodes != []:
deactivated_nodes = []
k = random.randint(0, math.ceil(len(activated_nodes) * 0.05))
deactivated_nodes = random.sample(activated_nodes, k = k)
print('Number of deactivations: ' + str(k))
print('Deactivated Nodes: ' + str(deactivated_nodes))
for node in deactivated_nodes:
self.status[node] = 0
activated_nodes.remove(node)
return len(activated_nodes), activated_nodes, deactivated_nodes
def mg_reset(self, temp_activated_nodes):
# mg reset for mg testing
#reset nodes that were activated during marginal gain testing
# need to reset attempted to 0 as well
# reset self.stop to false so can keep on testing
for n in temp_activated_nodes:
self.status[n] = 0
self.params['nodes']['attempt'][n] = 0
self.stop = False #so can run again
# return
# return self.status
def is_reset(self):
# is reset for influence spread
# reset self.stop so can proceed with subsequent mg testing and is
self.stop = False
def get_info(self):
"""
Describes the current model parameters (nodes, edges, status)
:return: a dictionary containing for each parameter class the values specified during model configuration
"""
info = {k: v for k, v in future.utils.iteritems(self.params) if k not in ['nodes', 'edges', 'status']}
if 'infected_nodes' in self.params['status']:
info['selected_initial_infected'] = True
return info['model']
def reset(self, infected_nodes=None):
"""
Reset the simulation setting the actual status to the initial configuration.
"""
self.actual_iteration = 0
if infected_nodes is not None:
for n in self.status:
self.status[n] = 0
for n in infected_nodes:
self.status[n] = self.available_statuses['Infected']
self.initial_status = self.status
self.stop = Falses
else:
if 'percentage_infected' in self.params['model']:
self.params['model']['fraction_infected'] = self.params['model']['percentage_infected']
if 'fraction_infected' in self.params['model']:
for n in self.status:
self.status[n] = 0
number_of_initial_infected = self.graph.number_of_nodes() * float(self.params['model']['fraction_infected'])
available_nodes = [n for n in self.status if self.status[n] == 0]
sampled_nodes = np.random.choice(available_nodes, int(number_of_initial_infected), replace=False)
for k in sampled_nodes:
self.status[k] = self.available_statuses['Infected']
self.initial_status = self.status
else:
self.status = self.initial_status
return self
def get_model_parameters(self):
return self.parameters
def get_name(self):
return self.name
def get_status_map(self):
"""
Specify the statuses allowed by the model and their numeric code
:return: a dictionary (status->code)
"""
return self.available_statuses
@abc.abstractmethod
def iteration(self, node_status=True):
"""
Execute a single model iteration
:param node_status: if the incremental node status has to be returned.
:return: Iteration_id,
(optional) Incremental node status (dictionary node->status),
Status count (dictionary status->node count),
Status delta (dictionary status->node delta)
"""
pass
@staticmethod
def check_status_similarity(actual, previous):
"""
Evaluate similarity among statuses
:param actual: actual status
:param previous: previous status
:return: True if the two statuses are the same, False otherwise
"""
for n, v in future.utils.iteritems(actual):
if n not in previous:
return False
if previous[n] != actual[n]:
return False
return True
def status_delta(self, actual_status):
"""
Compute the point-to-point variations for each status w.r.t. the previous system configuration
:param actual_status: the actual simulation status
:return: node that have changed their statuses (dictionary status->nodes),
count of actual nodes per status (dictionary status->node count),
delta of nodes per status w.r.t the previous configuration (dictionary status->delta)
"""
actual_status_count = {}
old_status_count = {}
delta = {}
for n, v in future.utils.iteritems(self.status):
if v != actual_status[n]:
delta[n] = actual_status[n]
for st in list(self.available_statuses.values()):
actual_status_count[st] = len([x for x in actual_status if actual_status[x] == st])
old_status_count[st] = len([x for x in self.status if self.status[x] == st])
status_delta = {st: actual_status_count[st] - old_status_count[st] for st in actual_status_count}
return delta, actual_status_count, status_delta
def status_delta_continuous(self, actual_status):
"""
Compute the point-to-point variations for each status w.r.t. the previous system configuration
Should be used for continuous statuses instead of discrete values
:param actual_status: the actual simulation status
:return: nodes that have changed their statuses (dictionary status->nodes),
count of actual nodes per status (dictionary status->node count),
delta of nodes per status w.r.t the previous configuration (dictionary status->delta)
"""
delta = {}
status_delta = {}
for n, v in future.utils.iteritems(self.status):
# print(n)
# print(v)
delta[n] = {}
status_delta[n] = {}
for var, val in list(v.items()):
if val != actual_status[n][var]:
delta[n][var] = actual_status[n][var]
status_delta[n][var] = actual_status[n][var] - val
if len(list(delta[n].values())) == 0:
del delta[n]
if len(list(status_delta[n].values())) == 0:
del status_delta[n]
return delta, status_delta
def build_trends(self, iterations):
"""
Build node status and node delta trends from model iteration bunch
:param iterations: a set of iterations
:return: a trend description
"""
status_delta = {status: [] for status in list(self.available_statuses.values())}
node_count = {status: [] for status in list(self.available_statuses.values())}
for it in iterations:
for st in list(self.available_statuses.values()):
try:
status_delta[st].append(it['status_delta'][st])
node_count[st].append(it['node_count'][st])
except:
status_delta[st].append(it['status_delta'][str(st)])
node_count[st].append(it['node_count'][str(st)])
return [{"trends": {"node_count": node_count, "status_delta": status_delta}}]
def test_add_edges(self):
g = nx.karate_club_graph()
ag = AGraph(g)
n1 = ag.neighbors(1)
ag.add_edges(1, [5, 6])
n2 = ag.neighbors(1)
n1.extend([5, 6])
self.assertListEqual(n2, n1)
g = nx.karate_club_graph()
g = from_nx_to_igraph(g)
ag = AGraph(g)
n3 = ag.neighbors(1)
ag.add_edges(1, [5, 6])
n4 = ag.neighbors(1)
n3.extend([5, 6])
self.assertListEqual(sorted(n3), sorted(n4))
self.assertListEqual(sorted(n2), sorted(n4))
def test_remove_edges(self):
g = nx.karate_club_graph()
ag = AGraph(g)
n1 = ag.neighbors(1)
ag.remove_edges(1, [0, 2])
n2 = ag.neighbors(1)
n1 = [n for n in n1 if n not in [0, 2]]
self.assertListEqual(n2, n1)
g = nx.karate_club_graph()
g = from_nx_to_igraph(g)
ag = AGraph(g)
n3 = ag.neighbors(1)
ag.remove_edges(1, [0, 2])
n4 = ag.neighbors(1)
n3 = [n for n in n3 if n not in [0, 2]]
self.assertListEqual(sorted(n3), sorted(n4))
self.assertListEqual(sorted(n2), sorted(n4))
class multiple_independent_cascade(DiffusionModel):
def __init__(self, graph, seed=None):
"""
Model Constructor
:param graph: A networkx graph object
"""
np.random.seed(seed)
self.discrete_state = True
self.params = {
'nodes': {},
'edges': {
"threshold_1": {
"descr": "Infection rate for contagion 1.",
"range": [0, 1],
"optional": False
},
"threshold_2": {
"descr": "Infection rate for contagion 2.",
"range": [0, 1],
"optional": False
},
"infection_1_given_2": {
"descr":
"Infection rate for contagion 1 given infected with contagion 2.",
"range": [0, 1],
"optional": False
},
"infection_2_given_1": {
"descr":
"Infection rate for contagion 2 given infected with contagion 1.",
"range": [0, 1],
"optional": False
},
},
'model': {},
'status': {}
}
self.available_statuses = {}
cnt = 0
for i in range(3):
for j in range(3):
self.available_statuses[(i, j)] = cnt
cnt += 1
self.name = "multiple_IC"
self.actual_iteration = 0
self.graph = AGraph(graph)
self.status = {n: 0 for n in self.graph.nodes}
self.initial_status = {}
def iteration(self, node_status=True):
"""
Execute a single model iteration
:return: Iteration_id, Incremental node status (dictionary node->status)
"""
self.clean_initial_status(self.available_statuses.values())
actual_status = {
node: nstatus
for node, nstatus in future.utils.iteritems(self.status)
}
if self.actual_iteration == 0:
self.actual_iteration += 1
delta, node_count, status_delta = self.status_delta(actual_status)
if node_status:
return {
"iteration": 0,
"status": actual_status.copy(),
"node_count": node_count.copy(),
"status_delta": status_delta.copy()
}
else:
return {
"iteration": 0,
"status": {},
"node_count": node_count.copy(),
"status_delta": status_delta.copy()
}
for u in self.graph.nodes:
for i in range(2):
if self.status[u][i] != 1:
continue
neighbors = list(
self.graph.neighbors(u)
) # neighbors and successors (in DiGraph) produce the same result
# Standard threshold
if len(neighbors) > 0:
threshold = 1.0 / len(neighbors)
for v in neighbors:
if actual_status[v][i] == 0:
key = (u, v)
if self.params['nodes']['com'][u] == self.params[
'nodes']['com'][
v]: # within same community
# Individual specified thresholds
if 'threshold_' + str(
i) in self.params['edges']:
if key in self.params['edges']['threshold_'
+ str(i)]:
threshold = self.params['edges'][
'threshold_' + str(i)][key]
elif (v, u) in self.params['edges'][
'threshold_' +
str(i
)] and not self.graph.directed:
threshold = self.params['edges'][
'threshold_' + str(i)][(v, u)]
else: # across communities
p = self.params['model']['permeability']
if 'threshold_' + str(
i) in self.params['edges']:
if key in self.params['edges']['threshold_'
+ str(i)]:
threshold = self.params['edges'][
'threshold_' + str(i)][key] * p
elif (v, u) in self.params['edges'][
'threshold_' +
str(i
)] and not self.graph.directed:
threshold = self.params['edges'][
'threshold_' + str(i)][(v, u)] * p
flip = np.random.random_sample()
if flip <= threshold:
actual_status[v][i] = 1
actual_status[u][i] = 2
delta, node_count, status_delta = self.status_delta(actual_status)
self.status = actual_status
self.actual_iteration += 1
if node_status:
return {
"iteration": self.actual_iteration - 1,
"status": delta.copy(),
"node_count": node_count.copy(),
"status_delta": status_delta.copy()
}
else:
return {
"iteration": self.actual_iteration - 1,
"status": {},
"node_count": node_count.copy(),
"status_delta": status_delta.copy()
}