def eval_func_dynamic(chromosome):
results_path = chromosome.getParam("results_path")
evaluation_method = chromosome.getParam("evaluation_method")
data_path = chromosome.getParam("data_path")
network_type = chromosome.getParam("network_type")
extension = chromosome.getParam("extension")
name = chromosome.getParam("name")
number_of_networks = len([namefile for namefile in os.listdir(data_path) if extension in namefile])
scores = {}
# we create a graph that is similar to the initial graph
initial_network = ne.read_typed_file(data_path + name + "0" + extension)
net = nd.createGraph(network_type, initial_network)
for numero in range(number_of_networks - 1):
nodes_next, edges_next = ne.get_number_of_nodes_and_edges(results_path, numero + 1)
nodes_now, edges_now = ne.get_number_of_nodes_and_edges(results_path, numero)
difference_of_size = nodes_next - nodes_now
difference_of_edge_number = edges_next - edges_now
# at each step we add nodes and edges to match with graph at next step
net = nd.grow_network(chromosome, difference_of_size, difference_of_edge_number, net)
# comparison with the network at next step : returns a dict : observable-proximity related to the observable
scores[str(numero + 1)] = ne.eval_network(net,
results_path, numero + 1,
evaluation_method=evaluation_method,
network_type=network_type,
name=name)
# we compute a list that contains aggregated proximity at each time step
scores_aggregated = [scores[str(score)]['proximity_aggregated'] for score in range(1, number_of_networks)]
return min(scores_aggregated)
def eval_func_dynamic(chromosome):
results_path = chromosome.getParam("results_path")
evaluation_method = chromosome.getParam("evaluation_method")
data_path = chromosome.getParam("data_path")
network_type = chromosome.getParam("network_type")
extension = chromosome.getParam("extension")
name = chromosome.getParam("name")
number_of_networks = len([namefile for namefile in os.listdir(data_path) if extension in namefile])
scores = {}
# we create a graph that is similar to the initial graph
initial_network = ne.read_typed_file(data_path + name + "0" + extension)
net = nd.createGraph(network_type, initial_network)
for numero in range(number_of_networks - 1):
nodes_next, edges_next = ne.get_number_of_nodes_and_edges(results_path, numero + 1)
nodes_now, edges_now = ne.get_number_of_nodes_and_edges(results_path, numero)
difference_of_size = nodes_next - nodes_now
difference_of_edge_number = edges_next - edges_now
# at each step we add nodes and edges to match with graph at next step
net = nd.grow_network(chromosome, difference_of_size, difference_of_edge_number, net)
# comparison with the network at next step : returns a dict : observable-proximity related to the observable
scores[str(numero + 1)] = ne.eval_network(net,
results_path, numero + 1,
evaluation_method=evaluation_method,
network_type=network_type,
name=name)
# we compute a list that contains aggregated proximity at each time step
scores_aggregated = [scores[str(score)]['proximity_aggregated'] for score in range(1, number_of_networks)]
return min(scores_aggregated)
def develop_network(rule,number):
graph = nd.createGraph(network_type)
variable = rule.getData()[1]
global data_path
network = nd.grow_network_with_constants(graph,py.GTree.GTree(rule),number_of_nodes, number_of_edges)
nx.write_gml(network, data_path)
nx.draw(network)
plt.savefig("../../results/temp/drawings/{}_{}.png".format(variable,number))
plt.close()
def develop_network(rule, number):
graph = nd.createGraph(network_type)
variable = rule.getData()[1]
global data_path
network = nd.grow_network_with_constants(graph, py.GTree.GTree(rule),
number_of_nodes, number_of_edges)
nx.write_gml(network, data_path)
nx.draw(network)
plt.savefig("../../results/temp/drawings/{}_{}.png".format(
variable, number))
plt.close()
