def store_best_network(chromosome, nb_tests):
#bugged and unused, unknown use
score_max = 0
for _ in range(nb_tests):
print chromosome.getParam("results_path")
number_of_nodes, number_of_edges = ne.get_number_of_nodes_and_edges(
chromosome.getParam("results_path"))
net = nd.grow_network(chromosome, number_of_nodes, number_of_edges)
scores = ne.eval_network(
net,
chromosome.getParam("results_path"),
evaluation_method=chromosome.getParam("evaluation_method"),
network_type=chromosome.getParam("network_type"),
name=chromosome.getParam("name"))
if scores['proximity_aggregated'] > score_max:
score_max = scores['proximity_aggregated']
nx.draw(net)
plt.savefig(
chromosome.getParam('results_path').replace(".xml", ".png"))
plt.close()
print scores
f = open(chromosome.getParam('results_path'), 'a')
f.write(str(scores))
f.close()
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 eval_func(chromosome):
number_of_nodes,number_of_edges = ne.get_number_of_nodes_and_edges(chromosome.getParam("results_path"))
net = nd.grow_network(chromosome,number_of_nodes,number_of_edges )
score = ne.eval_network(net,chromosome.getParam("results_path"),
evaluation_method=chromosome.getParam("evaluation_method"),
network_type = chromosome.getParam("network_type"),
name = chromosome.getParam("name"))
return score['proximity_aggregated']
def eval_func(chromosome):
number_of_nodes, number_of_edges = ne.get_number_of_nodes_and_edges(
chromosome.getParam("results_path"))
net = nd.grow_network(chromosome, number_of_nodes, number_of_edges)
score = ne.eval_network(
net,
chromosome.getParam("results_path"),
evaluation_method=chromosome.getParam("evaluation_method"),
network_type=chromosome.getParam("network_type"),
name=chromosome.getParam("name"))
return score['proximity_aggregated']
def eval_func(chromosome):
number_of_nodes, number_of_edges = ne.get_number_of_nodes_and_edges(chromosome.getParam("results_path"),
chromosome.getParam('name'))
static_number = min(number_of_nodes,chromosome.getParam("number_of_nodes"))
number_of_edges = static_number * number_of_edges / number_of_nodes
number_of_nodes =static_number
net = nd.grow_network(chromosome, number_of_nodes, number_of_edges)
raw_score,details,more_details = ne.eval_network(net, chromosome.getParam("results_path"),
evaluation_method=chromosome.getParam("evaluation_method"),
network_type=chromosome.getParam("network_type"),
name=chromosome.getParam("name"))
chromosome.scoref = lambda: None
setattr(chromosome.scoref, 'score', details)
setattr(chromosome.scoref, 'distributions', more_details)
return raw_score
def store_best_network(chromosome,nb_tests) :
#bugged and unused, unknown use
score_max = 0
for _ in range(nb_tests) :
print chromosome.getParam("results_path")
number_of_nodes,number_of_edges = ne.get_number_of_nodes_and_edges(chromosome.getParam("results_path"))
net = nd.grow_network(chromosome,number_of_nodes,number_of_edges )
scores = ne.eval_network(net,chromosome.getParam("results_path"),evaluation_method=chromosome.getParam("evaluation_method"),network_type = chromosome.getParam("network_type"), name = chromosome.getParam("name"))
if scores['proximity_aggregated'] > score_max :
score_max = scores['proximity_aggregated']
nx.draw(net)
plt.savefig(chromosome.getParam('results_path').replace(".xml",".png"))
plt.close()
print scores
f = open(chromosome.getParam('results_path'), 'a')
f.write(str(scores))
f.close()