def run(graph, measures, k):
result = []
for measure in measures:
if '_approx' in measure:
measure = measure.replace('_approx', '')
r = run_measure(graph=graph, measure=measure, k=k)
else:
r = run_measure(graph=graph, measure=measure)
result.append(r)
return result
def track_simulation(self, step):
"""
Keeps track of important simulation information at each step of the simulation
:param step: current simulation interation
"""
measure = run_measure(self.graph_, self.prm['robust_measure'])
ccs = list(nx.connected_components(self.graph_))
ccs.sort(key=len, reverse=True)
m = interp1d([0, len(ccs)], [0.15, 1])
status = {}
for n in self.graph:
for idx, cc in enumerate(ccs):
if n in self.attacked[0:step]:
status[n] = 1
break
elif n in cc:
status[n] = float(m(idx))
break
else:
status[n] = 0
self.sim_info[step] = {
'status': list(status.values()),
'failed': len(self.attacked[0:step]),
'measure': measure,
'protected': self.protected
}
def test_measures():
measure_ground_truth = { # graph order: o4, p4, c4, k4_1, c4_0, c4_1, c4_2, c4_3
'node_connectivity': [0, 1, 2, 2, 3, 0, 1, 1, 1],
'edge_connectivity': [0, 1, 2, 2, 3, 0, 1, 1, 1],
'diameter': [None, 3, 2, 2, 1, None, 5, 5, 5],
'average_distance': [None, 1.67, 1.33, 1.17, 1, None, 2.29, 2.29, 2.29],
'average_inverse_distance': [0, 0.72, 0.83, 0.92, 1.0, 0.36, 0.58, 0.58, 0.58],
'average_vertex_betweenness': [0, 4, 3.5, 3.25, 3, 3.5, 11.5, None, None],
'average_edge_betweenness': [0, 3.33, 2.0, 1.4, 1, 2, 7.11, 7.11, 7.11],
'average_clustering_coefficient': [0, 0, 0, 0.83, 1, 0, 0, None, None],
'largest_connected_component': [1, 4, 4, 4, 4, 4, 8, 8, 8],
'spectral_radius': [0, 1.62, 2, 2.56, 3, 2, 2.34, 2.9, 3.65],
'spectral_gap': [0, 1, 2, 2.56, 4, 0, 0.53, 1.19, 2],
'natural_connectivity': [0, 0.65, 0.87, 1.29, 1.67, 0.87, 0.97, 1.28, 1.81],
'spectral_scaling': [None, 7.18, 7.28, 0.17, 0.09, None, None, 7.04, 6.93],
'generalized_robustness_index': [None, 7.18, 7.28, 0.17, 0.09, None, None, 7.04, 6.93],
'algebraic_connectivity': [0, 0.59, 2, 2, 4, 0, 0.29, 0.4, 0.45],
'number_spanning_trees': [0, 1, 4, 8, 16, 0, 16, 32, 48],
'effective_resistance': [np.inf, 10, 5, 4, 3, np.inf, 46, 38, 35.33]
}
graphs = [o4_graph(), p4_graph(), c4_graph(), k4_1_graph(), k4_2_graph(),
two_c4_0_bridge(), two_c4_1_bridge(), two_c4_2_bridge(), two_c4_3_bridge()]
for measure_name, graph_values in measure_ground_truth.items():
for idx, graph in enumerate(graphs):
value = run_measure(graph, measure_name)
if value is not None: value = round(value, 2)
# print(idx, measure_name, value)
assert value, graph_values[idx]
def track_simulation(self, step):
"""
Keeps track of important simulation information at each step of the simulation
:param step: current simulation interation
"""
nodes_functioning = set(self.graph.nodes).difference(self.failed)
measure = 0
if len(nodes_functioning) > 0:
measure = run_measure(self.graph.subgraph(nodes_functioning),
self.prm['robust_measure'])
self.sim_info[step] = {
'status': [self.load[n] for n in self.graph.nodes],
'failed': len(self.failed),
'measure': measure,
'protected': self.protected
}
def measure_time(graph, measure, timeout):
start = time.time()
if measure in spectral_approx:
k = 30
measure = measure.replace('_approx', '')
elif measure in graph_approx:
k = int(0.1 * len(graph))
measure = measure.replace('_approx', '')
else:
k = np.inf
result = run_measure(graph, measure, k, timeout=timeout)
end = time.time()
run_time = math.ceil(end - start)
if result is None:
run_time = None
return len(graph), run_time