def simulate_and_dump_result(algorithm, graph_filename, out_filename):
# parsing valid algorithm
if algorithm == 'sun':
opt_class = Sun
config = dict(log_level=LOG_LEVEL,
force_update=True, # update graph even if swap is not done
max_trials=1000, # and continue even if swap is done
Nt=6, Ng=10)
n_steps = 30
elif algorithm == 'ichinose':
opt_class = IchinoseSatotani
config = dict(log_level=LOG_LEVEL,
force_update=False) # does not accept if swap is not done
n_steps = 500
elif algorithm == 'ichinose_greedy':
opt_class = IchinoseSatotani
config = dict(log_level=LOG_LEVEL,
greedy=True, # greedy swapping
force_update=False) # same as above
n_steps = 500
else: raise ValueError('invalid algorithm: ', algorithm)
# number of attacks (to calculate R)
n_attacks = 10
# load graph
graph = nx.read_gpickle(graph_filename)
# define columns in csv
columns = [
('R', lambda G: R(G, n_attacks)),
('r', nx.degree_pearson_correlation_coefficient)
]
# use TimeTracker to track R and r
tracker = TimeTracker(opt_class, graph, columns, **config)
tracker.track(steps=n_steps)
tracker.dump(out_filename) # in this time, algorithm$i.ba.csv
import networkx as nx
from robust_graph import R
from robust_graph import LOG_LEVEL_INFO as LOG_LEVEL
from robust_graph import TimeTracker
# an algorithm
from robust_graph import IchinoseSatotani
# function to load US airline network
from robust_graph import load_us
# some important constants
n_attacks = 10
n_steps = 100
filename = 'Randr.csv'
graph = load_us()
# define columns in csv
columns = [
# R robustness
('R', lambda G: R(G, n_attacks)),
# r degree correlation
('r', nx.degree_pearson_correlation_coefficient)
]
# use TimeTracker to track R and r
tracker = TimeTracker(IchinoseSatotani, graph, columns,
log_level=LOG_LEVEL, force_update=False, greedy=True)
tracker.track(steps=n_steps)
tracker.dump(filename)
from robust_graph import R
from robust_graph import LOG_LEVEL_INFO as LOG_LEVEL
from robust_graph import TimeTracker
# compare two algorithms
from robust_graph import Schneider, IchinoseSatotani
# function to load US airline network
from robust_graph.util import load_us
# some important constants
n_attacks = 10
n_steps = 100
fileprefix = 'speed_test'
graph = load_us()
# define columns in csv
columns = [
# R robustness
('R', lambda G: R(G, n_attacks))
]
for params in [(Schneider, dict(log_level=LOG_LEVEL, force_update=True)),
(IchinoseSatotani, dict(log_level=LOG_LEVEL,
force_update=False,
greedy=True))]:
opt_cls, config = params
tracker = TimeTracker(opt_cls, graph, columns,**config)
tracker.track(steps=n_steps)
tracker.dump(fileprefix + '.' + opt_cls.__name__ + '.csv')
# time tracker to track simulation
from robust_graph import TimeTracker
# function to load US airline network
from robust_graph import load_us
# some important constants
n_attacks = 10
n_steps = 30
filename = sys.argv[1]
# load graph
graph = load_us()
#graph = nx.barabasi_albert_graph(20, 3)
# define columns in csv
columns = [
('R', lambda G: R(G, n_attacks)),
('r', nx.degree_pearson_correlation_coefficient)
]
# use TimeTracker to track R and r
tracker = TimeTracker(Sun, graph, columns,
# configurations: see robust_graph.optimize.Sun
log_level=LOG_LEVEL,
force_update=True,
max_trials=1000,
Nt=6, Ng=10)
tracker.track(steps=n_steps)
tracker.dump(filename) # in this time, tabu$i.us.csv
