def main():
parser = argparse.ArgumentParser(
description='Calculate meta information for real traces',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('network')
parser.add_argument('traceroutes')
parser.add_argument('output')
parser.add_argument('--maxi', type=int, default=500)
# for paralelization
parser.add_argument('--lower-bound', '-lb', type=int, default=0, dest='lb')
parser.add_argument('--upper-bound',
'-ub',
type=int,
default=-1,
dest='ub')
arguments = parser.parse_args()
g = helpers.load_as_inferred_links(arguments.network)
traceroutes = helpers.load_from_json(arguments.traceroutes)
# traceroutes = random.sample(traceroutes, arguments.maxi)
arguments.lb = arguments.lb if 0 <= arguments.lb <= len(traceroutes) else 0
arguments.ub = arguments.ub if 0 <= arguments.ub <= len(
traceroutes) else len(traceroutes)
result = filter(g, traceroutes[arguments.lb:arguments.ub])
helpers.save_to_json(arguments.output, result)
def sh_gen(arguments):
ba_graph = helpers.load_network(arguments.ba_graph)
trace_count = int(arguments.route_count)
node_ids = range(ba_graph.vcount())
logger.info('Trace count: %d' % trace_count)
random_pairs = [random.sample(node_ids, 2) for x in xrange(trace_count)]
random_pairs = [(ba_graph.vs[x[0]]['name'], ba_graph.vs[x[1]]['name'])
for x in random_pairs]
logger.info('Random pair count: %d' % len(random_pairs))
shls = []
results = []
for s_name, t_name in random_pairs:
shl = ba_graph.shortest_paths(s_name, t_name)[0][0] + 1
while shl == float('inf'):
s, t = random.sample(node_ids, 2)
s_name = ba_graph.vs[s]['name']
t_name = ba_graph.vs[t]['name']
shl = ba_graph.shortest_paths(s_name, t_name)[0][0] + 1
logger.debug('From %s to %s SH: %d' % (s_name, t_name, shl))
shls.append(shl)
results.append([(s_name, t_name), shl])
# result = zip(random_pairs, shls)
helpers.save_to_json(arguments.sh_path_output, results)
def upwalker_counter(g, meta, vf_g, arguments):
N = g.vcount()
progress = progressbar1.DummyProgressBar(end=10, width=15)
if arguments.progressbar:
progress = progressbar1.AnimatedProgressBar(end=len(meta), width=15)
# trace_up_map = {}
# random_up_map = {}
trace_up_counter = []
random_up_counter = []
for m in meta:
progress += 1
progress.show_progress()
trace = m[helpers.TRACE]
trace_dir = vft.trace_to_string(g, trace)
trace_up_count = trace_dir.count('U')
trace_up_counter.append(trace_up_count)
# trace_up_map[trace_up_count] = trace_up_map.get(trace_up_count, 0) +
# 1
s, t = trace[0], trace[-1]
s_idx, t_idx = vft.node_to_nodeid(g, s), vft.node_to_nodeid(g, t)
random_vf_route = helpers.random_route_walk(vf_g,
s_idx,
t_idx + N,
len(trace),
named=False,
weight_field='VFweight')
random_vf_route = vft.vf_route_converter(random_vf_route, N)
random_vf_dir = vft.trace_to_string(g, random_vf_route)
random_vf_count = random_vf_dir.count('U')
random_up_counter.append(random_vf_count)
# random_up_map[random_vf_count] = random_up_map.get(random_vf_count,
# 0) + 1
real_counter = collections.Counter(trace_up_counter)
real_up = ' '.join(
['%s: %s' % (k, real_counter[k]) for k in sorted(list(real_counter))])
random_counter = collections.Counter(random_up_counter)
random_up = ' '.join([
'%s: %s' % (k, random_counter[k]) for k in sorted(list(random_counter))
])
logger.info('')
logger.info('Real trace UP counter: %s' % real_up)
logger.info('Random vf trace up counter: %s' % random_up)
helpers.save_to_json(arguments.out, {
'REAL': dict(real_counter),
'RANDOM': dict(random_counter)
})
keys = sorted(set(list(real_counter) + list(random_counter)))
logger.info('IDX;REAL;RANDOM')
for k in keys:
logger.info('%s;%d;%d' % (k, real_counter[k], random_counter[k]))
def ba_calc(arguments):
ba_graph = helpers.load_network(arguments.ba_graph)
sh_paths = helpers.load_from_json(arguments.point_pairs)
out = arguments.out
min_stretch = arguments.min_stretch
max_stretch = arguments.max_stretch
max_c = len(sh_paths)
arguments.lb = arguments.lb if 0 <= arguments.lb <= max_c else 0
arguments.ub = arguments.ub if 0 <= arguments.ub <= max_c else max_c
arguments.lb, arguments.ub = (min(arguments.lb, arguments.ub),
max(arguments.lb, arguments.ub))
sh_paths = sh_paths[arguments.lb:arguments.ub]
vf_g_closeness = vft.convert_to_vf(ba_graph, vfmode=vft.CLOSENESS)
results = [[] for x in xrange(min_stretch, max_stretch + 1)]
for stretch in xrange(min_stretch, max_stretch + 1):
logger.info('Calculate results with stretch %d' % stretch)
result = ba_generator(ba_graph, sh_paths, stretch, vf_g_closeness,
arguments.progressbar)
results[stretch - min_stretch] = result
helpers.save_to_json(out, results)
def main():
parser = argparse.ArgumentParser(description='Calculate meta information for real traces', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('network')
parser.add_argument('meta')
parser.add_argument('output', type=argparse.FileType('w'))
# parser.add_argument('--vfmode', type=str, default='labeled', dest='vfmode',
# choices=['labeled', 'closeness'])
# for paralelization
parser.add_argument('--lower-bound', '-lb', type=int, default=0, dest='lb')
parser.add_argument('--upper-bound', '-ub', type=int, default=-1, dest='ub')
parser.add_argument('--progressbar', action='store_true')
parser.add_argument('--verbose', '-v', action='count', default=0)
parser.add_argument('--with-prelabeled', action='store_true')
parser.add_argument('--with-closeness', action='store_true')
parser.add_argument('--with-degree', action='store_true')
parser.add_argument('--with-lp-hard', action='store_true')
parser.add_argument('--with-lp-soft', action='store_true')
# parser.add_argument('--with-lp', action='store_true')
# parser.add_argument('--with-vf', action='store_true')
parser.add_argument('--try-per-trace',
type=int, default=1, dest='try_per_trace')
arguments = parser.parse_args()
arguments.verbose = min(len(helpers.LEVELS), arguments.verbose)
logging.getLogger('compnet').setLevel(helpers.LEVELS[arguments.verbose])
g = helpers.load_network(arguments.network)
meta = helpers.load_from_json(arguments.meta)
arguments.lb = arguments.lb if 0 <= arguments.lb <= len(meta) else 0
arguments.ub = arguments.ub if 0 <= arguments.ub <= len(meta) else len(meta)
flags = {
FLAG_PRELABELED: arguments.with_prelabeled,
FLAG_CLOSENESS: arguments.with_closeness,
FLAG_DEGREE: arguments.with_degree,
FLAG_LP_HARD: arguments.with_lp_hard,
FLAG_LP_SOFT: arguments.with_lp_soft
}
# if arguments.vfmode == 'labeled': mode = vft.ORDER_PRELABELED
# elif arguments.vfmode == 'closeness': mode = vft.ORDER_CLOSENESS
# else: raise RuntimeError('Unhandled vfmode')
meta = meta[arguments.lb:arguments.ub]
# update meta at place
purify(g, meta, flags, arguments.try_per_trace, arguments.progressbar)
logger.info('Save to %s' % arguments.output)
helpers.save_to_json(arguments.output, meta)
def wrap_watts_trace_gen(args):
g = helpers.load_network(args.network)
traceroutes = helpers.load_from_json(args.original_traceroutes)
max_c = len(traceroutes)
args.lb = args.lb if 0 <= args.lb <= max_c else 0
args.ub = args.ub if 0 <= args.ub <= max_c else max_c
args.lb, args.ub = (min(args.lb, args.ub), max(args.lb, args.ub))
traceroutes = traceroutes[args.lb:args.ub]
watts_traceroutes = watts_trace_gen(g, traceroutes, args.progressbar)
helpers.save_to_json(args.traceroute_dest, watts_traceroutes)
def main():
parser = argparse.ArgumentParser(
description=
'Filter out non vf and non lp traceroutes from given traceroute list',
parents=[
argparse_general.commonParser,
],
**argparse_general.commonParams)
parser.add_argument('network')
parser.add_argument('traceroutes')
parser.add_argument(
'--filter',
default='sh+loop+ex+lp',
help=
'Possible values: sh (short), loop (AS number repetition), ex (non existent), vf (non valley free), lp (non local preferenced), or any combination with + sign. Note that lp automatically means vf+lp'
)
parser.add_argument('--lp-type',
default='first',
choices=['first', 'all'],
dest='first_edge')
parser.add_argument('output')
arguments = parser.parse_args()
arguments.verbose = min(len(helpers.LEVELS), arguments.verbose)
logging.getLogger('compnet').setLevel(helpers.LEVELS[arguments.verbose])
arguments.first_edge = arguments.first_edge == 'first'
if arguments.first_edge:
logger.debug('LP only first edge')
else:
logger.debug('LP all edge')
g = helpers.load_network(arguments.network)
traceroutes = helpers.load_from_json(arguments.traceroutes)
arguments.lb = arguments.lb if 0 <= arguments.lb <= len(traceroutes) else 0
arguments.ub = arguments.ub if 0 <= arguments.ub <= len(
traceroutes) else len(traceroutes)
arguments.filter = arguments.filter.replace('lp', 'vf+lp')
filters = arguments.filter.split('+')
result = filter(g, traceroutes[arguments.lb:arguments.ub], filters,
arguments.first_edge)
helpers.save_to_json(arguments.output, result)
def main():
parser = argparse.ArgumentParser(
description=('Generate test graphs'),
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('graph_out', metavar='graph-out')
parser.add_argument('trace_out', metavar='trace-out')
parser.add_argument('--progressbar', action='store_true')
parser.add_argument('--verbose', '-v', action='count', default=0)
parser.add_argument('--node-count',
'-nc',
type=int,
dest='node_count',
default=100)
parser.add_argument('--network-type',
choices=[x for x in graphs_map.iterkeys()],
default='star')
parser.add_argument('--trace-count', type=int, default=50)
arguments = parser.parse_args()
arguments.verbose = min(len(helpers.LEVELS), arguments.verbose)
logging.getLogger('compnet').setLevel(helpers.LEVELS[arguments.verbose])
show_progress = arguments.progressbar
# g = graphs_map[arguments.network_type](arguments.node_count)
# g = igraph.Graph.Barabasi(900, 9)
# for n in g.vs:
# n['closeness'] = g.closeness(n)
# n['name'] = 'V%d' % n.index
# g.save(arguments.graph_out)
g = igraph.load(arguments.graph_out)
pairs = [
random.sample(xrange(0, g.vcount()), 2)
for x in xrange(0, arguments.trace_count)
]
pairs = [[g.vs[x[0]]['name'], g.vs[x[1]]['name']] for x in pairs]
traces = []
for p in pairs:
trace = random.choice(g.get_all_shortest_paths(p[0], p[1]))
trace = [g.vs[x]['name'] for x in trace]
traces.append(trace)
helpers.save_to_json(arguments.trace_out, traces)
def purify(g, out, count=1000, show_progress=False):
logger.info('Started')
nodes = range(0, g.vcount())
endpoints = [random.sample(nodes, 2) for idx in range(0, count)]
progress = progressbar1.DummyProgressBar(end=10, width=15)
if show_progress:
progress = progressbar1.AnimatedProgressBar(
end=len(endpoints), width=15)
traces = []
for endpoint in endpoints:
progress += 1
progress.show_progress()
src, dst = endpoint
trace = g.get_shortest_paths(src, dst)[0]
if len(trace) > 0:
traces.append([g.vs[x]['name'] for x in trace])
logger.info('Len last trace: %d' % len(trace))
helpers.save_to_json(out, traces)
def purify(g, meta, out, count=1000):
results = list()
results2 = list()
results3 = list()
all_vf = 0
all_nonvf = 0
all_vf_closeness = 0
all_nonvf_closeness = 0
short_results = list()
short_results2 = list()
short_results3 = list()
all_short_vf = 0
all_short_nonvf = 0
all_short_vf_closeness = 0
all_short_nonvf_closeness = 0
long_results = list()
long_results2 = list()
long_results3 = list()
all_long_vf = 0
all_long_nonvf = 0
all_long_vf_closeness = 0
all_long_nonvf_closeness = 0
# remove traces with already calculated all_path
logger.warn('[r]ONLY NOT FILLED PATHS[/]')
meta = [x for x in meta if not helpers.ALL_PATH_COUNT in x]
# traces with a maximum stretch
logger.warn('[r]!!!ONLY WITH LOW STRETCH[/]')
meta = [x for x in meta if x[helpers.STRETCH] < 4]
# shorter meta records
logger.warn('[r]!!!ONLY SHORT TRACES[/]')
meta = [x for x in meta if len(x[helpers.TRACE]) < 5]
meta_map = {tuple(x[helpers.TRACE]): x for x in meta}
# traceroutes = [x for x in meta if x[TRACE_LEN] == x[SH_LEN]]
logger.info('All trace count: %d' % len(meta))
tr_count = min(len(meta), count)
meta = random.sample(meta, tr_count)
logger.info('Chosen trace count: %d' % len(meta))
real_vf = [x for x in meta if x[helpers.IS_VF] == 1]
real_nonvf = [x for x in meta if x[helpers.IS_VF] == 0]
real_vf_closeness = [x for x in meta if x[helpers.IS_VF_CLOSENESS] == 1]
real_nonvf_closeness = [x for x in meta if x[helpers.IS_VF_CLOSENESS] == 0]
logger.info('Real vf: %f[%d]' % ((len(real_vf)/float(len(meta)), len(real_vf))))
logger.info('Real nonvf: %f[%d]' % ((len(real_nonvf)/float(len(meta)), len(real_nonvf))))
logger.info('Real vf closeness: %f[%d]' % ((len(real_vf_closeness)/float(len(meta)), len(real_vf_closeness))))
logger.info('Real nonvf closeness: %f[%d]' % ((len(real_nonvf_closeness)/float(len(meta)), len(real_nonvf_closeness))))
logger.info('Remove unknown traces. Trace count before: %d' % len(meta))
traceroutes = [x[helpers.TRACE] for x in meta]
traceroutes, ignored = vft.trace_clean(g, traceroutes)
logger.info('Traceroutes after: %d. Ignored: %d' % (len(traceroutes), ignored))
traceroutes = vft.trace_in_vertex_id(g, traceroutes)
progress = progressbar1.AnimatedProgressBar(end=len(traceroutes), width=15)
for trace in traceroutes:
progress += 1
progress.show_progress()
for x in range(0, g.vcount()):
g.vs[x]['traces'] = dict()
s, t = trace[0], trace[-1]
sh_path = g.get_all_shortest_paths(s, t, mode=i.OUT)
all_path = helpers.dfs_mark(copy.deepcopy(g), s, t, len(trace))
# if len(sh_path) != len(all_path):
# print len(sh_path)
# print len(all_path)
# print s, t
# sanity check
for x in all_path:
if x[0] != s or x[-1] != t:
logger.error('ALERT')
if len(set([tuple(x) for x in all_path])) != len(all_path):
logger.error('LENGTH ALERT')
logger.error('%s' % len(all_path))
logger.error('%s' % len(set([tuple(x) for x in all_path])))
logger.error('%s' % sorted(all_path))
long_path = [x for x in all_path if len(x) == len(trace)]
short_path = [x for x in all_path if len(x) < len(trace)]
named_trace = [g.vs[x]['name'] for x in trace]
extra_meta = {
helpers.ALL_PATH_COUNT: len(all_path),
helpers.SAME_LONG_PATH_COUNT: len(long_path),
helpers.SHORTER_PATH_COUNT: len(short_path)
}
meta_map[tuple(named_trace)].update(extra_meta)
vf_count = sum([1 if vft.is_valley_free(g, x, vfmode=vft.PRELABELED) else 0 for x in all_path])
nonvf = len(all_path) - vf_count
vf_closeness_count = sum([1 if vft.is_valley_free(g, x, vfmode=vft.CLOSENESS) else 0 for x in all_path])
nonvf_closeness = len(all_path) - vf_closeness_count
tmp = [1 if vft.is_valley_free(g, x, vfmode=vft.PRELABELED) else 0 for x in short_path]
short_vf_count = sum(tmp)
short_nonvf = len(tmp) - short_vf_count
tmp = [1 if vft.is_valley_free(g, x, vfmode=vft.CLOSENESS) else 0 for x in short_path]
short_vf_closeness_count = sum(tmp)
short_nonvf_closeness = len(tmp) - short_vf_closeness_count
tmp = [1 if vft.is_valley_free(g, x, vfmode=vft.PRELABELED) else 0 for x in long_path]
long_vf_count = sum(tmp)
long_nonvf = len(tmp) - long_vf_count
tmp = [1 if vft.is_valley_free(g, x, vfmode=vft.CLOSENESS) else 0 for x in long_path]
long_vf_closeness_count = sum(tmp)
long_nonvf_closeness = len(tmp) - long_vf_closeness_count
extra_meta = {
helpers.ALL_PATH_VF_COUNT: vf_closeness_count,
helpers.SAME_LONG_PATH_VF_COUNT: long_vf_closeness_count,
helpers.SHORTER_PATH_VF_COUNT: short_vf_closeness_count
}
meta_map[tuple(named_trace)].update(extra_meta)
all_vf += vf_count
all_nonvf += nonvf
all_vf_closeness += vf_closeness_count
all_nonvf_closeness += nonvf_closeness
all_long_vf += long_vf_count
all_long_nonvf += long_nonvf
all_long_vf_closeness += long_vf_closeness_count
all_long_nonvf_closeness += long_nonvf_closeness
all_short_vf += short_vf_count
all_short_nonvf += short_nonvf
all_short_vf_closeness += short_vf_closeness_count
all_short_nonvf_closeness += short_nonvf_closeness
results.append(vf_count / float(len(all_path)))
results3.append(vf_closeness_count / float(len(all_path)))
if len(all_path) > 1: results2.append(vf_count / float(len(all_path)))
long_results.append(long_vf_count / float(len(long_path)))
long_results3.append(long_vf_closeness_count / float(len(long_path)))
if len(long_path) > 1: long_results2.append(long_vf_count / float(len(long_path)))
if len(short_path) > 0:
short_results.append(short_vf_count / float(len(short_path)))
short_results3.append(short_vf_closeness_count / float(len(short_path)))
else:
pass
# short_results.append(0)
# short_results3.append(0)
if len(short_path) > 1: short_results2.append(short_vf_count / float(len(short_path)))
# save mofified meta
meta_mod = [x for x in meta_map.itervalues()]
helpers.save_to_json(out, meta_mod)
# print results
print 'ALL'
print 'VF count: %d' % all_vf
print 'VF CLOSENESS count: %d' % all_vf_closeness
print 'Non vf count: %d' % all_nonvf
print 'Non vf CLOSENESS count: %d' % all_nonvf_closeness
print 'VF perc: %f' % (all_vf/float(all_vf + all_nonvf))
print 'VF CLOSENESS perc: %f' % (all_vf_closeness/float(all_vf_closeness + all_nonvf_closeness))
print 'Mean VF prob: %f' % np.mean(results)
print 'Mean VF CLOSENESS prob: %f' % np.mean(results3)
print 'Mean VF2 prob: %f' % np.mean(results2)
print '=========='
print 'SHORT'
print 'VF count: %d' % all_short_vf
print 'VF CLOSENESS count: %d' % all_short_vf_closeness
print 'Non vf count: %d' % all_short_nonvf
print 'Non vf CLOSENESS count: %d' % all_short_nonvf_closeness
if all_short_vf + all_short_nonvf > 0:
print 'VF perc: %f' % (all_short_vf/float(all_short_vf + all_short_nonvf))
if all_short_vf_closeness + all_short_nonvf_closeness > 0:
print 'VF CLOSENESS perc: %f' % (all_short_vf_closeness/float(all_short_vf_closeness + all_short_nonvf_closeness))
print 'Mean VF prob: %f' % np.mean(short_results)
print 'Mean VF CLOSENESS prob: %f' % np.mean(short_results3)
print 'Mean VF2 prob: %f' % np.mean(short_results2)
print '=-----------------'
print 'LONG'
print 'VF count: %d' % all_long_vf
print 'VF CLOSENESS count: %d' % all_long_vf_closeness
print 'Non vf count: %d' % all_long_nonvf
print 'Non vf CLOSENESS count: %d' % all_long_nonvf_closeness
print 'VF perc: %f' % (all_long_vf/float(all_long_vf + all_long_nonvf))
print 'VF CLOSENESS perc: %f' % (all_long_vf_closeness/float(all_long_vf_closeness + all_long_nonvf_closeness))
print 'Mean VF prob: %f' % np.mean(long_results)
print 'Mean VF CLOSENESS prob: %f' % np.mean(long_results3)
print 'Mean VF2 prob: %f' % np.mean(long_results2)
def purify(g,
meta_original,
out,
count=1000,
try_per_race=1,
show_progress=False,
with_lp=True):
empty = 0
# remove traces with already calculated random paths
logger.warn('[r]ONLY NOT FILLED PATHS[/]')
meta_filled = [
x for x in meta_original if helpers.RANDOM_WALK_RUN_COUNT not in x
]
# Filter if interested only in routes of stretch 1
# meta_filled = [x for x in meta_original
# if x[helpers.TRACE_LEN]-x[helpers.SH_LEN] == 1]
## traces with a maximum stretch
# logger.warn('[r]!!!ONLY WITH STRETCH[/]')
# meta = [x for x in meta if x[helpers.STRETCH] > -1]
# # shorter meta records
# logger.warn('[r]!!!ONLY SHORT TRACES[/]')
# meta = [x for x in meta if len(x[helpers.TRACE]) < 5]
# meta_map = {tuple(x[helpers.TRACE]): x for x in meta_filled}
logger.info('All trace count: %d' % len(meta_filled))
tr_count = min(len(meta_filled), count)
meta_random = random.sample(meta_filled, tr_count)
logger.info('Chosen subset count: %d' % len(meta_random))
# real_vf_degree = [x for x in meta_random if x[helpers.IS_VF_DEGREE] == 1]
# real_nonvf_degree = [x for x in meta_random if x[helpers.IS_VF_DEGREE] == 0]
# assert len(real_nonvf_degree) == tr_count - len(real_vf_degree)
# real_vf_prelabeled = [x for x in meta_random if x[helpers.IS_VF_PRELABELED] == 1]
# real_nonvf_prelabeled = [x for x in meta_random if x[helpers.IS_VF_PRELABELED] == 0]
# assert len(real_nonvf_prelabeled) == tr_count - len(real_vf_prelabeled)
# real_vf_closeness = [x for x in meta_random if x[helpers.IS_VF_CLOSENESS] == 1]
# real_nonvf_closeness = [x for x in meta_random if x[helpers.IS_VF_CLOSENESS] == 0]
# assert len(real_nonvf_closeness) == tr_count - len(real_vf_closeness)
# logger.info('Real vf degree: %f[%d]' % ((len(real_vf_degree) / float(tr_count),
# len(real_vf_degree))))
# logger.info('Real nonvf degree: %f[%d]' % ((len(real_nonvf_degree) / float(tr_count),
# len(real_nonvf_degree))))
# logger.info('Real vf prelabeled: %f[%d]' % ((len(real_vf_prelabeled) / float(tr_count),
# len(real_vf_prelabeled))))
# logger.info('Real nonvf prelabeled: %f[%d]' % ((len(real_nonvf_prelabeled) / float(tr_count),
# len(real_nonvf_prelabeled))))
# logger.info('Real vf closeness: %f[%d]' % ((len(real_vf_closeness)/float(tr_count), len(real_vf_closeness))))
# logger.info('Real nonvf closeness: %f[%d]' % ((len(real_nonvf_closeness)/float(tr_count), len(real_nonvf_closeness))))
# traceroutes = [x[helpers.TRACE] for x in meta_random]
# traceroutes = vft.trace_in_vertex_id(g, traceroutes)
try:
meta_random[0][helpers.TRACE]
except Exception:
meta_random = [{helpers.TRACE: x} for x in meta_random]
progress = progressbar1.DummyProgressBar(end=10, width=15)
if show_progress:
progress = progressbar1.AnimatedProgressBar(end=len(meta_random),
width=15)
stretch_list = []
max_stretch = max(
[x[helpers.TRACE_LEN] - x[helpers.SH_LEN] for x in meta_random])
for stretch in range(0, max_stretch + 1):
metas = [
x for x in meta_random
if x[helpers.TRACE_LEN] - x[helpers.SH_LEN] == stretch
]
stretch_list.extend(list(repeat(stretch, len(metas))))
# print(stretch_list)
lenghts = random.shuffle(stretch_list)
strx_array = []
for idx, trace_meta in enumerate(meta_random):
progress += 1
progress.show_progress()
# print(trace_meta[helpers.TRACE])
shl = trace_meta[helpers.SH_LEN]
trace = vft.trace_in_vertex_id(g, [
trace_meta[helpers.TRACE],
])
if len(trace) != 1:
print 'PROBLEM'
print trace_meta
continue
trace = trace[0]
# print(trace)
random_walk_closeness_route_vf = 0
random_walk_closeness_route_lp_soft = 0
random_walk_closeness_route_lp_hard = 0
random_walk_degree_route_vf = 0
random_walk_degree_route_lp_soft = 0
random_walk_degree_route_lp_hard = 0
random_walk_prelabeled_route_vf = 0
random_walk_prelabeled_route_lp_soft = 0
random_walk_prelabeled_route_lp_hard = 0
s, t = trace[0], trace[-1]
for counter in xrange(0, try_per_race):
# random_path = helpers.random_route_walk(g, s, t, len(trace)) # Modified
random_path = helpers.random_route_walk(
g, s, t, shl + stretch_list[idx]) # Modified
if len(random_path) == 0:
empty += 1
if vft.is_valley_free(g, random_path, vfmode=vft.CLOSENESS):
random_walk_closeness_route_vf += 1
if (len(random_path) == shl + 1):
strx_array.append(1)
if with_lp:
lp_soft = vft.is_local_preferenced(g,
random_path,
first_edge=True,
vfmode=vft.CLOSENESS)
lp_hard = vft.is_local_preferenced(g,
random_path,
first_edge=False,
vfmode=vft.CLOSENESS)
if lp_soft:
random_walk_closeness_route_lp_soft += 1
if lp_hard:
random_walk_closeness_route_lp_hard += 1
else:
if (len(random_path) == shl + 1):
strx_array.append(0)
# if vft.is_valley_free(g, random_path, vfmode=vft.DEGREE):
# random_walk_degree_route_vf += 1
# if with_lp:
# lp_soft = vft.is_local_preferenced(g, random_path,
# first_edge=True,
# vfmode=vft.DEGREE)
# lp_hard = vft.is_local_preferenced(g, random_path,
# first_edge=False,
# vfmode=vft.DEGREE)
# if lp_soft:
# random_walk_degree_route_lp_soft += 1
# if lp_hard:
# random_walk_degree_route_lp_hard += 1
# if vft.is_valley_free(g, random_path, vfmode=vft.PRELABELED):
# random_walk_prelabeled_route_vf += 1
# if with_lp:
# lp_soft = vft.is_local_preferenced(g, random_path,
# first_edge=True,
# vfmode=vft.PRELABELED)
# lp_hard = vft.is_local_preferenced(g, random_path,
# first_edge=False,
# vfmode=vft.PRELABELED)
# if lp_soft:
# random_walk_prelabeled_route_lp_soft += 1
# if lp_hard:
# random_walk_prelabeled_route_lp_hard += 1
# sanity check
# if random_path[0] != s or random_path[-1] != t:
# logger.error('ALERT')
if len(random_path) != len(set(random_path)):
logger.error('LENGTH ERROR')
extra_meta = {
helpers.RANDOM_WALK_RUN_COUNT:
try_per_race,
helpers.RANDOM_WALK_VF_CLOSENESS_ROUTE:
random_walk_closeness_route_vf,
helpers.RANDOM_WALK_VF_DEGREE_ROUTE:
random_walk_degree_route_vf,
helpers.RANDOM_WALK_VF_PRELABELED_ROUTE:
random_walk_prelabeled_route_vf,
}
if with_lp:
extra_meta.update({
helpers.RANDOM_WALK_LP_SOFT_CLOSENESS_ROUTE:
random_walk_closeness_route_lp_soft,
helpers.RANDOM_WALK_LP_HARD_CLOSENESS_ROUTE:
random_walk_closeness_route_lp_hard,
helpers.RANDOM_WALK_LP_SOFT_DEGREE_ROUTE:
random_walk_degree_route_lp_soft,
helpers.RANDOM_WALK_LP_HARD_DEGREE_ROUTE:
random_walk_degree_route_lp_hard,
helpers.RANDOM_WALK_LP_SOFT_PRELABELED_ROUTE:
random_walk_prelabeled_route_lp_soft,
helpers.RANDOM_WALK_LP_HARD_PRELABELED_ROUTE:
random_walk_prelabeled_route_lp_hard
})
trace_meta.update(extra_meta)
## save modified meta
# all meta_* get only references from meta_original
helpers.save_to_json(out, meta_random)
# meta_mod = [x for x in meta_map.itervalues()]
# helpers.save_to_json(out, meta_mod)
# calculate results
# real_vf = [x[helpers.IS_VF_CLOSENESS] for x in meta_random]
# real_vf_ratio = np.mean(real_vf)
random_walk_vf_ratio_per_element = [
x[helpers.RANDOM_WALK_VF_CLOSENESS_ROUTE] /
x[helpers.RANDOM_WALK_RUN_COUNT] for x in meta_random
]
random_walk_vf_ratio = np.mean(random_walk_vf_ratio_per_element)
# print results
logger.info('')
logger.info('Empty: %d' % empty)
logger.info('Tested trace count: %d' % len(meta_random))
# logger.info('VF ratio in tested traces: %f' % real_vf_ratio)
logger.info('VF ratio in random walks: %f' % random_walk_vf_ratio)
logger.info('VF ratio in random walks for path stretch 1: %f' %
np.mean(strx_array))
(len_component, (len(all_long_paths) - len(component_paths))))
logger.debug(
'REAL: %f[%d/%d] ALL: %f[%d/%d]' %
(np.mean(real_path_vf), sum(real_path_vf), len(real_path_vf),
np.mean(all_path_vf), sum(all_path_vf), len(all_path_vf)))
real_connected_pairs_pertime.append(active_pair_counter)
trace_count_per_pontpair_pertime.append(trace_count_per_pontpair)
vf_trace_count_per_pontpair_pertime.append(vf_trace_count_per_pontpair)
top_nodes_trace_pertime.append(len(current_top_nodes))
current_top_nodes = set(current_top_nodes)
top_nodes_pertime.append(len(current_top_nodes))
# logger.info('Trace count: %d' % len(user_traces))
helpers.save_to_json('traces_user_SQ8t7_%d.json' % user, user_traces)
# print 'saved %s' % user
# np.savetxt('user-small%s.csv' % user, pair_activity_matrix, delimiter=';')
trace_count_per_pontpair_pertime = np.matrix(
trace_count_per_pontpair_pertime)
vf_trace_count_per_pontpair_pertime = np.matrix(
vf_trace_count_per_pontpair_pertime)
print
tmp = [
100 * x / float(len(functionally_connected_pairs))
if len(functionally_connected_pairs) > 0 else 0
for x in real_connected_pairs_pertime
]
traceroutes = foodweb.get_traceroutes(arguments.traceroutes_input)
elif arguments.type == Networks.weibo:
pass
elif arguments.type == Networks.text:
traceroutes = text.get_traceroutes(arguments.traceroutes_input)
elif arguments.type == Networks.wiki:
traceroutes = wiki.get_traceroutes(arguments.traceroutes_input)
elif arguments.type == Networks.metabolic:
traceroutes = metabolic.get_traceroutes(arguments.traceroutes_input)
elif arguments.type == Networks.wordnavi:
traceroutes = wordnavi.get_traceroutes(arguments.traceroutes_input)
else:
raise RuntimeError('Unknown network type')
msg = 'Save traceroutes to {trace}'.format(trace=arguments.json_traces)
logger.info(msg)
helpers.save_to_json(arguments.json_traces, traceroutes)
if "network" in arguments.convert:
# label network with caida labeling tool
# first load previously converted traceroutes
traceroutes = helpers.load_from_json(arguments.json_traces)
logger.info('Trace count: {c}'.format(c=len(traceroutes)))
# to increase accuracy
if arguments.type == Networks.airport:
traceroutes.extend([[y for y in reversed(x)] for x in traceroutes])
# convert with caida labeling tools
logger.info('Caida labeling the graph')
edge_list = helpers.caida_labeling(arguments.caida_folder, traceroutes,
arguments.network_cliques)
def main():
parser = argparse.ArgumentParser(
description=('Syntetic route generator'),
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--progressbar', action='store_true')
parser.add_argument('--verbose', '-v', action='count', default=0)
parser.add_argument('network')
parser.add_argument('meta')
parser.add_argument('all_trace_out', metavar='all-trace-out')
parser.add_argument('syntetic_out', metavar='syntetic-out')
parser.add_argument('--trace-count',
'-tc',
type=int,
dest='trace_count',
default=5000)
parser.add_argument('--random-sample',
dest='random_sample',
action='store_true')
parser.add_argument('--closeness-error',
'-ce',
type=float,
dest='closeness_error',
default=0.0)
parser.add_argument('--core-limit-percentile',
'-cl',
type=int,
dest='core_limit',
default=0)
parser.add_argument('--toggle-node-error-mode', action='store_true')
arguments = parser.parse_args()
arguments.verbose = min(len(helpers.LEVELS) - 1, arguments.verbose)
logging.getLogger('compnet').setLevel(helpers.LEVELS[arguments.verbose])
show_progress = arguments.progressbar
g = helpers.load_network(arguments.network)
g = g.simplify()
meta = helpers.load_from_json(arguments.meta)
if arguments.random_sample:
random.shuffle(meta)
meta = meta[:arguments.trace_count]
N = g.vcount()
cl = sorted([x['closeness'] for x in g.vs], reverse=True)
logger.info('Min closeness: %s' % np.min(cl))
logger.info('Max closeness: %s' % np.max(cl))
logger.info('Mean closenss: %s' % np.mean(cl))
logger.info('10%% closeness: %s' % np.percentile(cl, 10))
logger.info('90%% closeness: %s' % np.percentile(cl, 90))
logger.info('Core limit: [r]%d%%[/]' % arguments.core_limit)
change_probability = 100 * arguments.closeness_error
logger.info('Change probability: [r]%6.2f%%[/]' % change_probability)
core_limit = np.percentile(cl, arguments.core_limit)
logger.info('Core limit in closeness: [bb]%f[/]' % core_limit)
if arguments.toggle_node_error_mode:
logger.info('[r]Node error mode[/]')
msg = (
"If given node's closeness >= core_limit then the new ",
"closeness in this node is ",
#"rand(closeness_error ... old closeness)"
"OLD_CLOSENSS * +/- closeness_error%")
logger.info(''.join(msg))
logger.info('Minimum node closeness: [g]%f[/]' %
arguments.closeness_error)
for n in g.vs:
if n['closeness'] < core_limit:
continue
# sign = -1 if random.uniform(-1, 1) < 0 else 1
# n['closeness'] = n['closeness'] * (1 + sign * arguments.closeness_error)
new_closeness = random.uniform(arguments.closeness_error,
n['closeness'])
n['closeness'] = new_closeness
g_labeled = vft.label_graph_edges(g, vfmode=vft.CLOSENESS)
peer_edge_count = len([x for x in g_labeled.es if x['dir'] == LinkDir.P])
logger.info('Peer edge count: %d' % peer_edge_count)
changed_edges = []
if not arguments.toggle_node_error_mode:
msg = ("If the closeness values of the endpoints in given edge is ",
"larger than the core_limit and ",
"random(0,1) < closeness_error then change the direction ",
"for this edge")
logger.info(''.join(msg))
changed_u = changed_d = 0
changed_edges = []
changed_edgess = []
for edge in g_labeled.es:
s, t = edge.source, edge.target
s_cl = g_labeled.vs[s]['closeness']
t_cl = g_labeled.vs[t]['closeness']
if (s_cl < core_limit or t_cl < core_limit): continue
if random.uniform(0, 1) > arguments.closeness_error: continue
# if abs(s_cl - t_cl) / min(s_cl, t_cl) > 0.02: continue
new_edge_dir = LinkDir.U if random.uniform(0,
1) > 0.5 else LinkDir.D
if new_edge_dir != edge['dir']:
if edge['dir'] == LinkDir.U:
changed_u += 1
else:
changed_d += 1
edge['dir'] = new_edge_dir
changed_edges.append(edge)
changed_edgess.append((edge.source, edge.target))
# if edge['dir'] == LinkDir.U:
# changed_u += 1
# changed_edgess.append((edge.source, edge.target))
# edge['dir'] = LinkDir.D
# changed_edges.append(edge)
# elif edge['dir'] == LinkDir.D:
# changed_d += 1
# changed_edgess.append((edge.source, edge.target))
# edge['dir'] = LinkDir.U
# changed_edges.append(edge)
logger.info('E count: %d' % g_labeled.ecount())
logger.info('Changed U: %d' % changed_u)
logger.info('Changed D: %d' % changed_d)
logger.info('Changed: %d' % (changed_d + changed_u))
changed_e = [(g_labeled.vs[e.source]['name'],
g_labeled.vs[e.target]['name']) for e in changed_edges]
changed_e = changed_e + [(x[1], x[0]) for x in changed_e]
changed_e = set(changed_e)
vf_g_closeness = vft.convert_to_vf(g,
vfmode=vft.CLOSENESS,
labeled_graph=g_labeled)
# e_colors = []
# for e in vf_g_closeness.es:
# if e.source < N and e.target < N: col = 'grey'
# elif e.source < N and e.target >= N: col = 'blue'
# elif e.source >= N and e.target >= N: col = 'red'
# else: col = 'cyan'
# e_colors.append(col)
# igraph.plot(vf_g_closeness, "/tmp/closeness.pdf",
# vertex_label=vf_g_closeness.vs['name'],
# vertex_size=0.2,
# edge_color=e_colors)
pairs = [(g.vs.find(x[helpers.TRACE][0]).index,
g.vs.find(x[helpers.TRACE][-1]).index, tuple(x[helpers.TRACE]))
for x in meta]
# pairs = list(set(pairs))
# random.shuffle(pairs)
# visited = set()
# pairs2 = []
# for x in pairs:
# k = (x[0], x[1])
# if k in visited: continue
# visited.add(k)
# visited.add((k[1], k[0]))
# pairs2.append(x)
# pairs = pairs2
traces = [x[2] for x in pairs]
stretches = []
syntetic_traces = []
sh_traces = []
base_traces = []
original_traces = []
bad = 0
progress = progressbar1.DummyProgressBar(end=10, width=15)
if show_progress:
progress = progressbar1.AnimatedProgressBar(end=len(pairs), width=15)
for s, t, trace_original in pairs:
progress += 1
progress.show_progress()
trace_original_idx = [g.vs.find(x).index for x in trace_original]
logger.debug('Original trace: %s -- %s -- %s',
[g.vs[x]['name'] for x in trace_original_idx],
vft.trace_to_string(g, trace_original_idx, vft.CLOSENESS),
[g.vs[x]['closeness'] for x in trace_original_idx])
sh_routes = g.get_all_shortest_paths(s, t)
sh_len = len(sh_routes[0])
sh_routes_named = [[g.vs[y]['name'] for y in x] for x in sh_routes]
sh_trace_name = random.choice(sh_routes_named)
base_trace_name = random.choice(sh_routes_named)
candidates = vf_g_closeness.get_all_shortest_paths(s + N, t + N)
candidates = [vft.vf_route_converter(x, N) for x in candidates]
# candidates = []
if len(candidates) == 0:
candidates = vft.get_shortest_vf_route(g_labeled,
s,
t,
mode='vf',
vf_g=vf_g_closeness,
_all=True,
vfmode=vft.CLOSENESS)
if len(candidates) == 0:
s_name, t_name = g.vs[s]['name'], g.vs[t]['name']
logger.debug("!!!No syntetic route from %s to %s" %
(s_name, t_name))
continue
logger.debug('Candidates from %s to %s:' %
(g.vs[s]['name'], g.vs[t]['name']))
for c in candidates:
logger.debug('%s -- %s -- %s' %
([g.vs[x]['name'] for x in c],
vft.trace_to_string(g_labeled, c, vft.PRELABELED),
[g.vs[x]['closeness'] for x in c]))
chosen_one = random.choice(candidates)
chosen_one_name = [g.vs[x]['name'] for x in chosen_one]
# print chosen_one
# print trace_original
# pretty_plotter.pretty_plot(g, trace_original_idx,
# chosen_one, changed_edgess,
# spec_color=(0, 0, 0, 155))
hop_stretch = len(chosen_one) - sh_len
stretches.append(hop_stretch)
trace_original_e = zip(trace_original, trace_original[1:])
chosen_one_e = zip(chosen_one_name, chosen_one_name[1:])
trace_affected = any([x in changed_e for x in trace_original_e])
chosen_affected = any([x in changed_e for x in chosen_one_e])
logger.debug('Trace affected: %s' % trace_affected)
logger.debug('Chosen affected: %s' % chosen_affected)
# if hop_stretch > 2:
# logger.debug('Base: %s' % trace_to_string(g_labeled, base_trace_name))
# logger.debug('SH: %s' % trace_to_string(g_labeled, sh_trace_name))
# logger.debug('Trace: %s' % trace_to_string(g_labeled, trace_original))
# logger.debug('Syntetic: %s' % trace_to_string(g_labeled, chosen_one_name))
if trace_affected or chosen_affected or hop_stretch > 2:
# pretty_plotter.pretty_plot_all(g, traces,
# chosen_one, changed_edgess,
# spec_color=(0, 0, 0, 255))
bad += 1
syntetic_traces.append(chosen_one_name)
sh_traces.append(sh_trace_name)
base_traces.append(base_trace_name)
original_traces.append(trace_original)
logger.debug('From %s to %s chosen one %s' %
(g.vs[s]['name'], g.vs[t]['name'], chosen_one_name))
result = zip(base_traces, sh_traces, original_traces, syntetic_traces)
helpers.save_to_json(arguments.all_trace_out, result)
helpers.save_to_json(arguments.syntetic_out, syntetic_traces)
print 'Bad: %d' % bad
c = collections.Counter(stretches)
trace_count = len(syntetic_traces)
logger.info('Stretch dist:')
for k in c:
logger.info('\t%d: %5.2f%%[%d]' %
(k, 100 * c[k] / float(trace_count), c[k]))
logger.info('Valid route count: %d' % trace_count)
logger.info('Route count parameter: %d' % arguments.trace_count)
logger.info('Generated valid pair count: %d' % len(pairs))
def purify(g,
meta_original,
out,
count=1000,
try_per_race=1,
show_progress=False):
empty = 0
# remove traces with already calculated random paths
logger.warn('[r]ONLY NOT FILLED PATHS[/]')
meta_filled = [
x for x in meta_original
if helpers.RANDOM_NONVF_WALK_RUN_COUNT not in x
]
logger.info('All trace count: %d' % len(meta_filled))
tr_count = min(len(meta_filled), count)
meta_random = random.sample(meta_filled, tr_count)
logger.info('Chosen subset count: %d' % len(meta_random))
real_vf_degree = [x for x in meta_random if x[helpers.IS_VF_DEGREE] == 1]
real_nonvf_degree = [
x for x in meta_random if x[helpers.IS_VF_DEGREE] == 0
]
assert len(real_nonvf_degree) == tr_count - len(real_vf_degree)
real_vf_prelabeled = [
x for x in meta_random if x[helpers.IS_VF_PRELABELED] == 1
]
real_nonvf_prelabeled = [
x for x in meta_random if x[helpers.IS_VF_PRELABELED] == 0
]
assert len(real_nonvf_prelabeled) == tr_count - len(real_vf_prelabeled)
real_vf_closeness = [
x for x in meta_random if x[helpers.IS_VF_CLOSENESS] == 1
]
real_nonvf_closeness = [
x for x in meta_random if x[helpers.IS_VF_CLOSENESS] == 0
]
assert len(real_nonvf_closeness) == tr_count - len(real_vf_closeness)
logger.info('Real vf degree: %f[%d]' %
((len(real_vf_degree) / float(tr_count), len(real_vf_degree))))
logger.info(
'Real nonvf degree: %f[%d]' %
((len(real_nonvf_degree) / float(tr_count), len(real_nonvf_degree))))
logger.info(
'Real vf prelabeled: %f[%d]' %
((len(real_vf_prelabeled) / float(tr_count), len(real_vf_prelabeled))))
logger.info('Real nonvf prelabeled: %f[%d]' %
((len(real_nonvf_prelabeled) / float(tr_count),
len(real_nonvf_prelabeled))))
logger.info(
'Real vf closeness: %f[%d]' %
((len(real_vf_closeness) / float(tr_count), len(real_vf_closeness))))
logger.info('Real nonvf closeness: %f[%d]' %
((len(real_nonvf_closeness) / float(tr_count),
len(real_nonvf_closeness))))
progress = progressbar1.DummyProgressBar(end=10, width=15)
if show_progress:
progress = progressbar1.AnimatedProgressBar(end=len(meta_random),
width=15)
for trace_meta in meta_random:
progress += 1
progress.show_progress()
trace = vft.trace_in_vertex_id(g, [
trace_meta[helpers.TRACE],
])
if len(trace) != 1:
logger.error('PROBLEM')
logger.error('%s' % trace_meta)
continue
trace = trace[0]
random_nonvf_walk_closeness_route_count = 0
random_nonvf_walk_closeness_route_len = []
random_nonvf_walk_degree_route_count = 0
random_nonvf_walk_degree_route_len = []
random_nonvf_walk_prelabeled_route_count = 0
random_nonvf_walk_prelabeled_route_len = []
random_nonvf_walk_lp_soft_closeness_route_count = 0
random_nonvf_walk_lp_soft_degree_route_count = 0
random_nonvf_walk_lp_soft_prelabeled_route_count = 0
random_nonvf_walk_lp_hard_closeness_route_count = 0
random_nonvf_walk_lp_hard_degree_route_count = 0
random_nonvf_walk_lp_hard_prelabeled_route_count = 0
s, t = trace[0], trace[-1]
for counter in xrange(0, try_per_race):
isvf, random_path = helpers.random_nonvf_route(
g, s, t, len(trace), vfmode=vft.CLOSENESS)
assert len(random_path) > 0
if isvf:
random_nonvf_walk_closeness_route_count += 1
lp_soft = vft.is_local_preferenced(g,
trace,
first_edge=True,
vfmode=vft.CLOSENESS)
lp_hard = vft.is_local_preferenced(g,
trace,
first_edge=False,
vfmode=vft.CLOSENESS)
if lp_soft:
random_nonvf_walk_lp_soft_closeness_route_count += 1
if lp_hard:
random_nonvf_walk_lp_hard_closeness_route_count += 1
random_nonvf_walk_closeness_route_len.append(len(random_path))
isvf, random_path = helpers.random_nonvf_route(g,
s,
t,
len(trace),
vfmode=vft.DEGREE)
assert len(random_path) > 0
if isvf:
random_nonvf_walk_degree_route_count += 1
lp_soft = vft.is_local_preferenced(g,
trace,
first_edge=True,
vfmode=vft.DEGREE)
lp_hard = vft.is_local_preferenced(g,
trace,
first_edge=False,
vfmode=vft.DEGREE)
if lp_soft:
random_nonvf_walk_lp_soft_degree_route_count += 1
if lp_hard:
random_nonvf_walk_lp_hard_degree_route_count += 1
random_nonvf_walk_degree_route_len.append(len(random_path))
isvf, random_path = helpers.random_nonvf_route(
g, s, t, len(trace), vfmode=vft.PRELABELED)
assert len(random_path) > 0
if isvf:
random_nonvf_walk_prelabeled_route_count += 1
lp_soft = vft.is_local_preferenced(g,
trace,
first_edge=True,
vfmode=vft.PRELABELED)
lp_hard = vft.is_local_preferenced(g,
trace,
first_edge=False,
vfmode=vft.PRELABELED)
if lp_soft:
random_nonvf_walk_lp_soft_prelabeled_route_count += 1
if lp_hard:
random_nonvf_walk_lp_hard_prelabeled_route_count += 1
random_nonvf_walk_prelabeled_route_len.append(len(random_path))
# sanity check
# if random_path[0] != s or random_path[-1] != t:
# logger.error('ALERT')
if len(random_path) != len(set(random_path)):
logger.error('LENGTH ERROR')
extra_meta = {
helpers.RANDOM_NONVF_WALK_RUN_COUNT:
try_per_race,
helpers.RANDOM_NONVF_WALK_VF_CLOSENESS_ROUTE:
random_nonvf_walk_closeness_route_count,
helpers.RANDOM_NONVF_WALK_VF_CLOSENESS_ROUTE_LEN:
random_nonvf_walk_closeness_route_len,
helpers.RANDOM_NONVF_WALK_VF_DEGREE_ROUTE:
random_nonvf_walk_degree_route_count,
helpers.RANDOM_NONVF_WALK_VF_DEGREE_ROUTE_LEN:
random_nonvf_walk_degree_route_len,
helpers.RANDOM_NONVF_WALK_VF_PRELABELED_ROUTE:
random_nonvf_walk_prelabeled_route_count,
helpers.RANDOM_NONVF_WALK_VF_PRELABELED_ROUTE_LEN:
random_nonvf_walk_prelabeled_route_len,
helpers.RANDOM_NONVF_WALK_LP_SOFT_DEGREE_ROUTE:
random_nonvf_walk_lp_soft_degree_route_count,
helpers.RANDOM_NONVF_WALK_LP_SOFT_CLOSENESS_ROUTE:
random_nonvf_walk_lp_soft_closeness_route_count,
helpers.RANDOM_NONVF_WALK_LP_SOFT_PRELABELED_ROUTE:
random_nonvf_walk_lp_soft_prelabeled_route_count,
helpers.RANDOM_NONVF_WALK_LP_HARD_DEGREE_ROUTE:
random_nonvf_walk_lp_hard_degree_route_count,
helpers.RANDOM_NONVF_WALK_LP_HARD_CLOSENESS_ROUTE:
random_nonvf_walk_lp_hard_closeness_route_count,
helpers.RANDOM_NONVF_WALK_LP_HARD_PRELABELED_ROUTE:
random_nonvf_walk_lp_hard_prelabeled_route_count
}
trace_meta.update(extra_meta)
## save modified meta
# all meta_* get only references from meta_original
helpers.save_to_json(out, meta_original)
# calculate results
real_vf = [x[helpers.IS_VF_CLOSENESS] for x in meta_random]
real_vf_ratio = np.mean(real_vf)
random_nonvf_walk_vf_ratio_per_element = [
x[helpers.RANDOM_NONVF_WALK_VF_CLOSENESS_ROUTE] /
x[helpers.RANDOM_NONVF_WALK_RUN_COUNT] for x in meta_random
]
random_nonvf_walk_vf_ratio = np.mean(
random_nonvf_walk_vf_ratio_per_element)
# print results
logger.info('')
logger.info('Empty: %d' % empty)
logger.info('Tested trace count: %d' % len(meta_random))
logger.info('VF ratio in tested traces: %f' % real_vf_ratio)
logger.info('VF ratio in random walks: %f' % random_nonvf_walk_vf_ratio)