def main():
parser = argparse.ArgumentParser()
parser.add_argument('graph_file')
parser.add_argument('embeddings_file')
parser.add_argument('--deg', action='store_true', help='angle in degrees')
parser.add_argument('--cda', action='store_true', help='correct directed arcs')
parser.add_argument('--cda-max-vertices', type=int, default=1000)
parser.add_argument('--gr', action='store_true', help='evaluate greedy routing')
parser.add_argument('--gr-max-pairs', type=int, default=10000)
args = parser.parse_args()
vertices, edges = read_graph_from_file(args.graph_file)
skip_lines=0
if args.deg:
skip_lines=2
embeddings = read_embeddings_from_file(args.embeddings_file, skip_lines, degrees=args.deg)
print "Evaluate embeddings"
report = evaluate_embeddings(embeddings, edges,
cda=args.cda, cda_max_vertices=args.cda_max_vertices,
greedy_routing=args.gr, gr_max_pairs=args.gr_max_pairs
)
print 'report'
for name, value in report:
print '{}: {}'.format(name, value)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('graph_file')
parser.add_argument('out_prefix')
parser.add_argument(
'--mode',
default='fit_degrees_sgd',
help='random|degrees|fit|fit_random|fit_degrees|fit_degrees_sgd')
parser.add_argument('--learning-rate',
default=0.1,
help='learning rate for fit_degrees_sgd',
type=float)
parser.add_argument('--n-epoch',
default=100,
help='number of training epoch for fit_degrees_sgd',
type=int)
parser.add_argument('--ratio-to-second',
default=2.,
help='ratio of nedges to second neighbour',
type=float)
parser.add_argument('--ratio-between-first',
default=1.,
help='ratio of nedges between first neighbours',
type=float)
parser.add_argument('--ratio-random',
default=1.,
help='ratio of random nedges',
type=float)
parser.add_argument('--silent', action='store_true')
args = parser.parse_args()
vertices, edges = read_graph_from_file(args.graph_file)
n = len(vertices)
print "Number of vertices: {}".format(n)
print "Number of edges: {}".format(len(edges))
print "Number of non-edges: {}".format(n * (n - 1) / 2 - len(edges))
print "Find embeddings"
embeddings, info = find_embeddings(
vertices,
edges,
mode=args.mode,
learning_rate=args.learning_rate,
n_epoch=args.n_epoch,
ratio_to_second=args.ratio_to_second,
ratio_between_first=args.ratio_between_first,
ratio_random=args.ratio_random,
silent=args.silent)
with open(args.out_prefix + '-embeddings.txt', 'w') as of:
for v in embeddings.keys():
r, phi = embeddings[v]
of.write('\t'.join(map(str, [v, r, phi])) + '\n')
core = info['core']
if core is not None:
with open(args.out_prefix + '-core.txt', 'w') as of_core:
for v1, v2 in core:
of_core.write('\t'.join(map(str, [v1, v2])) + '\n')
def main():
parser = argparse.ArgumentParser()
parser.add_argument("graph_file")
parser.add_argument("out_prefix")
parser.add_argument(
"--mode", default="fit_degrees_sgd", help="random|degrees|fit|fit_random|fit_degrees|fit_degrees_sgd"
)
parser.add_argument("--learning-rate", default=0.1, help="learning rate for fit_degrees_sgd", type=float)
parser.add_argument("--n-epoch", default=100, help="number of training epoch for fit_degrees_sgd", type=int)
parser.add_argument("--ratio-to-second", default=2.0, help="ratio of nedges to second neighbour", type=float)
parser.add_argument(
"--ratio-between-first", default=1.0, help="ratio of nedges between first neighbours", type=float
)
parser.add_argument("--ratio-random", default=1.0, help="ratio of random nedges", type=float)
parser.add_argument("--silent", action="store_true")
args = parser.parse_args()
vertices, edges = read_graph_from_file(args.graph_file)
n = len(vertices)
print "Number of vertices: {}".format(n)
print "Number of edges: {}".format(len(edges))
print "Number of non-edges: {}".format(n * (n - 1) / 2 - len(edges))
print "Find embeddings"
embeddings, info = find_embeddings(
vertices,
edges,
mode=args.mode,
learning_rate=args.learning_rate,
n_epoch=args.n_epoch,
ratio_to_second=args.ratio_to_second,
ratio_between_first=args.ratio_between_first,
ratio_random=args.ratio_random,
silent=args.silent,
)
with open(args.out_prefix + "-embeddings.txt", "w") as of:
for v in embeddings.keys():
r, phi = embeddings[v]
of.write("\t".join(map(str, [v, r, phi])) + "\n")
core = info["core"]
if core is not None:
with open(args.out_prefix + "-core.txt", "w") as of_core:
for v1, v2 in core:
of_core.write("\t".join(map(str, [v1, v2])) + "\n")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('graph_file')
parser.add_argument('embeddings_file')
parser.add_argument('--deg', action='store_true', help='angle in degrees')
parser.add_argument('--cda',
action='store_true',
help='correct directed arcs')
parser.add_argument('--cda-max-vertices', type=int, default=1000)
parser.add_argument('--gr',
action='store_true',
help='evaluate greedy routing')
parser.add_argument('--gr-max-pairs', type=int, default=10000)
parser.add_argument('--core', action='store_true', help='evaluate core')
parser.add_argument('--core-exponent',
type=float,
help='core exponent',
default=0.5)
parser.add_argument('--seed', type=int, help='random seed', default=42)
args = parser.parse_args()
np.random.seed(args.seed)
vertices, edges = read_graph_from_file(args.graph_file)
skip_lines = 0
if args.deg:
skip_lines = 2
embeddings = read_embeddings_from_file(args.embeddings_file,
skip_lines,
degrees=args.deg)
print "Evaluate embeddings"
report = evaluate_embeddings(embeddings,
edges,
cda=args.cda,
cda_max_vertices=args.cda_max_vertices,
greedy_routing=args.gr,
gr_max_pairs=args.gr_max_pairs,
eval_core=args.core,
core_exponent=args.core_exponent)
print 'report'
for name, value in report:
print '{}: {}'.format(name, value)
