def main():
parser = optparse.OptionParser(
description='Fits a hierarchical random graph (HRG) model to a network. Saves the model to a file in graph markup language (GML) format.',
prog='hrg-fit.py',
usage='%prog [options] GRAPH_EDGELIST_FILE')
parser.add_option('-s', '--num-steps', action='store', type=int,
default=100000,
help='The number of MCMC steps to take (default=100000).')
parser.add_option('-t', '--nodetype', action='store', type='choice',
choices=[int,str],
default=int,
help='The type of the nodes in the edgelist file; "int" or "str" (default="int")')
(options, args) = parser.parse_args()
if len(args) != 1:
parser.print_help()
return 1
G=nx.read_edgelist(args[0], nodetype=options.nodetype)
name=os.path.splitext(args[0])[0]
hrg_file = name + '-hrg.gml'
print("HRG model will be saved as " + hrg_file + ".")
D=Dendrogram.from_graph(G)
bestL=initL=D.graph['L']
prevL=bestL
bestI=0
print_status("step", "L", "best L", "MC step", "deltaL")
for i in range(1, options.num_steps):
taken=D.monte_carlo_move()
t = ''
if taken:
t = '*'
if D.graph['L'] > bestL:
bestL=D.graph['L']
bestI=i
nx.write_gml(D, hrg_file)
print_status("["+str(i)+"]", "%.3f" % bestL, "%.3f" % bestL, t, "%.3f"%D.deltaL)
elif i % 4096 == 0:
print_status("["+str(i)+"]", "%.3f" % D.graph['L'], "%.3f" % bestL, t, "%.3f"%D.deltaL)
prevL=D.graph['L']
if i % 10 == 0:
sys.stdout.flush()
print("Step number of last best fit "+str(bestI) + ".")
print("HRG model was saved as " + hrg_file + ".")
return 0
def main():
parser = optparse.OptionParser(
description=
'Fits a hierarchical random graph (hrg) model to a network. Saves the model to a file in graph markup language (GML) format.',
prog='hrg-fit.py',
usage='%prog [options] GRAPH_EDGELIST_FILE')
parser.add_option(
'-s',
'--num-steps',
action='store',
type=int,
default=100000,
help='The number of MCMC steps to take (default=100000).')
parser.add_option(
'-t',
'--nodetype',
action='store',
type='choice',
choices=[int, str],
default=int,
help=
'The type of the nodes in the edgelist file; "int" or "str" (default="int")'
)
(options, args) = parser.parse_args()
if len(args) != 1:
parser.print_help()
return 1
G = nx.read_edgelist(args[0], nodetype=options.nodetype)
name = os.path.splitext(args[0])[0]
hrg_file = name + '-hrg.gml'
print("hrg model will be saved as " + hrg_file + ".")
D = Dendrogram.from_graph(G)
bestL = initL = D.graph['L']
prevL = bestL
bestI = 0
print_status("step", "L", "best L", "MC step", "deltaL")
for i in range(1, options.num_steps):
taken = D.monte_carlo_move()
t = ''
if taken:
t = '*'
if D.graph['L'] > bestL:
bestL = D.graph['L']
bestI = i
nx.write_gml(D, hrg_file)
print_status("[" + str(i) + "]", "%.3f" % bestL, "%.3f" % bestL, t,
"%.3f" % D.deltaL)
elif i % 4096 == 0:
print_status("[" + str(i) + "]", "%.3f" % D.graph['L'],
"%.3f" % bestL, t, "%.3f" % D.deltaL)
prevL = D.graph['L']
if i % 10 == 0:
sys.stdout.flush()
print("Step number of last best fit " + str(bestI) + ".")
print("hrg model was saved as " + hrg_file + ".")
return 0
def main():
parser = OptionParser(
description=
"Finds a consensus dendrogram from an HRG model of a network. Saves the consensus dendrogram to a graph markup language (GML) file. Saves the histogram of splits in the consensus dendrogram to a file in Python's pickle format.",
prog='hrg-consensus.py',
usage='%prog [options] GRAPH_EDGELIST_FILE DENDROGRAM_GML_FILE')
parser.add_option(
'-s',
'--num-samples',
action='store',
type=int,
default=10000,
help=
'The number of times to sample the dendrogram\'s splits (default=10000).'
)
parser.add_option('-t',
'--temperature',
action='store',
type=float,
default=2.0,
help='The temperature at which to run (default=2.0).')
(options, args) = parser.parse_args()
if len(args) != 2:
parser.print_help()
return 1
graph_edgelist = args[0]
G = nx.read_edgelist(graph_edgelist, nodetype=int)
filename = os.path.basename(graph_edgelist)
G.name = os.path.splitext(filename)[0]
gml_file = args[1]
D = Dendrogram.from_gml_file(gml_file, G)
bestL = initL = D.graph['L']
bestI = 0
print_status("step", "L", "best L", "% complete", "consensus size")
threshold = 1 / (50.0 * G.number_of_nodes())
burnin = 200 * G.number_of_nodes()
i = 1
out = os.path.splitext(graph_edgelist)[0]
out += '-consensus-temp-%0.2f' % options.temperature
dendro_file = out + '-dendrogram.gml'
hist_file = out + '-histogram.dat'
print("HRG consensus dendrogram will be saved as " + dendro_file)
print("Split histogram will be saved as " + hist_file)
while D.num_samples < options.num_samples:
taken = D.monte_carlo_move(T=options.temperature, debug=False)
if i > burnin and random.random() < threshold:
D.sample_splits()
t = ''
if taken:
t = '*'
if D.graph['L'] > bestL:
bestL = D.graph['L']
if i % 4096 == 0:
nsplits = D.num_samples
pct_complete = 100 * D.num_samples / float(options.num_samples)
print_status("[" + str(i) + "]", "%.3f" % D.graph['L'],
"%.3f" % bestL, "%8.2f" % pct_complete,
"%10d" % nsplits)
if i % 10 == 0:
sys.stdout.flush()
i += 1
# Save the histogram to a file.
D.split_histogram['num_samples'] = D.num_samples
pickle.dump(D.split_histogram, open(hist_file, mode='wb'))
del D.split_histogram['num_samples']
print("Saved split histogram to " + hist_file)
# Build the consensus dendrogram, save it to a file.
builder = ConsensusDendrogramBuilder()
C = builder.build(D.graph_nodes_list, D.split_histogram, D.num_samples)
nx.write_gml(C, out + '-dendrogram.gml')
print("Saved consensus dendrogram to " + dendro_file)
return 0
def main():
parser = OptionParser(
description="Finds a consensus dendrogram from an HRG model of a network. Saves the consensus dendrogram to a graph markup language (GML) file. Saves the histogram of splits in the consensus dendrogram to a file in Python's pickle format.",
prog='hrg-consensus.py',
usage='%prog [options] GRAPH_EDGELIST_FILE DENDROGRAM_GML_FILE')
parser.add_option('-s', '--num-samples', action='store', type=int,
default=10000, help='The number of times to sample the dendrogram\'s splits (default=10000).')
parser.add_option('-t', '--temperature', action='store', type=float,
default=2.0, help='The temperature at which to run (default=2.0).')
(options, args) = parser.parse_args()
if len(args) != 2:
parser.print_help()
return 1
graph_edgelist=args[0]
G=nx.read_edgelist(graph_edgelist, nodetype=int)
filename=os.path.basename(graph_edgelist)
G.name=os.path.splitext(filename)[0]
gml_file=args[1]
D=Dendrogram.from_gml_file(gml_file, G)
bestL=initL=D.graph['L']
bestI=0
print_status("step", "L", "best L", "% complete", "consensus size")
threshold = 1/(50.0*G.number_of_nodes())
burnin = 200*G.number_of_nodes()
i=1
out = os.path.splitext(graph_edgelist)[0]
out += '-consensus-temp-%0.2f' % options.temperature
dendro_file = out + '-dendrogram.gml'
hist_file = out + '-histogram.dat'
print("HRG consensus dendrogram will be saved as " + dendro_file)
print("Split histogram will be saved as " + hist_file)
while D.num_samples < options.num_samples:
taken=D.monte_carlo_move(T=options.temperature, debug=False)
if i > burnin and random.random() < threshold:
D.sample_splits()
t = ''
if taken:
t = '*'
if D.graph['L'] > bestL:
bestL=D.graph['L']
if i % 4096 == 0:
nsplits = D.num_samples
pct_complete = 100 * D.num_samples / float(options.num_samples)
print_status(
"[" + str(i) + "]",
"%.3f" % D.graph['L'],
"%.3f" % bestL,
"%8.2f" % pct_complete,
"%10d" % nsplits)
if i % 10 == 0:
sys.stdout.flush()
i+=1
# Save the histogram to a file.
D.split_histogram['num_samples'] = D.num_samples
pickle.dump(D.split_histogram, open(hist_file, mode='wb'))
del D.split_histogram['num_samples']
print("Saved split histogram to " + hist_file)
# Build the consensus dendrogram, save it to a file.
builder = ConsensusDendrogramBuilder()
C = builder.build(D.graph_nodes_list, D.split_histogram, D.num_samples)
nx.write_gml(C, out + '-dendrogram.gml')
print("Saved consensus dendrogram to " + dendro_file)
return 0
