def main():
parser = argparse.ArgumentParser()
parser.add_argument("network_file", help="Network file used for initial\
clustering")
parser.add_argument("cluster_file", help="Clustering results file")
parser.add_argument("-c", "--no_conversion", action="store_true")
parser.add_argument("-d", "--directed", action="store_true",
help="Flag specifying if the input represents\
a directed graph. Defaults to false.")
parser.add_argument("-n", "--node_list", nargs="?",
help="Optionally specify a list of the nodes in\
the DSD file. Default is all the nodes in the\
graph.")
parser.add_argument("-s", "--simple_conversion", action="store_true")
opts = parser.parse_args()
if opts.node_list:
node_list = io.get_node_list(opts.node_list)
clusters = io.read_clusters(opts.cluster_file)
if opts.node_list:
G = io.build_ig_graph_from_matrix(opts.network_file, False, node_list)
else:
G = ig.Graph.Read_Ncol(opts.network_file, directed=opts.directed)
clusters_to_process, final_clusters = [], []
for cluster in clusters:
if len(cluster) > MAX_CL_SIZE:
clusters_to_process.append(cluster)
else:
final_clusters.append(cluster)
# if all nodes have been clustered, stop looping, otherwise continue to
# recurse on each large cluster
step = 1
while clusters_to_process:
processing = clusters_to_process
clusters_to_process = []
for cluster in processing:
id_cluster = names_to_ids(G, cluster)
SG = G.subgraph(cluster)
cluster_size = len(cluster)
num_clusters = 2
'''
num_clusters = (int(cluster_size / float(100)) if cluster_size > 200
else 2)
'''
clusters = cl.spectral_clustering(SG, num_clusters,
no_conversion=opts.no_conversion,
simple_conversion=opts.simple_conversion)
for cluster in clusters:
if len(cluster) > MAX_CL_SIZE:
clusters_to_process.append([SG.vs[i]['name'] for i in cluster])
else:
final_clusters.append([SG.vs[i]['name'] for i in cluster])
step += 1
io.output_clusters(final_clusters, '')
def main():
'''
Prints the modularity of the graph made from the given DSD matrix using the Louvain algorithm to generate clusters
Usage: python louvain_clustering.py <dsd_file>
'''
parser = argparse.ArgumentParser()
parser.add_argument("dsd_file", help = "Distance (i.e. DSD) matrix for network")
parser.add_argument("node_list", help = "Node list")
parser.add_argument("-p", "--ppi", action = "store_true", help = "Flag specifying if the input is a ppi network. Defaults to false.")
parser.add_argument("-d", "--directed", action = "store_true", help = "Flag specifying if the input represents a directed graph. Defaults to false.")
opts = parser.parse_args()
node_list = io.get_node_list(opts.node_list)
#print node_list
if opts.ppi == True:
G = io.build_ig_graph_from_edgelist(opts.dsd_file)
sys.stderr.write("Read in\n")
# print G.vs['name']
else:
G = io.build_ig_graph_from_matrix(opts.dsd_file, node_list = node_list)
sys.stderr.write("Read in\n")
# for edge in G.es:
# edge['weight'] = 1/edge['weight']
# sys.stderr.write("flipped edges\n")
all_info = {}
all_info['unused_nodes'] = []
all_info['enriched_clusters'] = []
all_info['total_clusters'] = []
all_info['ratio'] = []
all_info['avg_enrichments'] = []
all_info['avg_logodds'] = []
all_info['cluster_sizes'] = []
all_info['enriched_per_size'] = []
for repeat in range(0,10):
sys.stderr.write("starting trial " + str(repeat))
x = random.randint(0, G.vcount()-1)
for i in range(1,x):
G = shuffle(G)
sys.stderr.write(": shuffled, ")
partition = build_clusters(G)
#partition = G.community_multilevel(weights = 'weight', return_levels = True)[0]
sys.stderr.write("clustered\n")
f = open("trial"+str(repeat), "w")
i = 0
for line in partition:
f.write(str(i) + line)
i += 1
def main():
parser = argparse.ArgumentParser()
parser.add_argument("dsd_file",
help="Distance (i.e. DSD) matrix for network")
parser.add_argument("cluster_file", help="Clustering results file")
parser.add_argument("-n",
"--node_list",
nargs="?",
help="Optionally specify a list of the nodes in\
the DSD file. Default is all the nodes in the\
graph.")
opts = parser.parse_args()
node_list = io.get_node_list(opts.node_list)
clusters = io.read_clusters(opts.cluster_file)
G = io.build_ig_graph_from_matrix(opts.dsd_file, False, node_list)
clusters_to_process, final_clusters = [], []
for cluster in clusters:
if len(cluster) > MAX_CL_SIZE:
clusters_to_process.append(cluster)
else:
final_clusters.append(cluster)
# if all nodes have been clustered, stop looping, otherwise continue to
# recurse on each large cluster
step = 1
while clusters_to_process:
processing = clusters_to_process
clusters_to_process = []
for cluster in processing:
id_cluster = names_to_ids(G, cluster)
SG = G.subgraph(cluster)
cluster_size = len(cluster)
num_clusters = (int(cluster_size /
float(100)) if cluster_size > 200 else 2)
mat = SG.get_adjacency(attribute='weight')
dist_matrix = np.array(mat.data)
del mat
clusters = cl.spectral_clustering(dist_matrix, num_clusters)
del dist_matrix
for cluster in clusters:
if len(cluster) > MAX_CL_SIZE:
clusters_to_process.append(
[SG.vs[i]['name'] for i in cluster])
else:
final_clusters.append([SG.vs[i]['name'] for i in cluster])
step += 1
io.output_clusters(final_clusters, '')
def main():
'''
Prints the modularity of the graph made from the given DSD matrix using the Louvain algorithm to generate clusters
Usage: python louvain_clustering.py <dsd_file>
'''
parser = argparse.ArgumentParser()
parser.add_argument("dsd_file",
help="Distance (i.e. DSD) matrix for network")
parser.add_argument("node_list", help="Node list")
parser.add_argument(
"-p",
"--ppi",
action="store_true",
help="Flag specifying if the input is a ppi network. Defaults to false."
)
parser.add_argument(
"-d",
"--directed",
action="store_true",
help=
"Flag specifying if the input represents a directed graph. Defaults to false."
)
opts = parser.parse_args()
node_list = io.get_node_list(opts.node_list)
#print node_list
if opts.ppi == True:
G = io.build_ig_graph_from_edgelist(opts.dsd_file)
else:
G = io.build_ig_graph_from_matrix(opts.dsd_file, node_list=node_list)
#for edge in G.es:
# edge['weight'] = 1/edge['weight']
# the first level seems to have more clusters
partition = build_clusters(G, 4)
#print(partition)
#for part in partition:
# print part
# partition = G.community_walktrap(weights = 'weight', steps = 2).as_clustering()
f = open("trial", "w")
i = 0
for line in partition:
f.write(str(i) + line)
i += 1
def main():
parser = argparse.ArgumentParser()
parser.add_argument("network_file", help="Original network input file")
parser.add_argument("cluster_file", help="A cluster results file")
parser.add_argument("-n",
"--node_list",
nargs="?",
default=[],
help="Optionally specify a list of the nodes in\
the graph.")
parser.add_argument("-o",
"--output_prefix",
nargs="?",
default="",
help="Optionally specify a prefix for output files")
opts = parser.parse_args()
nodes = io.get_node_list(opts.node_list) if opts.node_list else []
output_pref = opts.output_prefix if opts.output_prefix else './clusters_'
G = io.build_ig_graph_from_matrix(opts.network_file,
is_directed=False,
node_list=nodes)
if opts.node_list: G.vs['name'] = nodes
clusters = io.read_clusters(opts.cluster_file)
for idx, cluster in enumerate(clusters):
try:
output_file = '{}{}.txt'.format(output_pref, idx)
output_fp = open(output_file, 'w')
except IOError:
sys.exit("Could not open file: {}".format(output_file))
id_cluster = names_to_ids(G, cluster)
SG = G.subgraph(id_cluster)
for e in SG.es:
output_fp.write('{}\t{}\t{}\n'.format(SG.vs[e.source]['name'],
SG.vs[e.target]['name'],
e['weight']))
output_fp.close()
def main():
parser = argparse.ArgumentParser()
# parser.add_argument("network_file", help="Original network input file")
parser.add_argument("dsd_file",
help="Distance (i.e. DSD) matrix for network")
parser.add_argument(
"-a",
"--algorithm",
nargs="?",
default=DEFAULT_ALG,
help="The clustering algorithm to use - 1 for spectral,\
2 for threshold clustering, and 3 for simple\
shortest-path divisive hierarchical clustering.\
Defaults to spectral clustering.")
parser.add_argument("-c", "--no_conversion", action="store_true")
parser.add_argument("-d",
"--directed",
action="store_true",
help="Flag specifying if the input represents\
a directed graph. Defaults to false.")
parser.add_argument("-n",
"--node_list",
nargs="?",
help="Optionally specify a list of the nodes in\
the DSD file. Default is all the nodes in the\
graph.")
parser.add_argument("-o",
"--output_file",
nargs="?",
default="",
help="Optionally specify an output file. Output is to\
stdout if no file is specified.")
parser.add_argument("-p",
"--parameter",
nargs="?",
default='',
help="Specify a parameter (i.e. number of clusters,\
distance threshold) to be used with clustering\
algorithm. If none is provided, a sensible\
default is used.")
parser.add_argument("-s", "--simple_conversion", action="store_true")
opts = parser.parse_args()
if USE_NETWORKX:
import clustering_algs_nx as cl
# G = io.build_nx_graph_from_matrix(opts.dsd_file, opts.directed)
G = io.build_nx_graph_from_edgelist(opts.dsd_file, opts.directed)
else:
import clustering_algs_ig as cl
if opts.node_list:
G = io.build_ig_graph_from_matrix(opts.dsd_file, opts.directed)
else:
# G = io.build_ig_graph_from_edgelist(opts.dsd_file, opts.directed)
# temporary, TODO remove after consensus experiments
G = ig.Graph.Read_Ncol(opts.dsd_file, directed=opts.directed)
# nodes = io.get_node_list(opts.node_list) if opts.node_list else []
if opts.node_list:
nodes = io.get_node_list(opts.node_list)
else:
nodes = zip(
*sorted([(v.index, v['name'])
for v in G.vs], key=lambda x: x[0]))[1]
opts.algorithm = int(opts.algorithm)
if opts.algorithm == SPECTRAL:
k_val = int(opts.parameter) if opts.parameter else 100
clusters = cl.spectral_clustering(
G,
n_clusters=k_val,
node_map=nodes,
no_conversion=opts.no_conversion,
simple_conversion=opts.simple_conversion)
elif opts.algorithm == THRESHOLD:
filter_weight = float(opts.parameter) if opts.parameter else 5.0
clusters = cl.threshold_clustering(G,
threshold=filter_weight,
node_map=nodes)
elif opts.algorithm == HIERARCHICAL:
filter_weight = float(opts.parameter) if opts.parameter else 1.0
clusters = cl.hierarchical_clustering(G, threshold=filter_weight)
else:
sys.exit('Please pick a valid clustering algorithm')
io.output_clusters(clusters, opts.output_file)
def main():
parser = argparse.ArgumentParser()
# parser.add_argument("network_file", help="Original network input file")
parser.add_argument("dsd_file",
help="Distance (i.e. DSD) matrix for network")
parser.add_argument(
"-a",
"--algorithm",
nargs="?",
default=DEFAULT_ALG,
help="The clustering algorithm to use - 1 for spectral,\
2 for threshold clustering, and 3 for simple\
shortest-path divisive hierarchical clustering.\
Defaults to spectral clustering.")
parser.add_argument("-d",
"--directed",
action="store_true",
help="Flag specifying if the input represents\
a directed graph. Defaults to false.")
parser.add_argument("-n",
"--node_list",
nargs="?",
help="Optionally specify a list of the nodes in\
the DSD file. Default is all the nodes in the\
graph.")
parser.add_argument("-o",
"--output_file",
nargs="?",
default="",
help="Optionally specify an output file. Output is to\
stdout if no file is specified.")
parser.add_argument("-p",
"--parameter",
nargs="?",
default='',
help="Specify a parameter (i.e. number of clusters,\
distance threshold) to be used with clustering\
algorithm. If none is provided, a sensible\
default is used.")
opts = parser.parse_args()
G = io.build_ig_graph_from_matrix(opts.dsd_file, opts.directed)
nodes = io.get_node_list(opts.node_list) if opts.node_list else []
opts.algorithm = int(opts.algorithm)
if opts.algorithm == SPECTRAL:
import numpy as np
k_val = int(opts.parameter) if opts.parameter else 100
mat = G.get_adjacency(attribute='weight')
del G
dist_matrix = np.array(mat.data)
del mat
clusters = cl.spectral_clustering(dist_matrix,
n_clusters=k_val,
node_map=nodes)
elif opts.algorithm == THRESHOLD:
filter_weight = float(opts.parameter) if opts.parameter else 5.0
clusters = cl.threshold_clustering(G,
threshold=filter_weight,
node_map=nodes)
elif opts.algorithm == HIERARCHICAL:
sys.exit('Hierarchical clustering is not implemented, please choose\
another algorithm')
else:
sys.exit('Please pick a valid clustering algorithm')
io.output_clusters(clusters, opts.output_file)