def main():
"""
Main entry point for the application when run from the command line.
"""
# Timing instance
timing = Timing(['Snippet', 'Time [m]', 'Time [s]'])
with timing.timeit_context_add('Pre-processing'):
# Setup parse options command line
current_path = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
parser = args.setup_parser(current_path + '/args/mfbn.json')
options = parser.parse_args()
args.update_json(options)
args.check_output(options)
if options.input and options.vertices is None:
print('Vertices are required when input is given.')
sys.exit(1)
# Load bipartite graph
with timing.timeit_context_add('Load graph'):
source_graph = MGraph()
source_graph.load(options.input, options.vertices)
# Coarsening
with timing.timeit_context_add('Coarsening'):
kwargs = dict(reduction_factor=options.reduction_factor,
max_levels=options.max_levels,
matching=options.matching,
similarity=options.similarity,
itr=options.itr,
upper_bound=options.upper_bound,
gmv=options.gmv,
tolerance=options.tolerance,
reverse=options.reverse,
seed_priority=options.seed_priority,
threads=options.threads)
coarsening = Coarsening(source_graph, **kwargs)
coarsening.run()
# Save
with timing.timeit_context_add('Save'):
output = options.output
for index, obj in enumerate(
zip(coarsening.hierarchy_levels, coarsening.hierarchy_graphs)):
level, coarsened_graph = obj
index += 1
if options.save_conf or options.show_conf:
d = {
'source_input': options.input,
'source_vertices': source_graph['vertices'],
'source_vcount': source_graph.vcount(),
'source_ecount': source_graph.ecount(),
'coarsened_ecount': coarsened_graph.ecount(),
'coarsened_vcount': coarsened_graph.vcount(),
'coarsened_vertices': coarsened_graph['vertices'],
'achieved_levels': coarsened_graph['level'],
'reduction_factor': options.reduction_factor,
'max_levels': options.max_levels,
'similarity': options.similarity,
'matching': options.matching,
'upper_bound': options.upper_bound,
'gmv': options.gmv,
'itr': options.itr,
'level': level
}
if options.save_conf:
with open(output + '-' + str(index) + '-info.json', 'w+') as f:
json.dump(d, f, indent=4)
if options.show_conf:
print(json.dumps(d, indent=4))
if options.save_ncol:
coarsened_graph.write(output + '-' + str(index) + '.ncol',
format='ncol')
if options.save_source:
with open(output + '-' + str(index) + '.source', 'w+') as f:
for v in coarsened_graph.vs():
f.write(' '.join(map(str, v['source'])) + '\n')
if options.save_membership:
membership = [0] * (source_graph['vertices'][0] +
source_graph['vertices'][1])
for v in coarsened_graph.vs():
for source in v['source']:
membership[source] = v.index
numpy.savetxt(output + '-' + str(index) + '.membership',
membership,
fmt='%d')
if options.save_predecessor:
with open(output + '-' + str(index) + '.predecessor',
'w+') as f:
for v in coarsened_graph.vs():
f.write(' '.join(map(str, v['predecessor'])) + '\n')
if options.save_successor:
numpy.savetxt(output + '-' + str(index) + '.successor',
coarsened_graph.vs['successor'],
fmt='%d')
if options.save_weight:
numpy.savetxt(output + '-' + str(index) + '.weight',
coarsened_graph.vs['weight'],
fmt='%d')
if options.save_gml:
del coarsened_graph['adjlist']
del coarsened_graph['similarity']
coarsened_graph['layers'] = str(coarsened_graph['layers'])
coarsened_graph['vertices'] = ','.join(
map(str, coarsened_graph['vertices']))
coarsened_graph['level'] = ','.join(
map(str, coarsened_graph['level']))
coarsened_graph.vs['name'] = map(
str, range(0, coarsened_graph.vcount()))
coarsened_graph.vs['type'] = map(str,
coarsened_graph.vs['type'])
coarsened_graph.vs['weight'] = map(
str, coarsened_graph.vs['weight'])
coarsened_graph.vs['successor'] = map(
str, coarsened_graph.vs['successor'])
for v in coarsened_graph.vs():
v['source'] = ','.join(map(str, v['source']))
v['predecessor'] = ','.join(map(str, v['predecessor']))
coarsened_graph.write(output + '-' + str(index) + '.gml',
format='gml')
if not options.save_hierarchy:
break
if options.show_timing:
timing.print_tabular()
if options.save_timing_csv:
timing.save_csv(output + '-timing.csv')
if options.save_timing_json:
timing.save_json(output + '-timing.json')
member = next(iter(vertex['membership']))
vertex['vertex_color'] = colors[comm2colors[member]]
with open(output + '.color', 'w+') as f:
for color in colors:
f.write(color + '\n')
if __name__ == '__main__':
# Setup parse options command line
current_path = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
parser = args.setup_parser(current_path + '/args/pynetviewer.json')
options = parser.parse_args()
args.update_json(options)
args.check_output(options)
# Log instanciation
log = helper.initialize_logger(dir='log', output='log')
# Check required fields
if options.input is None:
parser.error('required -f [input] arg.')
if options.vertices is None:
parser.error('required -v [number of vertices for each layer] arg.')
graph = helperigraph.load(options.input,
options.vertices,
type_filename=options.file_type)
def __init__(self):
""" Initialize the bnoc app
For help use:
> python bnoc.py --help
"""
self.timing = Timing(['Snippet', 'Time [m]', 'Time [s]'])
with self.timing.timeit_context_add('Pre-processing'):
# Setup parse options command line
current_path = os.path.dirname(
os.path.abspath(inspect.getfile(inspect.currentframe())))
parser = args.setup_parser(current_path + '/args/bnoc.json')
self.options = parser.parse_args()
args.update_json(self.options)
args.check_output(self.options)
self.log = helper.initialize_logger(dir='log', output='log')
if self.options.save_arff and (self.options.x is not None):
self.log.warning(
'Warning: Arff format does not allow overlap in the first layer (parameter x).\
Please use --save_arff=False or supress x parameter.'
)
sys.exit(1)
self.layers = len(self.options.vertices)
self.start_end = []
for layer in range(self.layers):
start = sum(self.options.vertices[0:layer])
end = sum(self.options.vertices[0:layer + 1]) - 1
self.start_end.append([start, end])
if self.options.p is None or self.options.balanced is True:
self.generate_balanced_probabilities()
for p in self.options.p:
if str(numpy.sum(round(sum(p), 1))) != str(1.0):
self.log.warning(
'Warning: The sum of probabilities p1 must be equal to 1.'
)
sys.exit(1)
if len(self.options.communities) is None:
self.options.communities = [1] * len(self.options.vertices)
if self.options.x is not None and isinstance(self.options.x, int):
self.options.x = [self.options.x] * self.layers
if self.options.y is not None and isinstance(self.options.y, int):
self.options.y = [self.options.y] * self.layers
if self.options.z is not None and isinstance(self.options.z, int):
self.options.z = [self.options.z] * self.layers
if all(isinstance(item, tuple) for item in self.options.schema):
self.options.schema = [
list(elem) for elem in self.options.schema
]
if not all(isinstance(item, list) for item in self.options.schema):
it = iter(self.options.schema)
self.options.schema = zip(it, it)
if self.options.mu is not None and isinstance(
self.options.mu, (int, float)):
self.options.mu = [self.options.mu] * len(self.options.schema)
if self.options.dispersion is not None and isinstance(
self.options.dispersion, (int, float)):
self.options.dispersion = [self.options.dispersion] * len(
self.options.schema)
if self.options.noise is not None and isinstance(
self.options.noise, (int, float)):
self.options.noise = [self.options.noise] * len(
self.options.schema)
for layer, comm in enumerate(self.options.communities):
if comm == 0:
self.log.warning(
'The number of communities must be greater than zero.')
sys.exit(1)
if self.options.communities[layer] > self.options.vertices[
layer]:
self.log.warning(
'Warning: The number of communities must be less than the number of vertices.'
)
sys.exit(1)
if self.options.x is not None and self.options.z is not None:
if self.options.z[layer] > self.options.communities[layer]:
self.log.warning(
'Warning: Number of vertices of overlapping communities must be less than \
the number of communities in all layers.')
sys.exit(1)
if sum(self.options.x) > 0 and sum(self.options.z) == 0:
self.options.z = [2] * len(self.options.x)
def main():
"""
Main entry point for the application when run from the command line.
"""
# Timing instanciation
timing = Timing(['Snippet', 'Time [m]', 'Time [s]'])
with timing.timeit_context_add('Pre-processing'):
# Setup parse options command line
current_path = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parser = args.setup_parser(current_path + '/args/mdr.json')
options = parser.parse_args()
args.update_json(options)
args.check_output(options)
# Log instanciation
log = helper.initialize_logger(dir='log', output='log')
if options.input and options.vertices is None:
log.warning('Vertices are required when input is given.')
sys.exit(1)
# Create default values for optional parameters
if options.reduction_factor is None:
options.reduction_factor = 0.5
if options.max_levels is None:
options.max_levels = 3
if options.matching is None:
options.matching = 'greedy_seed_twohops'
if options.similarity is None:
options.similarity = 'weighted_common_neighbors'
# Validation of matching method
valid_matching = ['gmb', 'rgmb', 'hem', 'lem', 'rm']
if options.matching.lower() not in valid_matching:
log.warning('Matching method is unvalid.')
sys.exit(1)
# Validation of input extension
valid_input = ['.arff', '.dat']
if options.extension not in valid_input:
log.warning('Input is unvalid.')
sys.exit(1)
# Validation of similarity measure
valid_similarity = ['common_neighbors', 'weighted_common_neighbors',
'salton', 'preferential_attachment', 'jaccard', 'adamic_adar',
'resource_allocation', 'sorensen', 'hub_promoted', 'hub_depressed',
'leicht_holme_newman', 'weighted_jaccard']
if options.similarity.lower() not in valid_similarity:
log.warning('Similarity misure is unvalid.')
sys.exit(1)
options.vertices = map(int, options.vertices)
options.max_levels = int(options.max_levels)
options.reduction_factor = float(options.reduction_factor)
# Load bipartite graph
with timing.timeit_context_add('Load'):
if options.extension == '.arff':
graph = helperigraph.load_csr(options.input)
elif options.extension == '.dat':
graph = helperigraph.load_dat(options.input, skip_last_column=options.skip_last_column, skip_rows=options.skip_rows)
graph['level'] = 0
# Coarsening
with timing.timeit_context_add('Coarsening'):
hierarchy_graphs = []
hierarchy_levels = []
while not graph['level'] == options.max_levels:
matching = range(graph.vcount())
levels = graph['level']
levels += 1
graph['similarity'] = getattr(Similarity(graph, graph['adjlist']), options.similarity)
start = sum(graph['vertices'][0:1])
end = sum(graph['vertices'][0:1 + 1])
if options.matching in ['hem', 'lem', 'rm']:
one_mode_graph = graph.weighted_one_mode_projection(vertices)
matching_method = getattr(one_mode_graph, options.matching)
matching_method(matching, reduction_factor=options.reduction_factor)
else:
matching_method = getattr(graph, options.matching)
matching_method(range(start, end), matching, reduction_factor=options.reduction_factor)
coarse = graph.contract(matching)
coarse['level'] = levels
graph = coarse
if options.save_hierarchy or (graph['level'] == options.max_levels):
hierarchy_graphs.append(graph)
hierarchy_levels.append(levels)
# Save
with timing.timeit_context_add('Save'):
output = options.output
for index, obj in enumerate(reversed(zip(hierarchy_levels, hierarchy_graphs))):
levels, graph = obj
if options.save_conf:
with open(output + '-' + str(index) + '.conf', 'w+') as f:
d = {}
d['source_filename'] = options.input
d['source_v0'] = options.vertices[0]
d['source_v1'] = options.vertices[1]
d['source_vertices'] = options.vertices[0] + options.vertices[1]
d['edges'] = graph.ecount()
d['vertices'] = graph.vcount()
d['reduction_factor'] = options.reduction_factor
d['max_levels'] = options.max_levels
d['similarity'] = options.similarity
d['matching'] = options.matching
d['levels'] = levels
for layer in range(graph['layers']):
vcount = str(len(graph.vs.select(type=layer)))
attr = 'v' + str(layer)
d[attr] = vcount
json.dump(d, f, indent=4)
if options.save_ncol:
graph.write(output + '-' + str(index) + '.ncol', format='ncol')
if options.save_source:
with open(output + '-' + str(index) + '.source', 'w+') as f:
for v in graph.vs():
f.write(' '.join(map(str, v['source'])) + '\n')
if options.save_predecessor:
with open(output + '-' + str(index) + '.predecessor', 'w+') as f:
for v in graph.vs():
f.write(' '.join(map(str, v['predecessor'])) + '\n')
if options.save_successor:
numpy.savetxt(output + '-' + str(index) + '.successor', graph.vs['successor'], fmt='%d')
if options.save_weight:
numpy.savetxt(output + '-' + str(index) + '.weight', graph.vs['weight'], fmt='%d')
if options.save_adjacency:
numpy.savetxt(output + '-' + str(index) + '.dat', helperigraph.biajcent_matrix(graph), fmt='%.2f')
if options.save_gml:
del graph['adjlist']
del graph['similarity']
graph['layers'] = str(graph['layers'])
graph['vertices'] = ','.join(map(str, graph['vertices']))
graph['level'] = str(graph['level'])
graph.vs['name'] = map(str, range(0, graph.vcount()))
graph.vs['type'] = map(str, graph.vs['type'])
graph.vs['weight'] = map(str, graph.vs['weight'])
graph.vs['successor'] = map(str, graph.vs['successor'])
for v in graph.vs():
v['source'] = ','.join(map(str, v['source']))
v['predecessor'] = ','.join(map(str, v['predecessor']))
graph.write(output + '-' + str(index) + '.gml', format='gml')
if not options.save_hierarchy:
break
if options.show_timing:
timing.print_tabular()
if options.save_timing_csv:
timing.save_csv(output + '-timing.csv')
if options.save_timing_json:
timing.save_json(output + '-timing.csv')