def show_usage():
print 'Multiscale Entropic Network Generator 2 (MUSKETEER2)'
print 'Allowed options are:'
print '-c, --citation Citation information for MUSKETEER 2'
print '-f, --input_path Input graph file path'
print '-h, --help Shows these options'
print '-M, --metrics Compare the replica to the original. Computing intensive. (Default: -M False).'
print '-o, --output_path Path to the output file for the graph.'
print ' Output format is chosen automatically based on the extension.'
print '-p, --params Input paremeters. Surround the argument with double quotes:'
print ' e.g. -p "{\'p1_name\':p1_value, \'p2_name\':p2_value}"'
print ' Key parameters: edge_edit_rate, node_edit_rate, node_growth_rate, edge_growth_rate (all are lists of values e.g. [0.01, 0.02])'
print '-s, --seed Random seed (integer)'
print '-T, --test Run a quick self-test'
print '-t, --graph_type Specify the format of the input graph (Default: -t AUTODETECT)'
print '-v, --visualizer Visualization command to call after the replica has been prepared (Default: -v None). Try -v sfdp or -v sfdp3d'
print '--verbose Verbose output (Default: --verbose True)'
print '-w, --write_graph Write replica to disc (Default: -w True).'
print ' For interactive Python make False to speed up generation (disables visualization).'
print
print 'For reading graphs with -t, the supported graph types are: \n%s' % graphutils.load_graph(
path=None, list_types_and_exit=True)
print
print 'For writing graphs with -o, the supported graph extensions are: \n%s' % graphutils.write_graph(
G=None, path=None, list_types_and_exit=True)
print
print
print 'Example call format:'
print graphutils.MUSKETEER_EXAMPLE_CMD
def replica_vs_original(seed=None, figpath=None, generator_func=None, G=None, params=None, num_replicas = 150, title_infix='', metrics=None, intermediates=False, n_jobs=-1):
#generate one or more replicas and compare them to the original graph
if seed==None:
seed = npr.randint(1E6)
print 'rand seed: %d'%seed
npr.seed(seed)
random.seed(seed)
if generator_func==None:
generator_func=algorithms.generate_graph
if G==None:
G = graphutils.load_graph(path='data-social/potterat_Hiv250.elist')
if metrics == None:
metrics = graphutils.default_metrics[:]
metrics = filter(lambda m: m['optional'] < 2, metrics)
if 'metric_runningtime_bound' in params:
mrtb = params['metric_runningtime_bound']
metrics = filter(lambda m: m['runningtime'] <= mrtb, metrics)
metrics = filter(lambda m: m['name'] not in ['avg flow closeness'], metrics) #broken in NX 1.6
metrics.reverse()
if params == None:
params = {'verbose':False, 'node_edit_rate':[0.05, 0.04, 0.03, 0.02, 0.01],
'edge_edit_rate':[0.05, 0.04, 0.03, 0.02, 0.01], 'node_growth_rate':[0], 'locality_bias_correction':0., 'enforce_connected':True, 'accept_chance_edges':1.0,
'retain_intermediates':intermediates}
if intermediates:
params['retain_intermediates'] = True
print 'Params:'
print params
print 'Metrics:'
print [metric['name'] for metric in metrics]
def show_usage():
print "Multiscale Entropic Network Generator 2 (MUSKETEER2)"
print "Allowed options are:"
print "-c, --citation Citation information for MUSKETEER 2"
print "-f, --input_path Input graph file path"
print "-h, --help Shows these options"
print "-M, --metrics Compare the replica to the original. Computing intensive. (Default: -M False)."
print "-o, --output_path Path to the output file for the graph."
print " Output format is chosen automatically based on the extension."
print "-p, --params Input paremeters. Surround the argument with double quotes:"
print " e.g. -p \"{'p1_name':p1_value, 'p2_name':p2_value}\""
print " Key parameters: edge_edit_rate, node_edit_rate, node_growth_rate, edge_growth_rate (all are lists of values e.g. [0.01, 0.02])"
print "-s, --seed Random seed (integer)"
print "-T, --test Run a quick self-test"
print "-t, --graph_type Specify the format of the input graph (Default: -t AUTODETECT)"
print "-v, --visualizer Visualization command to call after the replica has been prepared (Default: -v None). Try -v sfdp or -v sfdp3d"
print "--verbose Verbose output (Default: --verbose True)"
print "-w, --write_graph Write replica to disc (Default: -w True)."
print " For interactive Python make False to speed up generation (disables visualization)."
print
print "For reading graphs with -t, the supported graph types are: \n%s" % graphutils.load_graph(
path=None, list_types_and_exit=True
)
print
print "For writing graphs with -o, the supported graph extensions are: \n%s" % graphutils.write_graph(
G=None, path=None, list_types_and_exit=True
)
print
print
print "Example call format:"
print graphutils.MUSKETEER_EXAMPLE_CMD
def coarsening_test2(seed=None):
#visualizes coarsening: stores the coarsening of the nodes, and then labels the original nodes based on their aggregates in the final level
import matplotlib as mpl
if seed==None:
seed = npr.randint(1E6)
print('rnd seed: %d'%seed)
npr.seed(seed)
random.seed(seed)
G = graphutils.load_graph('data/mesh33.gml')
#G = graphutils.load_graph('data-engineering/watts_strogatz98_power.elist')
c_tree = []
def store_aggregation_chain(G, G_coarse, c_data):
store_aggregation_chain.static_c_tree.append(c_data['home_nodes'].copy())
#print c_data['home_nodes']
#print store_aggregation_chain.static_c_tree
store_aggregation_chain.static_c_tree = c_tree
params = {}
params['do_coarsen_tester'] = store_aggregation_chain
params['node_edit_rate'] = [0, 0, 0, 0] #change to force coarsening
dummy_replica = algorithms.generate_graph(G, params=params)
node_colors = {}
aggregate_colors = {seed:(npr.rand(), npr.rand(), npr.rand(), 1.) for seed in list(c_tree[-1].values())}
for node in G:
my_final_agg = node
for c_set in c_tree:
my_final_agg = c_set[my_final_agg] #this could be faster with union-find structure
node_colors[node] = aggregate_colors[my_final_agg]
clr = aggregate_colors[my_final_agg]
G.node[node]['color'] = '%.3f %.3f %.3f'%(clr[0],clr[1],clr[2])
G.node[node]['label'] = ''
all_nodes = G.nodes()
color_array = np.ones((len(all_nodes),4))
for i,node in enumerate(all_nodes):
color_array[i,:] *= node_colors[node]
#pos = nx.fruchterman_reingold_layout(G)
#nx.draw_networkx_nodes(G, pos=pos, nodelist=G.nodes(), node_color=color_array, cmap=pylab.hot, node_size=500, with_labels=True, node_shape='s')
#nx.draw_networkx_edges(G, pos=pos, alpha=1.0)
#nx.draw_networkx_labels(G, pos=pos)
#pylab.show()
gpath = 'output/coarsening_test_'+timeNow()+'.dot'
gpath_fig = gpath+'.pdf'
graphutils.write_graph(G=G, path=gpath)
print('Writing graph image: %s ..'%gpath_fig)
visualizer_cmdl = 'sfdp -Nwidth=0.10 -Nheight=0.10 -Nfixedsize=true -Nstyle=filled -Tpdf %s > %s &'%(gpath,gpath_fig)
#visualizer_cmdl = 'sfdp -Nwidth=0.03 -Nheight=0.03 -Nfixedsize=true -Nstyle=solid -Tpdf %s > %s &'%(gpath,gpath_fig)
retCode = os.system(visualizer_cmdl)
time.sleep(1)
subprocess.call(['xdg-open', gpath_fig])
def coarsening_test2(seed=None):
#visualizes coarsening: stores the coarsening of the nodes, and then labels the original nodes based on their aggregates in the final level
import matplotlib as mpl
if seed==None:
seed = npr.randint(1E6)
print('rnd seed: %d'%seed)
npr.seed(seed)
random.seed(seed)
G = graphutils.load_graph('data/mesh33.gml')
#G = graphutils.load_graph('data-engineering/watts_strogatz98_power.elist')
c_tree = []
def store_aggregation_chain(G, G_coarse, c_data):
store_aggregation_chain.static_c_tree.append(c_data['home_nodes'].copy())
#print c_data['home_nodes']
#print store_aggregation_chain.static_c_tree
store_aggregation_chain.static_c_tree = c_tree
params = {}
params['do_coarsen_tester'] = store_aggregation_chain
params['node_edit_rate'] = [0, 0, 0, 0] #change to force coarsening
dummy_replica = algorithms.generate_graph(G, params=params)
node_colors = {}
aggregate_colors = {seed:(npr.rand(), npr.rand(), npr.rand(), 1.) for seed in list(c_tree[-1].values())}
for node in G:
my_final_agg = node
for c_set in c_tree:
my_final_agg = c_set[my_final_agg] #this could be faster with union-find structure
node_colors[node] = aggregate_colors[my_final_agg]
clr = aggregate_colors[my_final_agg]
G.node[node]['color'] = '%.3f %.3f %.3f'%(clr[0],clr[1],clr[2])
G.node[node]['label'] = ''
all_nodes = G.nodes()
color_array = np.ones((len(all_nodes),4))
for i,node in enumerate(all_nodes):
color_array[i,:] *= node_colors[node]
#pos = nx.fruchterman_reingold_layout(G)
#nx.draw_networkx_nodes(G, pos=pos, nodelist=G.nodes(), node_color=color_array, cmap=pylab.hot, node_size=500, with_labels=True, node_shape='s')
#nx.draw_networkx_edges(G, pos=pos, alpha=1.0)
#nx.draw_networkx_labels(G, pos=pos)
#pylab.show()
gpath = 'output/coarsening_test_'+timeNow()+'.dot'
gpath_fig = gpath+'.pdf'
graphutils.write_graph(G=G, path=gpath)
print('Writing graph image: %s ..'%gpath_fig)
visualizer_cmdl = 'sfdp -Nwidth=0.10 -Nheight=0.10 -Nfixedsize=true -Nstyle=filled -Tpdf %s > %s &'%(gpath,gpath_fig)
#visualizer_cmdl = 'sfdp -Nwidth=0.03 -Nheight=0.03 -Nfixedsize=true -Nstyle=solid -Tpdf %s > %s &'%(gpath,gpath_fig)
retCode = os.system(visualizer_cmdl)
time.sleep(1)
subprocess.call(['xdg-open', gpath_fig])
def statistical_tests(seed=8):
#systematic comparison of a collection of problems (graphs and parameters)
if seed==None:
seed = npr.randint(1E6)
print('rand seed: %d'%seed)
npr.seed(seed)
random.seed(seed)
default_num_replicas = 20
params_default = {'verbose':False, 'edge_edit_rate':[0.08, 0.07], 'node_edit_rate':[0.08, 0.07], 'node_growth_rate':[0],
'dont_cutoff_leafs':False,
'new_edge_horizon':10, 'num_deletion_trials':20, 'locality_bias_correction':[0,], 'edit_method':'sequential',
}
#params_default['algorithm'] = algorithms.musketeer_on_subgraphs
metrics_default = graphutils.default_metrics[:]
#some metrics are removed because of long running time
metrics_default = [met for met in metrics_default if met['name'] not in ['avg flow closeness', 'avg eigvec centrality', 'degree connectivity', 'degree assortativity', 'average shortest path', 'mean ecc', 'powerlaw exp', ]]
problems = [{'graph_data':nx.erdos_renyi_graph(n=300, p=0.04, seed=42), 'name':'ER300', 'num_replicas':20},
{'graph_data':'data-samples/ftp3c.elist'},
{'graph_data':'data-samples/mesh33.edges'},
{'graph_data':'data-samples/newman06_netscience.gml', 'num_replicas':10},
{'graph_data':'data-samples/watts_strogatz98_power.elist', 'num_replicas':10},
]
for problem in problems:
graph_data = problem['graph_data']
params = problem.get('params', params_default)
metrics = problem.get('metrics', metrics_default)
num_replicas = problem.get('num_replicas', default_num_replicas)
if type(graph_data) is str:
base_graph = graphutils.load_graph(path=graph_data)
base_graph.name = os.path.split(graph_data)[1]
else:
base_graph = graph_data
if not hasattr(base_graph, 'name'):
base_graph.name = problem.get('name', str(npr.randint(10000)))
gpath = 'output/'+os.path.split(base_graph.name)[1]+'_'+timeNow()+'.dot'
gpath_fig = gpath[:-3]+'eps'
graphutils.write_graph(G=base_graph, path=gpath)
visualizer_cmdl = 'sfdp -Nlabel="" -Nwidth=0.03 -Nfixedsize=true -Nheight=0.03 -Teps %s > %s &'%(gpath,gpath_fig)
print('Writing graph image: %s ..'%gpath_fig)
retCode = os.system(visualizer_cmdl)
replica_vs_original(G=base_graph, num_replicas=num_replicas, seed=1, params=params, metrics=metrics, title_infix='musketeer')
def statistical_tests(seed=8):
#systematic comparison of a collection of problems (graphs and parameters)
if seed==None:
seed = npr.randint(1E6)
print('rand seed: %d'%seed)
npr.seed(seed)
random.seed(seed)
default_num_replicas = 20
params_default = {'verbose':False, 'edge_edit_rate':[0.08, 0.07], 'node_edit_rate':[0.08, 0.07], 'node_growth_rate':[0],
'dont_cutoff_leafs':False,
'new_edge_horizon':10, 'num_deletion_trials':20, 'locality_bias_correction':[0,], 'edit_method':'sequential',
}
#params_default['algorithm'] = algorithms.musketeer_on_subgraphs
metrics_default = graphutils.default_metrics[:]
#some metrics are removed because of long running time
metrics_default = [met for met in metrics_default if met['name'] not in ['avg flow closeness', 'avg eigvec centrality', 'degree connectivity', 'degree assortativity', 'average shortest path', 'mean ecc', 'powerlaw exp', ]]
problems = [{'graph_data':nx.erdos_renyi_graph(n=300, p=0.04, seed=42), 'name':'ER300', 'num_replicas':20},
{'graph_data':'data-samples/ftp3c.elist'},
{'graph_data':'data-samples/mesh33.edges'},
{'graph_data':'data-samples/newman06_netscience.gml', 'num_replicas':10},
{'graph_data':'data-samples/watts_strogatz98_power.elist', 'num_replicas':10},
]
for problem in problems:
graph_data = problem['graph_data']
params = problem.get('params', params_default)
metrics = problem.get('metrics', metrics_default)
num_replicas = problem.get('num_replicas', default_num_replicas)
if type(graph_data) is str:
base_graph = graphutils.load_graph(path=graph_data)
base_graph.name = os.path.split(graph_data)[1]
else:
base_graph = graph_data
if not hasattr(base_graph, 'name'):
base_graph.name = problem.get('name', str(npr.randint(10000)))
gpath = 'output/'+os.path.split(base_graph.name)[1]+'_'+timeNow()+'.dot'
gpath_fig = gpath[:-3]+'eps'
graphutils.write_graph(G=base_graph, path=gpath)
visualizer_cmdl = 'sfdp -Nlabel="" -Nwidth=0.03 -Nfixedsize=true -Nheight=0.03 -Teps %s > %s &'%(gpath,gpath_fig)
print('Writing graph image: %s ..'%gpath_fig)
retCode = os.system(visualizer_cmdl)
replica_vs_original(G=base_graph, num_replicas=num_replicas, seed=1, params=params, metrics=metrics, title_infix='musketeer')
def __main() :
"""Main function to mimic C++ version behavior"""
try:
import graphutils
filename = sys.argv[1]
#graph = __load_binary(filename)
graph = graphutils.load_graph(path=filename)
partition = best_partition(graph)
print >> sys.stderr, str(modularity(partition, graph))
for elem, part in partition.iteritems() :
print str(elem) + " " + str(part)
except (IndexError, IOError):
print "Usage : ./community filename"
print "find the communities in graph filename and display the dendogram"
print "Parameters:"
print "filename is a binary file as generated by the "
print "convert utility distributed with the C implementation"
def __main():
"""Main function to mimic C++ version behavior"""
try:
import graphutils
filename = sys.argv[1]
#graph = __load_binary(filename)
graph = graphutils.load_graph(path=filename)
partition = best_partition(graph)
print >> sys.stderr, str(modularity(partition, graph))
for elem, part in partition.iteritems():
print str(elem) + " " + str(part)
except (IndexError, IOError):
print "Usage : ./community filename"
print "find the communities in graph filename and display the dendogram"
print "Parameters:"
print "filename is a binary file as generated by the "
print "convert utility distributed with the C implementation"
def drake_hougardy_test():
import new_algs, graphutils
matching_weight = lambda G, mat: sum(G.edge[u][mat[u]].get('weight', 1.0) for u in mat)/2.0
def is_matching(mat):
G = nx.Graph()
G.add_edges_from(list(mat.items()))
for cc in nx.connected_components(G):
if len(cc) not in [0,2]:
return False
return True
def is_maximal(G, mat):
for edge in G.edges():
if (edge[0] not in mat) and (edge[1] not in mat):
return False
return True
path = nx.path_graph(11)
for u,v,d in path.edges(data=True):
d['weight'] = max(u,v)**2
matching = graphutils.drake_hougardy_slow(path)
print('Matching slow: ' + str(matching))
print(' wt: ' + str(matching_weight(path,matching)))
matching = graphutils.drake_hougardy(path)
assert is_matching(matching)
assert is_maximal(path,matching)
print('Matching: ' + str(matching))
print(' wt: ' + str(matching_weight(path,matching)))
path_opt_m = nx.max_weight_matching(path)
print(' Opt Mat: ' + str(path_opt_m))
print(' wt: ' + str(matching_weight(path,path_opt_m)))
Gr2 = graphutils.load_graph('data-cyber-small/gr2.gml')
matching = graphutils.drake_hougardy_slow(Gr2)
print('Matching slow: ' + str(matching))
print(' wt: ' + str(matching_weight(Gr2,matching)))
matching = graphutils.drake_hougardy(Gr2)
assert is_matching(matching)
assert is_maximal(Gr2, matching)
print('Matching: ' + str(matching))
print(' wt: ' + str(matching_weight(Gr2,matching)))
gr2_opt_m = nx.max_weight_matching(Gr2)
print(' Opt Mat: ' + str(gr2_opt_m))
print(' wt: ' + str(matching_weight(Gr2, gr2_opt_m)))
#matching = graphutils.drake_hougardy(nx.erdos_renyi_graph(1000, 0.02))
num_test_graphs = 100
num_nodes = 400
edge_density = 0.02
seed = 0
for trial in range(num_test_graphs):
seed += 1
Gnp = nx.erdos_renyi_graph(num_nodes, edge_density, seed=seed)
print('Seed: %d'%seed)
matching = graphutils.drake_hougardy(Gnp)
assert is_matching(matching)
assert is_maximal(Gnp, matching)
wtDH = matching_weight(Gnp,matching)
print(' wt DH: ' + str(wtDH))
gnp_opt_m = nx.max_weight_matching(Gnp)
wtOpt = matching_weight(Gnp, gnp_opt_m)
print(' wt Opt: ' + str(wtOpt))
assert wtOpt <= 2*wtDH
def replica_vs_original(seed=None, figpath=None, graph_name = "Generated", generator_func=None, G=None, params=None, num_replicas = 100, title_infix='', metrics=None, generator = "musketeer_all", intermediates=False, n_jobs=-1, store_replicas=False, output_path = "Results"):
"""generate one or more replicas and compare them to the original graph"""
if seed==None:
seed = npr.randint(1E6)
print('rand seed: %d'%seed)
npr.seed(seed)
random.seed(seed)
if generator_func==None:
generator_func=algorithms.generate_graph
if G==None:
G = graphutils.load_graph(path='data-social/potterat_Hiv250.elist')
if metrics == None:
metrics = graphutils.default_metrics[:]
metrics = [m for m in metrics if m['optional'] < 2]
if 'metric_runningtime_bound' in params:
mrtb = params['metric_runningtime_bound']
metrics = [m for m in metrics if m['runningtime'] <= mrtb]
metrics = [m for m in metrics if m['name'] not in ['avg flow closeness']] #broken in NX 1.6
metrics.reverse()
if params == None:
params = {'verbose':False, 'node_edit_rate':[0.05, 0.04, 0.03, 0.02, 0.01],
'edge_edit_rate':[0.05, 0.04, 0.03, 0.02, 0.01], 'node_growth_rate':[0], 'locality_bias_correction':0., 'enforce_connected':True, 'accept_chance_edges':1.0,
'retain_intermediates':intermediates}
if intermediates:
params['retain_intermediates'] = True
print('Params:')
print(params)
print('Metrics:')
print([metric['name'] for metric in metrics])
data = {}
replicas = read_all_files(output_path +generator+"/")
out_dir = output_path + generator +"/"+graph_name+"computation_results"
print(out_dir)
myfile = open(out_dir+ generator, 'w')
#replicas = replicate_graph(G=G, generator_func=generator_func, num_replicas=num_replicas, params=params,
# title_infix=title_infix, n_jobs=n_jobs)
#jaccard_edges = evaluate_similarity(base_graphs=G, graphs=replicas, n_jobs=n_jobs) #this is actually a mean
vals_of_all = evaluate_metrics(graphs=[G]+replicas, metrics=metrics, n_jobs=n_jobs)
vals_of_graph = [metric_data[0] for metric_data in vals_of_all]
vals_of_replicas = [metric_data[1:] for metric_data in vals_of_all]
#replica_statistics, figpath = plot_deviation(vals_of_replicas, vals_of_graph, metrics, figpath, jaccard_edges, title_infix, seed, getattr(G, 'name', ''))
#pylab.show()
#data = {'metrics':[met['name'] for met in metrics], 'name':getattr(G, 'name', ''), 'params':params, 'num_replicas':num_replicas, 'figpath':figpath}
#data[0] = replica_statistics
#data[0].update({'vals_of_replicas':vals_of_replicas, 'val_of_models':vals_of_graph, 'avg_jaccard_edges':jaccard_edges})
i = 0
for (vals,met) in zip(vals_of_replicas,metrics):
attribute = met['name'].replace(" ", "_")
myfile.write(attribute+'\t')
for elem in vals:
nor_value = elem
if (vals_of_graph[i] != 0):
nor_value = float(elem) / vals_of_graph[i]
myfile.write(str(nor_value))
myfile.write('\t')
i += 1
myfile.write('\n')
myfile.write('deleted_edges'+'\t')
i=1
for replica in replicas:
myfile.write(str(find_deleted(G,replica))+'\t')
#nx.write_edgelist(replica,output_path+generator+'/'+ graph_name+ str(i)+".edgelist")
#nx.write_edgelist(replica,output_path+generator+'/'+ str(i)+".edgelist")
i+=1
myfile.write('\nnew_edges' + '\t')
for replica in replicas:
myfile.write(str(find_new(G,replica))+'\t')
myfile.close()
# if intermediates:
# current_replicas = replicas
# for level in range(1, max(len(params.get('node_edit_rate', [])), len(params.get('edge_edit_rate', [])), len(params.get('node_growth_rate', [])), len(params.get('edge_growth_rate', [])))):
# print('LEVEL: %d'%level)
# coarse_models = [r.coarser_graph.model_graph for r in current_replicas]
# coarse_replicas = [r.coarser_graph for r in current_replicas]
# vals_of_models = evaluate_metrics(graphs=coarse_models, metrics=metrics, n_jobs=n_jobs)
# vals_of_replicas = evaluate_metrics(graphs=coarse_replicas, metrics=metrics, n_jobs=n_jobs)
# jaccard_edges = evaluate_similarity(base_graphs=coarse_models, graphs=coarse_replicas, n_jobs=n_jobs)
# replica_statistics, dummy \
# = plot_deviation(vals_of_replicas=vals_of_replicas, vals_of_graph=vals_of_models,
# metrics=metrics, figpath=figpath + 'level%d'%level, jaccard_edges=jaccard_edges)
# current_replicas = coarse_replicas
# data[level] = replica_statistics
#data[level].update({'vals_of_replicas':vals_of_replicas, 'vals_of_models':vals_of_models, 'avg_jaccard_edges':jaccard_edges})
#graphutils.safe_pickle(path=figpath+'.pkl', data=data)
#save_param_set(params, seed, figpath)
#save_stats_csv(path=figpath+'.csv', seed=seed, data=data)
# optionally store replica graphs in files
#if store_replicas:
# out_dir = "output/replicas_{0}_{1}".format(getattr(G, "name", ""), timeNow()) # FIXME: add graph name
# os.mkdir(out_dir)
# for (G, replica_no) in zip(replicas, range(len(replicas))):
# graphutils.write_graph(G, path="{0}/{1}.gml".format(out_dir, replica_no))
return data
metrics = problem.get('metrics', metrics_default)
num_replicas = problem.get('num_replicas', default_num_replicas)
if type(graph_data) is str:
base_graph = graphutils.load_graph(path=graph_data)
base_graph.name = os.path.split(graph_data)[1]
else:
base_graph = graph_data
if not hasattr(base_graph, 'name'):
base_graph.name = problem.get('name', str(npr.randint(10000)))
gpath = 'output/'+os.path.split(base_graph.name)[1]+'_'+timeNow()+'.dot'
gpath_fig = gpath[:-3]+'eps'
graphutils.write_graph(G=base_graph, path=gpath)
visualizer_cmdl = 'sfdp -Nlabel="" -Nwidth=0.03 -Nfixedsize=true -Nheight=0.03 -Teps %s > %s &'%(gpath,gpath_fig)
print('Writing graph image: %s ..'%gpath_fig)
retCode = os.system(visualizer_cmdl)
replica_vs_original(G=base_graph, num_replicas=num_replicas, seed=1, params=params, metrics=metrics, title_infix='musketeer')
if __name__ == '__main__':
pass
#drake_hougardy_test()
#coarsening_test()
#coarsening_test2(1)
#edge_attachment_test(seed=None)
#print 'Statistical tests: this would take time ...'
#statistical_tests()
replica_vs_original(G=graphutils.load_graph('data-samples/mesh33.edges'), params={'edge_edit_rate':[0.01, 0.01]}, num_replicas=2, n_jobs=1)
if __name__ == '__main__':
init_options = initialize()
input_path = init_options['input_path']
params = init_options['params']
graph_type = init_options['graph_type']
output_path = init_options['output_path']
visualizer = init_options['visualizer']
verbose = init_options['verbose']
write_graph = init_options['write_graph']
planar = init_options['planar']
if verbose:
print('Loading: %s' % input_path)
G = graphutils.load_graph(path=input_path,
params={
'graph_type': graph_type,
'verbose': verbose
})
#G = generatesubgraphs.bfs_tree_custom(G,1000)
if verbose:
print('Generating ...')
new_G = algorithms.generate_graph(G, params=params, planar=planar)
#optional
#print graphutils.graph_graph_delta(G=G, new_G=new_G)
#new_G = nx.convert_node_labels_to_integers(new_G, 1, 'default', True)
#TODO: too many reports
if params.get('stats_report_on_all_levels', False):
model_Gs = [new_G.model_graph]
def replica_vs_original(seed=None,
figpath=None,
generator_func=None,
G=None,
params=None,
num_replicas=150,
title_infix='',
metrics=None,
intermediates=False,
n_jobs=-1):
#generate one or more replicas and compare them to the original graph
if seed == None:
seed = npr.randint(1E6)
print 'rand seed: %d' % seed
npr.seed(seed)
random.seed(seed)
if generator_func == None:
generator_func = algorithms.generate_graph
if G == None:
G = graphutils.load_graph(path='data-social/potterat_Hiv250.elist')
if metrics == None:
metrics = graphutils.default_metrics[:]
metrics = filter(lambda m: m['optional'] < 2, metrics)
if 'metric_runningtime_bound' in params:
mrtb = params['metric_runningtime_bound']
metrics = filter(lambda m: m['runningtime'] <= mrtb, metrics)
metrics = filter(lambda m: m['name'] not in ['avg flow closeness'],
metrics) #broken in NX 1.6
metrics.reverse()
if params == None:
params = {
'verbose': False,
'node_edit_rate': [0.05, 0.04, 0.03, 0.02, 0.01],
'edge_edit_rate': [0.05, 0.04, 0.03, 0.02, 0.01],
'node_growth_rate': [0],
'locality_bias_correction': 0.,
'enforce_connected': True,
'accept_chance_edges': 1.0,
'retain_intermediates': intermediates
}
if intermediates:
params['retain_intermediates'] = True
print 'Params:'
print params
print 'Metrics:'
print[metric['name'] for metric in metrics]
replicas = replicate_graph(G=G,
generator_func=generator_func,
num_replicas=num_replicas,
params=params,
title_infix=title_infix,
n_jobs=n_jobs)
jaccard_edges = evaluate_similarity(
base_graphs=G, graphs=replicas,
n_jobs=n_jobs) #this is actually a mean
vals_of_all = evaluate_metrics(graphs=[G] + replicas,
metrics=metrics,
n_jobs=n_jobs)
vals_of_graph = [metric_data[0] for metric_data in vals_of_all]
vals_of_replicas = [metric_data[1:] for metric_data in vals_of_all]
replica_statistics, figpath = plot_deviation(vals_of_replicas,
vals_of_graph, metrics,
figpath, jaccard_edges,
title_infix, seed,
getattr(G, 'name', ''))
#pylab.show()
data = {
'metrics': [met['name'] for met in metrics],
'name': getattr(G, 'name', ''),
'params': params,
'num_replicas': num_replicas,
'figpath': figpath
}
data[0] = replica_statistics
data[0].update({
'vals_of_replicas': vals_of_replicas,
'val_of_models': vals_of_graph,
'avg_jaccard_edges': jaccard_edges
})
if intermediates:
current_replicas = replicas
for level in xrange(
1,
max(len(params.get('node_edit_rate', [])),
len(params.get('edge_edit_rate', [])),
len(params.get('node_growth_rate', [])),
len(params.get('edge_growth_rate', [])))):
print 'LEVEL: %d' % level
coarse_models = [
r.coarser_graph.model_graph for r in current_replicas
]
coarse_replicas = [r.coarser_graph for r in current_replicas]
vals_of_models = evaluate_metrics(graphs=coarse_models,
metrics=metrics,
n_jobs=n_jobs)
vals_of_replicas = evaluate_metrics(graphs=coarse_replicas,
metrics=metrics,
n_jobs=n_jobs)
jaccard_edges = evaluate_similarity(base_graphs=coarse_models,
graphs=coarse_replicas,
n_jobs=n_jobs)
replica_statistics, dummy \
= plot_deviation(vals_of_replicas=vals_of_replicas, vals_of_graph=vals_of_models,
metrics=metrics, figpath=figpath + 'level%d'%level, jaccard_edges=jaccard_edges)
current_replicas = coarse_replicas
data[level] = replica_statistics
data[level].update({
'vals_of_replicas': vals_of_replicas,
'vals_of_models': vals_of_models,
'avg_jaccard_edges': jaccard_edges
})
graphutils.safe_pickle(path=figpath + '.pkl', data=data)
save_param_set(params, seed, figpath)
save_stats_csv(path=figpath + '.csv', seed=seed, data=data)
return data
def replica_vs_original(seed=None,
figpath=None,
generator_func=None,
G=None,
params=None,
num_replicas=150,
title_infix='',
metrics=None,
intermediates=False,
n_jobs=-1,
store_replicas=False):
"""generate one or more replicas and compare them to the original graph"""
if seed == None:
seed = npr.randint(1E6)
print('rand seed: %d' % seed)
npr.seed(seed)
random.seed(seed)
if generator_func == None:
generator_func = algorithms.generate_graph
if G == None:
G = graphutils.load_graph(path='data-social/potterat_Hiv250.elist')
if metrics == None:
metrics = graphutils.default_metrics[:]
metrics = [m for m in metrics if m['optional'] < 2]
if 'metric_runningtime_bound' in params:
mrtb = params['metric_runningtime_bound']
metrics = [m for m in metrics if m['runningtime'] <= mrtb]
metrics = [m for m in metrics
if m['name'] not in ['avg flow closeness']] #broken in NX 1.6
metrics.reverse()
if params == None:
params = {
'verbose': False,
'node_edit_rate': [0.05, 0.04, 0.03, 0.02, 0.01],
'edge_edit_rate': [0.05, 0.04, 0.03, 0.02, 0.01],
'node_growth_rate': [0],
'locality_bias_correction': 0.,
'enforce_connected': True,
'accept_chance_edges': 1.0,
'retain_intermediates': intermediates
}
if intermediates:
params['retain_intermediates'] = True
print('Params:')
print(params)
print('Metrics:')
print([metric['name'] for metric in metrics])
#if generator_func == algorithms.generate_graph:
#replicas = replicate_graph(G=G, generator_func=generator_func, num_replicas=num_replicas, params=params, title_infix=title_infix, n_jobs=n_jobs)
#else:
#replicas = replicate_graph(G=G, generator_func=generator_func, num_replicas=num_replicas, params=params,
# title_infix=title_infix, n_jobs=n_jobs)
replicas = read_all_files()
jaccard_edges = evaluate_similarity(
base_graphs=G, graphs=replicas,
n_jobs=n_jobs) #this is actually a mean
vals_of_all = evaluate_metrics(graphs=[G] + replicas,
metrics=metrics,
n_jobs=n_jobs)
vals_of_graph = [metric_data[0] for metric_data in vals_of_all]
vals_of_replicas = [metric_data[1:] for metric_data in vals_of_all]
replica_statistics, figpath = plot_deviation(vals_of_replicas,
vals_of_graph, metrics,
figpath, jaccard_edges,
title_infix, seed,
getattr(G, 'name', ''))
#pylab.show()
data = {
'metrics': [met['name'] for met in metrics],
'name': getattr(G, 'name', ''),
'params': params,
'num_replicas': num_replicas,
'figpath': figpath
}
data[0] = replica_statistics
data[0].update({
'vals_of_replicas': vals_of_replicas,
'val_of_models': vals_of_graph,
'avg_jaccard_edges': jaccard_edges
})
out_dir = "/home/varsha/Documents/final_results/Krongen/Boeing_normalized" + timeNow(
)
myfile = open(out_dir, 'w')
i = 0
for repl in vals_of_replicas:
for elem in repl:
nor_value = elem
if (vals_of_graph[i] != 0):
nor_value = float(elem) / vals_of_graph[i]
myfile.write(str(nor_value))
myfile.write('\t')
i += 1
myfile.write('\n')
myfile.close()
if intermediates:
current_replicas = replicas
for level in range(
1,
max(len(params.get('node_edit_rate', [])),
len(params.get('edge_edit_rate', [])),
len(params.get('node_growth_rate', [])),
len(params.get('edge_growth_rate', [])))):
print('LEVEL: %d' % level)
coarse_models = [
r.coarser_graph.model_graph for r in current_replicas
]
coarse_replicas = [r.coarser_graph for r in current_replicas]
vals_of_models = evaluate_metrics(graphs=coarse_models,
metrics=metrics,
n_jobs=n_jobs)
vals_of_replicas = evaluate_metrics(graphs=coarse_replicas,
metrics=metrics,
n_jobs=n_jobs)
jaccard_edges = evaluate_similarity(base_graphs=coarse_models,
graphs=coarse_replicas,
n_jobs=n_jobs)
replica_statistics, dummy \
= plot_deviation(vals_of_replicas=vals_of_replicas, vals_of_graph=vals_of_models,
metrics=metrics, figpath=figpath + 'level%d'%level, jaccard_edges=jaccard_edges)
current_replicas = coarse_replicas
data[level] = replica_statistics
data[level].update({
'vals_of_replicas': vals_of_replicas,
'vals_of_models': vals_of_models,
'avg_jaccard_edges': jaccard_edges
})
graphutils.safe_pickle(path=figpath + '.pkl', data=data)
save_param_set(params, seed, figpath)
save_stats_csv(path=figpath + '.csv', seed=seed, data=data)
# optionally store replica graphs in files
if store_replicas:
out_dir = "output/replicas_{0}_{1}".format(getattr(
G, "name", ""), timeNow()) # FIXME: add graph name
os.mkdir(out_dir)
for (G, replica_no) in zip(replicas, range(len(replicas))):
graphutils.write_graph(G,
path="{0}/{1}.gml".format(
out_dir, replica_no))
return data
def drake_hougardy_test():
import new_algs, graphutils
matching_weight = lambda G, mat: sum(G.edge[u][mat[u]].get('weight', 1.0)
for u in mat) / 2.0
def is_matching(mat):
G = nx.Graph()
G.add_edges_from(list(mat.items()))
for cc in nx.connected_components(G):
if len(cc) not in [0, 2]:
return False
return True
def is_maximal(G, mat):
for edge in G.edges():
if (edge[0] not in mat) and (edge[1] not in mat):
return False
return True
path = nx.path_graph(11)
for u, v, d in path.edges(data=True):
d['weight'] = max(u, v)**2
matching = graphutils.drake_hougardy_slow(path)
print('Matching slow: ' + str(matching))
print(' wt: ' + str(matching_weight(path, matching)))
matching = graphutils.drake_hougardy(path)
assert is_matching(matching)
assert is_maximal(path, matching)
print('Matching: ' + str(matching))
print(' wt: ' + str(matching_weight(path, matching)))
path_opt_m = nx.max_weight_matching(path)
print(' Opt Mat: ' + str(path_opt_m))
print(' wt: ' + str(matching_weight(path, path_opt_m)))
Gr2 = graphutils.load_graph('data-cyber-small/gr2.gml')
matching = graphutils.drake_hougardy_slow(Gr2)
print('Matching slow: ' + str(matching))
print(' wt: ' + str(matching_weight(Gr2, matching)))
matching = graphutils.drake_hougardy(Gr2)
assert is_matching(matching)
assert is_maximal(Gr2, matching)
print('Matching: ' + str(matching))
print(' wt: ' + str(matching_weight(Gr2, matching)))
gr2_opt_m = nx.max_weight_matching(Gr2)
print(' Opt Mat: ' + str(gr2_opt_m))
print(' wt: ' + str(matching_weight(Gr2, gr2_opt_m)))
#matching = graphutils.drake_hougardy(nx.erdos_renyi_graph(1000, 0.02))
num_test_graphs = 100
num_nodes = 400
edge_density = 0.02
seed = 0
for trial in range(num_test_graphs):
seed += 1
Gnp = nx.erdos_renyi_graph(num_nodes, edge_density, seed=seed)
print('Seed: %d' % seed)
matching = graphutils.drake_hougardy(Gnp)
assert is_matching(matching)
assert is_maximal(Gnp, matching)
wtDH = matching_weight(Gnp, matching)
print(' wt DH: ' + str(wtDH))
gnp_opt_m = nx.max_weight_matching(Gnp)
wtOpt = matching_weight(Gnp, gnp_opt_m)
print(' wt Opt: ' + str(wtOpt))
assert wtOpt <= 2 * wtDH
def replica_vs_original(seed=None, figpath=None, generator_func=None, G=None, params=None, num_replicas = 150, title_infix='', metrics=None, intermediates=False, n_jobs=-1):
#generate one or more replicas and compare them to the original graph
if seed==None:
seed = npr.randint(1E6)
print('rand seed: %d'%seed)
npr.seed(seed)
random.seed(seed)
if generator_func==None:
generator_func=algorithms.generate_graph
if G==None:
G = graphutils.load_graph(path='data-social/potterat_Hiv250.elist')
if metrics == None:
metrics = graphutils.default_metrics[:]
metrics = [m for m in metrics if m['optional'] < 2]
if 'metric_runningtime_bound' in params:
mrtb = params['metric_runningtime_bound']
metrics = [m for m in metrics if m['runningtime'] <= mrtb]
metrics = [m for m in metrics if m['name'] not in ['avg flow closeness']] #broken in NX 1.6
metrics.reverse()
if params == None:
params = {'verbose':False, 'node_edit_rate':[0.05, 0.04, 0.03, 0.02, 0.01],
'edge_edit_rate':[0.05, 0.04, 0.03, 0.02, 0.01], 'node_growth_rate':[0], 'locality_bias_correction':0., 'enforce_connected':True, 'accept_chance_edges':1.0,
'retain_intermediates':intermediates}
if intermediates:
params['retain_intermediates'] = True
print('Params:')
print(params)
print('Metrics:')
print([metric['name'] for metric in metrics])
replicas = replicate_graph(G=G, generator_func=generator_func, num_replicas=num_replicas, params=params, title_infix=title_infix, n_jobs=n_jobs)
jaccard_edges = evaluate_similarity(base_graphs=G, graphs=replicas, n_jobs=n_jobs) #this is actually a mean
vals_of_all = evaluate_metrics(graphs=[G]+replicas, metrics=metrics, n_jobs=n_jobs)
vals_of_graph = [metric_data[0] for metric_data in vals_of_all]
vals_of_replicas = [metric_data[1:] for metric_data in vals_of_all]
replica_statistics, figpath = plot_deviation(vals_of_replicas, vals_of_graph, metrics, figpath, jaccard_edges, title_infix, seed, getattr(G, 'name', ''))
#pylab.show()
data = {'metrics':[met['name'] for met in metrics], 'name':getattr(G, 'name', ''), 'params':params, 'num_replicas':num_replicas, 'figpath':figpath}
data[0] = replica_statistics
data[0].update({'vals_of_replicas':vals_of_replicas, 'val_of_models':vals_of_graph, 'avg_jaccard_edges':jaccard_edges})
if intermediates:
current_replicas = replicas
for level in range(1, max(len(params.get('node_edit_rate', [])), len(params.get('edge_edit_rate', [])), len(params.get('node_growth_rate', [])), len(params.get('edge_growth_rate', [])))):
print('LEVEL: %d'%level)
coarse_models = [r.coarser_graph.model_graph for r in current_replicas]
coarse_replicas = [r.coarser_graph for r in current_replicas]
vals_of_models = evaluate_metrics(graphs=coarse_models, metrics=metrics, n_jobs=n_jobs)
vals_of_replicas = evaluate_metrics(graphs=coarse_replicas, metrics=metrics, n_jobs=n_jobs)
jaccard_edges = evaluate_similarity(base_graphs=coarse_models, graphs=coarse_replicas, n_jobs=n_jobs)
replica_statistics, dummy \
= plot_deviation(vals_of_replicas=vals_of_replicas, vals_of_graph=vals_of_models,
metrics=metrics, figpath=figpath + 'level%d'%level, jaccard_edges=jaccard_edges)
current_replicas = coarse_replicas
data[level] = replica_statistics
data[level].update({'vals_of_replicas':vals_of_replicas, 'vals_of_models':vals_of_models, 'avg_jaccard_edges':jaccard_edges})
graphutils.safe_pickle(path=figpath+'.pkl', data=data)
save_param_set(params, seed, figpath)
save_stats_csv(path=figpath+'.csv', seed=seed, data=data)
return data
print graphutils.MUSKETEER_EXAMPLE_CMD
if __name__ == "__main__":
init_options = initialize()
input_path = init_options["input_path"]
params = init_options["params"]
graph_type = init_options["graph_type"]
output_path = init_options["output_path"]
visualizer = init_options["visualizer"]
verbose = init_options["verbose"]
write_graph = init_options["write_graph"]
if verbose:
print "Loading: %s" % input_path
G = graphutils.load_graph(path=input_path, params={"graph_type": graph_type, "verbose": verbose})
if verbose:
print "Generating ..."
new_G = algorithms.generate_graph(G, params=params)
# optional
# print graphutils.graph_graph_delta(G=G, new_G=new_G)
# new_G = nx.convert_node_labels_to_integers(new_G, 1, 'default', True)
# TODO: too many reports
if params.get("stats_report_on_all_levels", False):
model_Gs = [new_G.model_graph]
Gs = [new_G]
current_G = new_G.coarser_graph
while current_G.coarser_graph != None:
if __name__ == '__main__':
init_options = initialize()
input_path = init_options['input_path']
params = init_options['params']
graph_type = init_options['graph_type']
output_path = init_options['output_path']
visualizer = init_options['visualizer']
verbose = init_options['verbose']
write_graph = init_options['write_graph']
if verbose:
print('Loading: %s'%input_path)
G = graphutils.load_graph(path=input_path, params={'graph_type':graph_type, 'verbose':verbose})
if verbose:
print('Generating ...')
new_G = algorithms.generate_graph(G, params=params)
#optional
#print graphutils.graph_graph_delta(G=G, new_G=new_G)
#new_G = nx.convert_node_labels_to_integers(new_G, 1, 'default', True)
#TODO: too many reports
if params.get('stats_report_on_all_levels', False):
model_Gs = [new_G.model_graph]
Gs = [new_G]
current_G = new_G.coarser_graph
while current_G.coarser_graph != None:
metrics=metrics,
title_infix='musketeer')
def read_all_files():
import os
replicas = []
files = os.listdir(
"/home/varsha/Documents/final_results/Krongen/planar/Boeing/")
for file in files:
tmp = nx.read_gml(
"/home/varsha/Documents/final_results/Krongen/planar/Boeing/" +
file)
replica = tmp.to_undirected()
replicas.append(replica)
return replicas
if __name__ == '__main__':
pass
#drake_hougardy_test()
#coarsening_test()
#coarsening_test2(1)
#edge_attachment_test(seed=None)
#print 'Statistical tests: this would take time ...'
#statistical_tests()
replica_vs_original(G=graphutils.load_graph('data-samples/mesh33.edges'),
params={'edge_edit_rate': [0.01, 0.01]},
num_replicas=2,
n_jobs=25)
gpath_fig = gpath[:-3]+'eps'
graphutils.write_graph(G=base_graph, path=gpath)
visualizer_cmdl = 'sfdp -Nlabel="" -Nwidth=0.03 -Nfixedsize=true -Nheight=0.03 -Teps %s > %s &'%(gpath,gpath_fig)
print('Writing graph image: %s ..'%gpath_fig)
retCode = os.system(visualizer_cmdl)
replica_vs_original(G=base_graph, num_replicas=num_replicas, seed=1, params=params, metrics=metrics, title_infix='musketeer')
def read_all_files(dir):
print(dir)
import os
replicas = []
print(dir)
files = os.listdir(dir)
for file in files:
tmp = nx.read_edgelist(dir+file)
replica = tmp.to_undirected()
replicas.append(replica)
return replicas
if __name__ == '__main__':
pass
#drake_hougardy_test()
#coarsening_test()
#coarsening_test2(1)
#edge_attachment_test(seed=None)
#print 'Statistical tests: this would take time ...'
#statistical_tests()
replica_vs_original(G=graphutils.load_graph('data-samples/mesh33.edges'), params={'edge_edit_rate':[0.01, 0.01]}, num_replicas=2, n_jobs=25)