def grow_exact_size_hrg_graphs_from_prod_rules(prod_rules, gname, n, runs=1):
"""
Args:
rules: production rules (model)
gname: graph name
n: target graph order (number of nodes)
runs: how many graphs to generate
Returns: list of synthetic graphs
"""
if n <= 0: sys.exit(1)
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in prod_rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
#print "n", n
num_nodes = n
if DEBUG: print "Starting max size"
g.set_max_size(num_nodes)
if DEBUG: print "Done with max size"
hstars_lst = []
for i in range(0, runs):
rule_list = g.sample(num_nodes)
hstar = phrg.grow(rule_list, g)[0]
hstars_lst.append(hstar)
return hstars_lst
def ba_control_hrg(v_lst):
grow_graphs = False
v_lst = [int(n) for n in v_lst] # set of nodes to generate BA graphs
data = []
prules_lst = []
for n_v in v_lst:
# nxgobj = nx.barabasi_albert_graph(n_v, np.random.choice(range(1,n_v)))
nxgobj = nx.barabasi_albert_graph(n_v,3)
nxgobj.name = "ba_%d_%d" %(nxgobj.number_of_nodes(), nxgobj.number_of_edges())
print "ba", nxgobj.number_of_nodes(), nxgobj.number_of_edges()
data.append(nxgobj)
prod_rules = phrg.probabilistic_hrg_deriving_prod_rules(nxgobj)
df = pd.DataFrame(list(prod_rules))
out_base_fname = "ba_cntrl_%d"%(n_v)
ofname = "Results/" + out_base_fname + ".tsv" #_________________
df.to_csv(ofname, sep="\t", header=False, index=False)
prules_lst.append(prod_rules)
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in df.values:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
num_nodes = nxgobj.number_of_nodes()
print " ","Starting max size", 'n=', num_nodes
g.set_max_size(num_nodes)
print " ","Done with max size"
Hstars = []
num_samples = 10
for i in range(0, num_samples):
try:
rule_list = g.sample(num_nodes)
except Exception, e:
print str(e)
traceback.print_exc()
continue #sys.exit(1)
hstar = phrg.grow(rule_list, g)[0]
Hstars.append(hstar)
print " ", 'Save BA production rules'
if os.path.exists(ofname):
print '\tSaved to disk:',ofname
if 0:
metricx = ['degree','clust', 'hop', 'gcd']
metrics.network_properties([nxgobj], metricx, Hstars, name=nxgobj.name, out_tsv=False)
def Hstar_Graphs_Control(G, graph_name, axs=None):
# Derive the prod rules in a naive way, where
prod_rules = phrg.probabilistic_hrg_learning(G)
pp.pprint(prod_rules)
exit()
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in prod_rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
num_nodes = G.number_of_nodes()
print "Starting max size", 'n=', num_nodes
g.set_max_size(num_nodes)
print "Done with max size"
Hstars = []
num_samples = 20
print '*' * 40
for i in range(0, num_samples):
rule_list = g.sample(num_nodes)
hstar = phrg.grow(rule_list, g)[0]
Hstars.append(hstar)
# if 0:
# g = nx.from_pandas_dataframe(df, 'src', 'trg', edge_attr=['ts'])
# draw_degree_whole_graph(g,axs)
# draw_degree(Hstars, axs=axs, col='r')
# #axs.set_title('Rules derived by ignoring time')
# axs.set_ylabel('Frequency')
# axs.set_xlabel('degree')
if 0:
# metricx = [ 'degree','hops', 'clust', 'assort', 'kcore','eigen','gcd']
metricx = ['gcd']
# g = nx.from_pandas_dataframe(df, 'src', 'trg',edge_attr=['ts'])
# graph_name = os.path.basename(f_path).rstrip('.tel')
if DBG: print ">", graph_name
metrics.network_properties([G],
metricx,
Hstars,
name=graph_name,
out_tsv=True)
def grow_exact_size_hrg_graphs_from_prod_rules(prod_rules, gname, n, runs=1):
"""
Args:
rules: production rules (model)
gname: graph name
n: target graph order (number of nodes)
runs: how many graphs to generate
Returns: list of synthetic graphs
"""
nslog("grow_exact_size_hrg_graphs_from_prod_rules")
DBG = True
if n <= 0: sys.exit(1)
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in prod_rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
print
print "Added rules HRG (pr", len(prod_rules), ", n,", n, ")"
exit() # temp pls remove me
num_nodes = n
if DBG: print "Starting max size"
g.set_max_size(num_nodes)
if DBG: print "Done with max size"
hstars_lst = []
print " ",
for i in range(0, runs):
print '>',
rule_list = g.sample(num_nodes)
hstar = phrg.grow(rule_list, g)[0]
hstars_lst.append(hstar)
return hstars_lst
# ['r8.1', 'A,B,C,D,E,F', ['0,B:T', '0,C:T', '0,D:T', '0,E:T', '0,F:T', '0,A:T', 'A,0,D,E,F:N'] ,0.5]
# ]
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
print 'Grammar g loaded.'
# Synthetic Graphs
#num_nodes = int(sys.argv[-1])
g.set_max_size(num_nodes)
hStars = []
for i in range(20):
rule_list = g.sample(num_nodes)
hstar = phrg.grow(rule_list, g)[0]
hStars.append(hstar)
print i, hstar.number_of_nodes(), hstar.number_of_edges()
metricx = ['degree', 'hops', 'clust', 'gcd']
metrics.network_properties([G], metricx, hStars, name=graph_name, out_tsv=True)
# parser = get_parser()
# args = vars(parser.parse_args())
# try:
# main(args)
# except Exception, e:
# print str(e)
# traceback.print_exc()
# sys.exit(1)
# sys.exit(0)