def will_prod_rules_fire(prs_files_lst, nbr_nodes):
if not len(prs_files_lst): return
ret_val = []
for fname in prs_files_lst:
# Read the subset of prod rules
df = pd.read_csv(fname,
header=None,
sep="\t",
dtype={
0: str,
1: list,
2: list,
3: float
})
g = pcfg.Grammar('S')
from td_isom_jaccard_sim import listify_rhs
for (id, lhs, rhs, prob) in df.values:
rhs = listify_rhs(rhs)
g.add_rule(pcfg.Rule(id, lhs, rhs, float(prob)))
num_nodes = nbr_nodes[0]
# print "Starting max size", 'n=', num_nodes[0], type(num_nodes)
return ret_val
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)
print runs
print n
print gname
for i,x in enumerate(prod_rules):
print i,' ', x[:1]
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in prod_rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
print '... pcfg.Grammar'
g.set_max_size(n)
print "Done with max size"
if DBG: print '*' * 40
hstars_lst = []
for i in range(0, runs):
rule_list = g.sample(n)
print 'g.sample'
hstar = PHRG.grow(rule_list, g)[0]
hstars_lst.append(hstar)
return hstars_lst
def Hstar_Graphs_Ignore_Time(df, graph_name, tslices, axs):
if len(df.columns) == 3:
G = nx.from_pandas_dataframe(df, 'src', 'trg', edge_attr='ts')
else:
G = nx.from_pandas_dataframe(df, 'src', 'trg')
# force to unrepeated edgesA
if 0: print nx.info(G)
G = G.to_undirected()
if 0: print nx.info(G)
exit()
# Derive the prod rules in a naive way, where
prod_rules = PHRG.probabilistic_hrg_learning(G)
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"
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 1:
# metricx = [ 'degree','hops', 'clust', 'assort', 'kcore','eigen','gcd']
metricx = ['eigen']
g = nx.from_pandas_dataframe(df, 'src', 'trg', edge_attr=['ts'])
# graph_name = os.path.basename(f_path).rstrip('.tel')
print ">", graph_name
metrics.network_properties([g],
metricx,
Hstars,
name=graph_name,
out_tsv=True)
def Growing_Network_Using_Final_State_ProdRules(pddf, prod_rules, nSlices,
kSlice, axs):
'''
Grow a synthetic graph up to the end of block kSlice using HRG rules
from the final (whole) state of the graph.
pddf: pandas df
prod_rules: production rules learned on the entire graph
nSlices: total number of blocks (pseudo-states of the graph)
kSlice: the current slice
axs: axes to plot to
'''
span = (pddf['ts'].max() - pddf['ts'].min()) / nSlices
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in prod_rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
# mask = (pddf['ts'] >= pddf['ts'].min()+ span*kSlice) & (pddf['ts'] < pddf['ts'].min()+ span*(kSlice +1))
mask = (pddf['ts'] >= pddf['ts'].min()) & (
pddf['ts'] < pddf['ts'].min() + span * (kSlice + 1))
ldf = pddf.loc[mask]
G = nx.from_pandas_dataframe(ldf, 'src', 'trg', ['ts'])
num_nodes = G.number_of_nodes()
print "Starting max size"
g.set_max_size(num_nodes)
print "Done with max size"
num_samples = 20
print '*' * 40
tdf = pd.DataFrame()
for i in range(0, num_samples):
rule_list = g.sample(num_nodes)
hstar = PHRG.grow(rule_list, g)[0]
df = pd.DataFrame.from_dict(hstar.degree().items())
#
tdf = pd.concat([df.groupby([1]).count(),
df.groupby([1]).count()],
axis=1)
tdf = tdf[0].mean(axis=1)
tdf.plot(ax=axs, color='r', label='Orig')
# Orig Graph
tdf = pd.DataFrame.from_dict(G.degree().items())
gb = tdf.groupby([1]).count()
gb[0].plot(ax=axs, color='b', label='Orig')
axs.set_xscale('log')
'''
def probe_stacked_prs_likelihood_tofire(df, fname="", nbr_nodes=0):
Info("probe stacked prs likelihood tofire")
g = pcfg.Grammar('S')
df = df[['rnbr', 'lhs', 'rhs',
'prob']] # ToDo: need to drop the gname column
for (id, lhs, rhs, prob) in df.values.tolist(): # 21Nov17
g.add_rule(pcfg.Rule(id, lhs, rhs, float(prob)))
num_nodes = int(nbr_nodes)
g.set_max_size(num_nodes)
try:
g.set_max_size(num_nodes)
except Exception, e: # print "Done with max size"
print "\t:", e
# return False
os._exit(1)
def Hstar_Graphs_Control(G, graph_name, axs):
print '-', Hstar_Graphs_Control, '-'
# Derive the prod rules in a naive way, where
prod_rules = PHRG.probabilistic_hrg_learning(G)
print prod_rules
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 1:
# metricx = [ 'degree','hops', 'clust', 'assort', 'kcore','eigen','gcd']
metricx = ['degree', '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
"""
if n <= 0: sys.exit(1)
# print runs
# for i,x in enumerate(prod_rules):
# print i,' ', x
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in prod_rules:
rhs = [f[1:-1] for f in re.findall("'.+?'", rhs)]
prob = float(prob)
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
# # mask = (pddf['ts'] >= pddf['ts'].min()+ span*kSlice) & (pddf['ts'] < pddf['ts'].min()+ span*(kSlice +1))
# mask = (pddf['ts'] >= pddf['ts'].min()) & (pddf['ts'] < pddf['ts'].min() + span * (kSlice + 1))
# ldf = pddf.loc[mask]
#
# G = nx.from_pandas_dataframe(ldf, 'src', 'trg', ['ts'])
#
num_nodes = n
if DBG: print "Starting max size"
g.set_max_size(num_nodes)
if DBG: print "Done with max size"
#
# num_samples = 20
if DBG: print '*' * 40
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)
# print rule_list
return hstars_lst
def hstar_fixed_graph_gen(args):
import networkx as nx
orig_fname = args['grow'][0]
gn = graph_name(orig_fname)
if os.path.exists("../datasets/{}.p".format(gn)):
origG = nx.read_gpickle("../datasets/{}.p".format(gn))
else:
print("we load edgelist into an nx.obj")
prs_files = glob("../ProdRules/{}*prs".format(gn))
for f in prs_files:
prod_rules = get_prod_rules(f)
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in prod_rules:
# print (id, lhs, rhs, prob)
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
# exit() # Takes this out
# ToDo: We nee to get these rules in the right format
num_nodes = origG.number_of_nodes()
print "Starting max size"
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)
import pickle
pickle.dump({
'origG': origG,
'hstars': Hstars
}, open('../Results/{}_hstars.p'.format(gn), "wb"))
if os.path.exists('../Results/{}_hstars.p'.format(gn)):
print("Pickle written")
def grow_graphs_using_rules(production_rules, n=0, recrncs=1):
from PHRG import grow
if n == 0:
return
prod_rules = production_rules
rules = []
id = 0
for k, v in prod_rules.iteritems():
sid = 0
for x in prod_rules[k]:
rhs = re.findall("[^()]+", x)
rules.append(
("r%d.%d" % (id, sid), "%s" % re.findall("[^()]+", k)[0], rhs,
prod_rules[k][x]))
#print ("r%d.%d" % (id, sid), "%s" % re.findall("[^()]+", k)[0], rhs, prod_rules[k][x])
sid += 1
id += 1
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
print "Starting max size"
num_nodes = n
g.set_max_size(num_nodes)
print "Done with max size"
Hstars = []
for i in range(0, recrncs):
rule_list = g.sample(num_nodes)
hstar = grow(rule_list, g)[0]
print '\tPHRG -> run:', i, str(hstar.number_of_nodes()), str(
hstar.number_of_edges())
g = hstar
Hstars.append(hstar)
return Hstars
def synthetic_graph_generator(ref, graph_model):
G = ref
synth_graph = None
n = ref.number_of_nodes()
if 'hrg' in graph_model:
prod_rules = PHRG.probabilistic_hrg_deriving_prod_rules(
G) # derive rules
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 = []
rule_list = g.sample(num_nodes)
synth_graph = PHRG.grow(rule_list, g)[0]
return synth_graph
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
"""
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, ")"
num_nodes = n
if DBG: print "Starting max size ..."
t_start = time.time()
g.set_max_size(num_nodes)
print "Done with max size, took %s seconds" % (time.time() - t_start)
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
def probabilistic_hrg(G, num_samples=1):
graphletG = []
#print G.number_of_nodes()
#print G.number_of_edges()
G.remove_edges_from(G.selfloop_edges())
giant_nodes = max(nx.connected_component_subgraphs(G), key=len)
G = nx.subgraph(G, giant_nodes)
num_nodes = G.number_of_nodes()
# print G.number_of_nodes()
# print G.number_of_edges()
graph_checks(G)
print
print "--------------------"
print "-Tree Decomposition-"
print "--------------------"
if num_nodes >= 500:
for Gprime in gs.rwr_sample(G, 2, 100):
T = td.quickbb(Gprime)
root = list(T)[0]
T = td.make_rooted(T, root)
T = binarize(T)
root = list(T)[0]
root, children = T
td.new_visit(T, G, prod_rules)
else:
T = td.quickbb(G)
root = list(T)[0]
T = td.make_rooted(T, root)
T = binarize(T)
root = list(T)[0]
root, children = T
td.new_visit(T, G, prod_rules)
print
print "--------------------"
print "- Production Rules -"
print "--------------------"
for k in prod_rules.iterkeys():
#print k
s = 0
for d in prod_rules[k]:
s += prod_rules[k][d]
for d in prod_rules[k]:
prod_rules[k][d] = float(prod_rules[k][d]) / float(s) # normailization step to create probs not counts.
#print '\t -> ', d, prod_rules[k][d]
rules = []
id = 0
for k, v in prod_rules.iteritems():
sid = 0
for x in prod_rules[k]:
rhs = re.findall("[^()]+", x)
rules.append(("r%d.%d" % (id, sid), "%s" % re.findall("[^()]+", k)[0], rhs, prod_rules[k][x]))
#print ("r%d.%d" % (id, sid), "%s" % re.findall("[^()]+", k)[0], rhs, prod_rules[k][x])
sid += 1
id += 1
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob))
print "Starting max size"
g.set_max_size(num_nodes)
print "Done with max size"
Hstars = []
for i in range(0, num_samples):
rule_list = g.sample(num_nodes)
# print rule_list
hstar = grow(rule_list, g)[0]
# print "H* nodes: " + str(hstar.number_of_nodes())
# print "H* edges: " + str(hstar.number_of_edges())
Hstars.append(hstar)
return (Hstars)
G = nx.Graph()
G.add_edge(1, 2)
G.add_edge(2, 3)
G.add_edge(2, 4)
G.add_edge(3, 4)
G.add_edge(3, 5)
G.add_edge(4, 6)
G.add_edge(5, 6)
G.add_edge(1, 5)
num_nodes = G.number_of_nodes()
prod_rules = {}
p_rules = probabilistic_hrg(G)
g = pcfg.Grammar('S')
for (id, lhs, rhs, prob) in p_rules:
g.add_rule(pcfg.Rule(id, lhs, rhs, prob, True))
print '> prod rules added to Grammar g' #
g.set_max_size(num_nodes * 4)
print '> max-size set.'
rids = g.sample(num_nodes * 4)
print rids
new_graph = pg.gen(rids, g)
print "nodes: ", new_graph.number_of_nodes()
print "edges: ", new_graph.number_of_edges()
print
print