def local_alg():
dir_content = []
graph_index = -1
percent_comm = -1
k1 = -1
k2 = -1
edges_to_recommend = -1
print '########## Local section ########## \nAvailable files: \n'
for root, dirs, files in os.walk('../outcomes'):
for i in range(0,len(files)):
print str(i)+'-'+files[i]
dir_content.append(files[i])
while (graph_index >= len(dir_content) or graph_index < 0):
try:
graph_index = int(raw_input("\nChoose from one of these graphs and type its corresponding index: \n"))
except EOFError as error:
print '\nBye!'
sys.exit(0)
try:
percent_comm = float(raw_input("\nChoose the percentage of nodes of each community to consider: \n"))
k1 = int(raw_input("\nChoose the number of best nodes of partition 0 (depends on strategy): \n"))
k2 = int(raw_input("\nChoose the number of best nodes of partition 1 (depends on strategy): \n"))
edges_to_recommend = int(raw_input("\nChoose the number of edges to recommend: \n"))
if (percent_comm > 1 or percent_comm <= 0) or (k1 <= 0 or k2 <= 0) or (edges_to_recommend <= 0):
print 'Bad input'
sys.exit(1)
except EOFError as error:
print '\nBye!'
sys.exit(0)
graph_name = (dir_content[graph_index].split('.'))[0]
path = '../outcomes/'+graph_name+'.pickle'
print '\n##### Chosen graph: %s #####\n'%graph_name
print '########---Study of ratio in_degree/(out_degree + 1) in its average and its variance. \nStudy of the trend of (in_degree, out_degree + 1, ratio) too.---########'
print '-----------------------------------------------------------'
avg_in_out = 0.0
var_in_out = 0.0
scanned_nodes = 0
g = nx.read_gpickle(path)
nodes = g.nodes()
minimum = min(len(nodes),100)
for node in nodes:
in_deg = g.in_degree(node)
out_deg = g.out_degree(node)
ratio = (float(in_deg)/float(out_deg+1))
'''Welford's method:'''
old_avg = avg_in_out
avg_in_out = (avg_in_out*scanned_nodes + ratio)/(scanned_nodes + 1)
var_in_out = var_in_out + (ratio - avg_in_out)*(ratio - old_avg)
scanned_nodes += 1
if scanned_nodes < minimum:
print "%d,%d,%.3f"%(in_deg,(out_deg+1),ratio)
var_in_out /= (len(nodes)-1) # correct variance
print path+" avg_in_out = %13.10f"%avg_in_out
print path+" var_in_out = %13.10f"%var_in_out
print '\n'
print '##########################---SIMULATIONS---###############################'
R = []
comment = ["Opt Total Decrease RWC -- in_degree type (HIGH-TO-HIGH) : ","Opt Total Decrease RWC -- ratio type : ","Opt Total Decrease RWC -- betweenness centrality : "]
strategies = ['in_deg','ratio','betwn']
start_time = time.time()
for i in range(0,len(strategies)):
graphData = ut.computeData(None, g, 0.85, i, percent_community=percent_comm)
print "---------------------------------------------------------------------------------------------------------------------------"
r = rwc_lib.rwc(0.85, graphData)
print "RWC score =%13.10f"%r[0] #%width.precisionf
print "---------------------------------------------------------------------------------------------------------------------------"
sorted_x_y = (graphData[0],graphData[1])
sorted_dp = rwc_lib.deltaPredictorOrdered(None, g, 0.85, k1, k2, sorted_x_y, graphData, r)
R.append(rwc_lib.fagin(sorted_dp,edges_to_recommend))
print R[i][1]
(new_graph,opt,ratio,max_opt) = ut.addEdgeToGraph(path,None,R[i][0],R[i][1],graph_name,strategies[i])
mygraphData = ut.computeData(None, new_graph, 0.85, i, percent_community=percent_comm, comms_part=(graphData[8],graphData[9]))
r1 = rwc_lib.rwc(0.85, mygraphData)
print "RWC score after addiction of accepted edges =%13.10f"%r1[0] #%width.precisionf
print comment[i],"%13.10f"%opt
print "Maximum Optimum Decrease RWC : =%13.10f"%max_opt
print "Real Total Decrease RWC =%13.10f"%(r[0]-r1[0]), " acceptance_ratio :",ratio
print "-----------------------------------------------"
print "-------------------------------------------------End of simulation---------------------------------------------------------"
print "Time elapsed: %f"%(time.time() - start_time)
def greedy_alg():
dir_content = []
graph_index = -1
k1 = -1
k2 = -1
edges_to_recommend = -1
print '########## Greedy section ########## \nAvailable files: \n'
for root, dirs, files in os.walk('../outcomes'):
for i in range(0,len(files)):
print str(i)+'-'+files[i]
dir_content.append(files[i])
while (graph_index >= len(dir_content) or graph_index < 0):
try:
graph_index = int(raw_input("\nChoose from one of these graphs and type its corresponding index: \n"))
except EOFError as error:
print '\nBye!'
sys.exit(0)
try:
k1 = int(raw_input("\nChoose the number of best nodes of partition 0 (depends on strategy): \n"))
k2 = int(raw_input("\nChoose the number of best nodes of partition 1 (depends on strategy): \n"))
edges_to_recommend = int(raw_input("\nChoose the number of edges to recommend: \n"))
if (k1 <= 0 or k2 <= 0) or (edges_to_recommend <= 0):
print 'Bad input'
sys.exit(1)
except EOFError as error:
print '\nBye!'
sys.exit(0)
graph_name = (dir_content[graph_index].split('.'))[0]
path = '../outcomes/'+graph_name+'.pickle'
print '\n##### Chosen graph: %s #####\n'%graph_name
print '########---Study of ratio in_degree/(out_degree + 1) in its average and its variance. Study of the trend of (in_degree, out_degree + 1, ratio) too.---########'
print '-----------------------------------------------------------'
avg_in_out = 0.0
var_in_out = 0.0
scanned_nodes = 0
g = nx.read_gpickle(path)
nodes = g.nodes()
minimum = min(len(nodes),100)
for node in nodes:
in_deg = g.in_degree(node)
out_deg = g.out_degree(node)
ratio = (float(in_deg)/float(out_deg+1))
'''Welford's method:'''
old_avg = avg_in_out
avg_in_out = (avg_in_out*scanned_nodes + ratio)/(scanned_nodes + 1)
var_in_out = var_in_out + (ratio - avg_in_out)*(ratio - old_avg)
scanned_nodes += 1
if scanned_nodes < minimum:
print "%d,%d,%.3f"%(in_deg,(out_deg+1),ratio)
var_in_out /= (len(nodes)-1) # correct variance
print path+" avg_in_out = %13.10f"%avg_in_out
print path+" var_in_out = %13.10f"%var_in_out
print '\n'
print '##########################---SIMULATIONS---###############################'
strategies = ['in_deg_greedy','ratio_greedy','betwn_greedy']
initGraphData = ut.computeData(None, g, 0.85, 0, percent_community=1)
print "---------------------------------------------------------------------------------------------------------------------------"
r = rwc_lib.rwc(0.85, initGraphData)
print "Initial RWC score =%13.10f"%r[0] #%width.precisionf
print "---------------------------------------------------------------------------------------------------------------------------"
start_time = time.time()
#Loop over strategies:
for i in range(0,len(strategies)):
round_graph = nx.read_gpickle(path)
round_data = initGraphData;
#sorted_x_y = ut.sortNodes(None, g, initGraphData[8], initGraphData[9], i)#sorted by type sorting 'i'
r1= r;
print strategies[i]+' \n'
print "rwc initial",r1[0], len(round_graph.edges())
for k in range(0,edges_to_recommend):
R = []
sorted_x_y = ut.sortNodes(None, round_graph, round_data[8], round_data[9], i)#sorted by type sorting 'i'
sorted_dp = rwc_lib.deltaPredictorOrdered(None, round_graph, 0.85, k1, k2, sorted_x_y, round_data, r1)
R.append(rwc_lib.fagin(sorted_dp,1))
print R[0][1]
(round_graph,opt,ratio,max_opt) = ut.addEdgeToGraph(None,round_graph,R[0][0],R[0][1],graph_name,strategies[i])
#We assume that communities and partition do not change
round_data = ut.computeData(None, round_graph, 0.85, i, percent_community=1, comms_part=(round_data[8],round_data[9]))
r1 = rwc_lib.rwc(0.85, round_data)
print "rwc",r1[0]
print "RWC score after addiction of accepted edges =%13.10f"%r1[0] #%width.precisionf
print "Real Total Decrease RWC =%13.10f"%(r[0]-r1[0])
print "-----------------------------------------------"
print "-------------------------------------------------End of simulation---------------------------------------------------------"
print "Time elapsed: %f"%(time.time() - start_time)
print "---------------------------------------------------------------------------------------------------------------------------"
r = rwc_lib.rwc(0.85, graphData)
print "RWC score =%13.10f" % r[0] #%width.precisionf
print "---------------------------------------------------------------------------------------------------------------------------"
sorted_x_y = (graphData[0], graphData[1])
sorted_dp = rwc_lib.deltaPredictorOrdered(None, g, 0.85, k1, k2,
sorted_x_y, graphData, r)
R.append(rwc_lib.fagin(sorted_dp, edges_to_recommend))
print R[i][1]
(new_graph, opt, ratio,
max_opt) = ut.addEdgeToGraph(path, None, R[i][0], R[i][1], graph_name,
strategies[i])
mygraphData = ut.computeData(None,
new_graph,
0.85,
i,
percent_community=percent_comm,
comms_part=(graphData[8], graphData[9]))
r1 = rwc_lib.rwc(0.85, mygraphData)
print "RWC score after addiction of accepted edges =%13.10f" % r1[
0] #%width.precisionf
print comment[i], "%13.10f" % opt
print "Maximum Optimum Decrease RWC : =%13.10f" % max_opt
print "Real Total Decrease RWC =%13.10f" % (
r[0] - r1[0]), " acceptance_ratio :", ratio
print "-----------------------------------------------"
print "---------------------------------------------------------------------------------------------------------------------------"
r = rwc_lib.rwc(0.85, graphData)
print "RWC score =%13.10f" % r[0] #%width.precisionf
print "---------------------------------------------------------------------------------------------------------------------------"
sorted_dp = rwc_lib.deltaPredictorOrdered(None, g, 0.85, 40, 40,
graphData, r)
R.append(rwc_lib.fagin(sorted_dp, 20))
print R[i][1]
(new_graph, opt, ratio, max_opt) = ut.addEdgeToGraph(
'../outcomes/retweet_graph_beefban.pickle', R[i][0], R[i][1],
graph_name, strategies[i])
mygraphData = ut.computeData(None,
new_graph,
0.85,
i,
percent_community=0.5)
r1 = rwc_lib.rwc(0.85, mygraphData)
print "RWC score after addiction of accepted edges =%13.10f" % r1[
0] #%width.precisionf
print comment[i], "%13.10f" % opt
print "Maximum Optimum Decrease RWC : =%13.10f" % max_opt
print "Real Total Decrease RWC =%13.10f" % (
r[0] - r1[0]), " acceptance_ratio :", ratio
print "-----------------------------------------------"
r1= r;
print strategies[i]+' \n'
print "rwc initial",r1[0], len(round_graph.edges())
for k in range(0,edges_to_recommend):
R = []
sorted_x_y = ut.sortNodes(None, round_graph, round_data[8], round_data[9], i)#sorted by type sorting 'i'
sorted_dp = rwc_lib.deltaPredictorOrdered(None, round_graph, 0.85, k1, k2, sorted_x_y, round_data, r1)
R.append(rwc_lib.fagin(sorted_dp,1))
print R[0][1]
(round_graph,opt,ratio,max_opt) = ut.addEdgeToGraph(None,round_graph,R[0][0],R[0][1],graph_name,strategies[i])
#We assume that communities and partition do not change
round_data = ut.computeData(None, round_graph, 0.85, i, percent_community=1, comms_part=(round_data[8],round_data[9]))
r1 = rwc_lib.rwc(0.85, round_data)
print "rwc",r1[0]
print "RWC score after addiction of accepted edges =%13.10f"%r1[0] #%width.precisionf
print "Real Total Decrease RWC =%13.10f"%(r[0]-r1[0])
print "-----------------------------------------------"
print "-------------------------------------------------End of simulation---------------------------------------------------------"