case-study.py

import networkx as nx
from time import time

from graphGeneration import Cora, CiteSeer, PubMed, connSW, ER
from IM import eigen, degree, pi, sigma, Netshield
from score import scoreIC, Y, SobolT, sobols, IE
from simulation import simulationIC, simulationLT
import statistics as s

# g, config = Cora()
g, config = CiteSeer()
# g, config = PubMed()
# g, config = ER()
# g, config = connSW()

print("Graph is on.")
print(nx.info(g))

print('------------------------------------------------')
print('degree')
start = time()
set = degree(g,config,5)
end = time()
print('time: ', end - start)
print('degree: ', set)

# Distance among seeds
for i in range(len(set)-1):
  for j in range(i+1, len(set)):
    print(set[i],'-',set[j],':',nx.shortest_path_length(g, source = set[i], target = set[j]))

# Sobol Total of each seed
df = simulationIC(100,g,set,config)
print(df)
EY,VY = Y(df)
print('Expectation: ', EY)
print('Variance: ', VY)

Sobols = sobols(df,set)
for key in Sobols:
  print(key, ' : ', Sobols[key])

ST = SobolT(df,set)
for node in ST.keys():
  print(node, ': ', ST[node]/VY)



ie, std = IE(df,set)
print('IE:', ie)

# Marginal Contribution
MC0 = []
MC1 = []
MC2 = []
MC3 = []
MC4 = []
ST0 = []
ST1 = []
ST2 = []
ST3 = []
ST4 = []
for i in range(5):

  df = simulationIC(100, g, set, config)
  ie, std = IE(df, set)
  EY, VY = Y(df)
  ST = SobolT(df, set)

  node0 = set[0]
  ST0.append(ST[node0] / VY)
  node1 = set[1]
  ST1.append(ST[node1] / VY)
  node2 = set[2]
  ST2.append(ST[node2] / VY)
  node3 = set[3]
  ST3.append(ST[node3] / VY)
  node4 = set[4]
  ST4.append(ST[node4] / VY)
  # set[0]
  sim = df[(df[set[0]] == 0) & (df[set[1]] == 1) & (df[set[2]] == 1) & (df[set[3]] == 1) & (df[set[4]] == 1)]
  Esim = s.mean(sim['result']) - ie
  MC0.append(Esim)

  # set[1]

  sim = df[(df[set[0]] == 1) & (df[set[1]] == 0) & (df[set[2]] == 1) & (df[set[3]] == 1) & (df[set[4]] == 1)]
  Esim = s.mean(sim['result']) - ie
  MC1.append(Esim)

  # set[2]

  sim = df[(df[set[0]] == 1) & (df[set[1]] == 1) & (df[set[2]] == 0) & (df[set[3]] == 1) & (df[set[4]] == 1)]
  Esim = s.mean(sim['result']) - ie
  MC2.append(Esim)

  # set[3]

  sim = df[(df[set[0]] == 1) & (df[set[1]] == 1) & (df[set[2]] == 1) & (df[set[3]] == 0) & (df[set[4]] == 1)]
  Esim = s.mean(sim['result']) - ie
  MC3.append(Esim)

  # set[4]

  sim = df[(df[set[0]] == 1) & (df[set[1]] == 1) & (df[set[2]] == 1) & (df[set[3]] == 1) & (df[set[4]] == 0)]
  Esim = s.mean(sim['result']) - ie
  MC4.append(Esim)

print('degree: ', set)
print(s.mean(MC0), ' +- ', s.stdev(MC0))
print(s.mean(MC1), ' +- ', s.stdev(MC1))
print(s.mean(MC2), ' +- ', s.stdev(MC2))
print(s.mean(MC3), ' +- ', s.stdev(MC3))
print(s.mean(MC4), ' +- ', s.stdev(MC4))
print("ST-----------------------------")
print(s.mean(ST0), ' +- ', s.stdev(ST0))
print(s.mean(ST1), ' +- ', s.stdev(ST1))
print(s.mean(ST2), ' +- ', s.stdev(ST2))
print(s.mean(ST3), ' +- ', s.stdev(ST3))
print(s.mean(ST4), ' +- ', s.stdev(ST4))

rank = []
for node in sorted(ST, key=ST.get, reverse = True):
  rank.append(node)
print(rank)