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per_agent_simulation_kronecker.py
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per_agent_simulation_kronecker.py
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#Bens Version
import random
import math
import SimpleAgent
import GraphGen as gg
import SimpleSimulationStats as ss
import networkx as nx
from simutil import *
import simplejson as sj
import time
import sys
import KroneckerGenforSimulations as kg
def current_agent_stats (current_agent, stats):
node_stats = {}
node_stats['cc'] = stats['cc'][current_agent]
node_stats['bc'] = stats['bc'][current_agent]
node_stats['deg'] = stats['deg'][current_agent]
#node_stats['dens'] = stats['dens'] #added from stats
return node_stats
def balanced_node_sample(stats): #made by Ben
""" Return 50 nodes balanced from top to bottom (25 from each), or all if less than 50 nodes in decreasing order
of closeness centrality.
"""
balanced_list = []
for key in stats['cc'].keys():
balanced_list.append( (stats['cc'][key], key) )
balanced_list.sort(reverse=True)
nodes = [-1]
num_to_extract_top = min(25, len(balanced_list))
num_to_extract_bottom = min(25, (len(balanced_list) - num_to_extract_top))
for (val, node) in balanced_list[: num_to_extract_top]:
nodes.append(node)
balanced_list.reverse();
for (val, node) in balanced_list[: num_to_extract_bottom]:
nodes.append(node)
'''
#test below
print "Nodes Final"
for i in range(len(nodes)):
print nodes[i]
#test above
'''
return nodes
def top_nodes (stats): #made by Sibel
""" Return top 50, or all if less than 50 nodes in decreasing order
of closeness centrality.
"""
best_list = []
for key in stats['cc'].keys():
best_list.append( (stats['cc'][key], key) )
best_list.sort(reverse=True)
nodes = [-1]
num_to_extract = min(50, len(best_list))
for (val, node) in best_list[: num_to_extract]:
nodes.append(node)
'''
#test below
print "Nodes Final for top"
for i in range(len(nodes)):
print nodes[i]
#test above
'''
return nodes
def add_to_output(all_results, results, outfile):
all_results.append(results)
if len(all_results) > 20:
flush_results(all_results, outfile)
def flush_results(all_results, outfile):
f = open(outfile,"a")
for result in all_results:
f.write( sj.dumps(result) + "\n")
f.close()
all_results = []
########## Run simulation
def one_step_simulation(agents):
num_actions = 0
actions_taken = []
for agent in agents:
actions_taken = agent.act() ##list of (n, fact)
for (n,fact) in actions_taken:
num_actions += 1
n.receive(fact, agent)
return num_actions
def run_simulation_one_graph(properties, outfile):
facts = range(properties['num_facts']+properties['num_noise'])
agents = []
all_results = []
for i in xrange(properties['num_agents']):
agents.append ( SimpleAgent.SimpleAgent(properties['willingness'],\
properties['competence'],\
properties['num_facts'],\
properties['num_noise'],\
properties['spamminess'],\
properties['selfishness'],\
properties['trust_used'],\
properties['inbox_trust_used'],\
properties['trust_filter_on'],
twitter_model = properties['twitter_model']))
## Create agent graph
# conn, stats = gg.create_graph_type(agents, properties) #This is where Kronecker should
conn, stats = kg.create_graph_kronecker(agents, properties)
for agent1 in conn.nodes():
agent1.connect_to(conn.neighbors(agent1))
nodes_to_try = balanced_node_sample(stats) # top_nodes (stats)
#print "Nodes", nodes_to_try
for current_agent in nodes_to_try:
#print "New node", current_agent
if current_agent != -1:
agents[current_agent].capacity = 10 ##set one agent to high capacity
node_stats = current_agent_stats (current_agent, stats)
agent_to_track = current_agent
else:
node_stats = {}
agent_to_track = 0
## Distribute facts to agents
for i in facts:
for j in xrange(properties['agent_per_fact']):
## find a random agent, and distribute fact i
k = random.randint(0,properties['num_agents']-1)
agents[k].knowledge.add(i)
## Initialize agents to send everything that they think is valuable
## in their outbox
for agent in agents:
agent.init_outbox()
#action_list = []
all_stats = ss.SimpleSimulationStats(properties['num_facts'],\
properties['num_noise'],\
stats['num_cc'],\
properties['sa_increment'],\
agent_to_track) #stats['largest_cc'], \
##actual simulation starts here
for i in xrange(properties['num_steps']):
x = one_step_simulation(agents)
#action_list.append(x)
if i%properties['statistic_taking_frequency'] == 0:
all_stats.update_stats(agents,i)
summary_results = all_stats.process_sa()
density = stats['dens']
results = {}
results['setup'] = properties
results['graph_type'] = properties['graph_type']
results['total_filtered'] = summary_results['total_filtered']
results['num_cc'] = summary_results['num_cc']
results['size_lcc'] = summary_results['size_lcc']
results['summary_results'] = summary_results
results['all_sa'] = all_stats.sa
results['all_comm'] = all_stats.comm
results['all_sa0'] = all_stats.sa0
results['all_comm0'] = all_stats.comm0
results['steps'] = all_stats.steps
results['node_stats'] = node_stats
results['dens'] = density
add_to_output(all_results, results, outfile)
for agent in agents:
agent.clear()
agent.capacity = 1
flush_results(all_results, outfile)
def run_simulation(properties, outfile):
for i in xrange( properties['num_trial'] ):
start = time.time()
run_simulation_one_graph(properties, outfile)
end = time.time()
print "Trial: %d: simulation took %d seconds" %(i, end-start)
########## Main body
if __name__ == '__main__':
#Make sure num_agents is equal to the final size of the kronecker (seednodes^interations)
#init_edges is a list of where edges should be placed in seed matrix
properties = {'connection_probability': 0.5, \
'num_nodes_to_attach': 5, \
'graph_type':'kronecker',\
'num_agents': int(math.pow(2, 5)), \
'agent_per_fact':1,\
'num_steps':10000,\
'num_trial':100,\
'statistic_taking_frequency': 1000, \
'num_facts':5000,\
'num_noise':0,\
'sa_increment': 500, \
'trust_used':False,\
'trust_filter_on':False,\
'inbox_trust_used':False,\
'agent_setup':[],\
'competence':1,\
'willingness':1,\
'spamminess':0,\
'selfishness':0,\
'twitter_model':True,\
'init_edges':[[1,0]],\
'alpha': 0.2,\
'beta': 0.09,\
'kron_interations':5,\
'kron_seed_nodes':2,\
'kron_seed_self_loops_stochastic':True} #added some kron specfic properties
if len(sys.argv) > 1:
outfile = sys.argv[1]
else:
outfile = "kronecker_results.txt"
print "Writing results to", outfile
f = open(outfile,"a")
f.write( sj.dumps(properties) + "\n")
f.close()
run_simulation(properties, outfile)