mirror of
https://github.com/gsi-upm/soil
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493 lines
19 KiB
Python
493 lines
19 KiB
Python
from nxsim import NetworkSimulation
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from nxsim import BaseNetworkAgent
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from nxsim import BaseLoggingAgent
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from random import randint
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from matplotlib import pyplot as plt
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import random
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import numpy as np
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import networkx as nx
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import settings
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import math
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import json
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settings.init() # Loads all the data from settings
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####################
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# Network creation #
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####################
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if settings.network_type == 0:
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G = nx.complete_graph(settings.number_of_nodes)
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if settings.network_type == 1:
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G = nx.barabasi_albert_graph(settings.number_of_nodes,3)
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if settings.network_type == 2:
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G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
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# More types of networks can be added here
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##############################
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# Variables initializitation #
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##############################
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myList=[] # List just for debugging
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networkStatus=[] # This list will contain the status of every node of the network
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emotionStatus=[]
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enterprise1Status=[]
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enterprise2Status=[]
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allEnterprisesEmotionList = {}
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for x in range(0, settings.number_of_nodes):
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networkStatus.append({'id':x})
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emotionStatus.append({'id':x})
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enterprise1Status.append({'id':x})
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enterprise2Status.append({'id':x})
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for enterpriseIndex in range(0,len(settings.tweet_probability_about)):
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allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)] = enterprise1Status
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# for node in range(0, settings.number_of_nodes):
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# allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)].update({'id':node})
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#print(allEnterprisesEmotionList)
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# Initialize agent states. Let's assume everyone is normal.
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init_states = [{'id': 0, } for _ in range(settings.number_of_nodes)] # add keys as as necessary, but "id" must always refer to that state category
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# Seed a zombie, just for zombie model
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#init_states[5] = {'id': 1}
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#init_states[3] = {'id': 1}
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####################
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# Available models #
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####################
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class BigMarketModel(BaseNetworkAgent):
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number_of_enterprises = 0
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.time_awareness = 0
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self.type = ""
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self.number_of_enterprises = len(settings.tweet_probability_about)
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if self.id < self.number_of_enterprises: #Empresas
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self.state['id']=self.id
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self.type="Enterprise"
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self.tweet_probability = settings.tweet_probability_enterprises[self.id]
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else: #Usuarios normales
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self.state['id']=self.number_of_enterprises
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self.type="User"
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self.tweet_probability = settings.tweet_probability_users
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self.tweet_relevant_probability = settings.tweet_relevant_probability
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self.tweet_probability_about = settings.tweet_probability_about #Lista
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self.sentiment_about = settings.sentiment_about #Lista
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#Inicializacion de visualizacion
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for enterpriseIndex in range(0,len(settings.tweet_probability_about)):
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allEnterprisesEmotionList['enterprise'+str(enterpriseIndex)][self.id].update({0:self.sentiment_about[enterpriseIndex]})
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#print(allEnterprisesEmotionList)
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#networkStatus[self.id][self.env.now]=self.state['id']
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#emotionStatus[self.id][self.env.now]=0
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def run(self):
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while True:
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if(self.id < self.number_of_enterprises): # Empresa
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self.enterpriseBehaviour()
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else: # Usuario
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self.userBehaviour()
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yield self.env.timeout(settings.timeout)
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def enterpriseBehaviour(self):
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if random.random()< self.tweet_probability: #Twittea
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aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodos vecinos usuarios
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for x in aware_neighbors:
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if random.uniform(0,10) < 5:
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x.sentiment_about[self.id] += 0.1 #Aumenta para empresa
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else:
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x.sentiment_about[self.id] -= 0.1 #Reduce para empresa
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# Establecemos limites
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if x.sentiment_about[self.id] > 1:
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x.sentiment_about[self.id] = 1
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if x.sentiment_about[self.id] < -1:
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x.sentiment_about[self.id] = -1
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#Visualización
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enterpriseEmotion=[]
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if self.id < self.number_of_enterprises:
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#try:
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#enterpriseEmotion = allEnterprisesEmotionList[self.id] #Cogemos la lista si ya ha sido creada
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#print (enterpriseEmotion)
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#except IndexError: # Si no existe la inicializamos
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# for y in range(0, settings.number_of_nodes):
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# enterpriseEmotion.append({'id':y})
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#enterpriseEmotion[x.id][self.env.now]=x.sentiment_about[self.id]
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#allEnterprisesEmotionList.insert(self.id,enterpriseEmotion) #Guardamos el valor
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#enterpriseEmotion[:] = [] #Vaciamos la lista
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allEnterprisesEmotionList['enterprise'+str(self.id)][x.id].update({self.env.now:x.sentiment_about[self.id]})
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#if self.id == 0:
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# enterprise1Status[x.id][self.env.now]=x.sentiment_about[self.id]
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#if self.id == 1:
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# enterprise2Status[x.id][self.env.now]=x.sentiment_about[self.id]
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def userBehaviour(self):
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if random.random() < self.tweet_probability: #Twittea
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if random.random() < self.tweet_relevant_probability: #Twittea algo relevante
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#Probabilidad de tweet para cada empresa
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for i in range(len(self.tweet_probability_about)):
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random_num = random.random()
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if random_num < self.tweet_probability_about[i]:
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#Se ha cumplido la condicion, evaluo los sentimientos hacia esa empresa
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if self.sentiment_about[i] < 0:
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#NEGATIVO
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self.userTweets("negative",i)
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elif self.sentiment_about[i] == 0:
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#NEUTRO
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pass
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else:
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#POSITIVO
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self.userTweets("positive",i)
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def userTweets(self,sentiment,enterprise):
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aware_neighbors = self.get_neighboring_agents(state_id=2) #Nodos vecinos usuarios
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for x in aware_neighbors:
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if sentiment == "positive":
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x.sentiment_about[enterprise] +=0.003
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elif sentiment == "negative":
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x.sentiment_about[enterprise] -=0.003
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else:
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pass
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# Establecemos limites
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if x.sentiment_about[enterprise] > 1:
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x.sentiment_about[enterprise] = 1
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if x.sentiment_about[enterprise] < -1:
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x.sentiment_about[enterprise] = -1
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#Visualización
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# enterpriseEmotion=[]
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# try:
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# enterpriseEmotion = allEnterprisesEmotionList[self.id] #Cogemos la lista si ya ha sido creada
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# except IndexError: # Si no existe la inicializamos
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# for y in range(0, settings.number_of_nodes):
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# enterpriseEmotion.append({'id':y})
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# enterpriseEmotion[x.id][self.env.now]=x.sentiment_about[enterprise]
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# allEnterprisesEmotionList.insert(enterprise,enterpriseEmotion) #Guardamos el valor
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# enterpriseEmotion[:] = [] #Vaciamos la lista
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#if enterprise == 0:
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# enterprise1Status[x.id][self.env.now]=x.sentiment_about[enterprise]
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#if enterprise == 1:
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# enterprise2Status[x.id][self.env.now]=x.sentiment_about[enterprise]
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allEnterprisesEmotionList['enterprise'+str(enterprise)][x.id].update({self.env.now:x.sentiment_about[enterprise]})
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def checkLimits(sentimentValue):
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if sentimentValue > 1:
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return 1
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if sentimentValue < -1:
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return -1
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################################################
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class SentimentCorrelationModel(BaseNetworkAgent):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.outside_effects_prob = settings.outside_effects_prob
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self.anger_prob = settings.anger_prob
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self.joy_prob = settings.joy_prob
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self.sadness_prob = settings.sadness_prob
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self.disgust_prob = settings.disgust_prob
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self.time_awareness=[]
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for i in range(4): #En este modelo tenemos 4 sentimientos
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self.time_awareness.append(0) #0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
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networkStatus[self.id][self.env.now]=0
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def run(self):
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while True:
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angry_neighbors_1_time_step=[]
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joyful_neighbors_1_time_step=[]
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sad_neighbors_1_time_step=[]
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disgusted_neighbors_1_time_step=[]
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angry_neighbors = self.get_neighboring_agents(state_id=1)
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for x in angry_neighbors:
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if x.time_awareness[0] > (self.env.now-500):
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angry_neighbors_1_time_step.append(x)
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num_neighbors_angry = len(angry_neighbors_1_time_step)
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joyful_neighbors = self.get_neighboring_agents(state_id=2)
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for x in joyful_neighbors:
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if x.time_awareness[1] > (self.env.now-500):
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joyful_neighbors_1_time_step.append(x)
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num_neighbors_joyful = len(joyful_neighbors_1_time_step)
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sad_neighbors = self.get_neighboring_agents(state_id=3)
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for x in sad_neighbors:
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if x.time_awareness[2] > (self.env.now-500):
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sad_neighbors_1_time_step.append(x)
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num_neighbors_sad = len(sad_neighbors_1_time_step)
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disgusted_neighbors = self.get_neighboring_agents(state_id=4)
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for x in disgusted_neighbors:
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if x.time_awareness[3] > (self.env.now-500):
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disgusted_neighbors_1_time_step.append(x)
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num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
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anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
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joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
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sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
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disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
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outside_effects_prob= settings.outside_effects_prob
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num = random.random()
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if(num<outside_effects_prob):
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self.state['id'] = random.randint(1,4)
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myList.append(self.id)
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networkStatus[self.id][self.env.now]=self.state['id'] #Almaceno cuando se ha infectado para la red dinamica
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self.time_awareness[self.state['id']-1] = self.env.now
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yield self.env.timeout(settings.timeout)
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if(num<anger_prob):
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myList.append(self.id)
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self.state['id'] = 1
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networkStatus[self.id][self.env.now]=1
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self.time_awareness[self.state['id']-1] = self.env.now
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elif (num<joy_prob+anger_prob and num>anger_prob):
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myList.append(self.id)
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self.state['id'] = 2
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networkStatus[self.id][self.env.now]=2
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self.time_awareness[self.state['id']-1] = self.env.now
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elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
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myList.append(self.id)
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self.state['id'] = 3
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networkStatus[self.id][self.env.now]=3
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self.time_awareness[self.state['id']-1] = self.env.now
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elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
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myList.append(self.id)
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self.state['id'] = 4
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networkStatus[self.id][self.env.now]=4
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self.time_awareness[self.state['id']-1] = self.env.now
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yield self.env.timeout(settings.timeout)
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class BassModel(BaseNetworkAgent):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.innovation_prob = settings.innovation_prob
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self.imitation_prob = settings.imitation_prob
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networkStatus[self.id][self.env.now]=0
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def run(self):
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while True:
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#Outside effects
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if random.random() < settings.innovation_prob:
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if self.state['id'] == 0:
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self.state['id'] = 1
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myList.append(self.id)
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networkStatus[self.id][self.env.now]=1
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yield self.env.timeout(settings.timeout)
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else:
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yield self.env.timeout(settings.timeout)
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#Imitation effects
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if self.state['id'] == 0:
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aware_neighbors = self.get_neighboring_agents(state_id=1)
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num_neighbors_aware = len(aware_neighbors)
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if random.random() < (settings.imitation_prob*num_neighbors_aware):
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myList.append(self.id)
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self.state['id'] = 1
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networkStatus[self.id][self.env.now]=1
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yield self.env.timeout(settings.timeout)
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else:
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yield self.env.timeout(settings.timeout)
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class IndependentCascadeModel(BaseNetworkAgent):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.innovation_prob = settings.innovation_prob
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self.imitation_prob = settings.imitation_prob
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self.time_awareness = 0
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networkStatus[self.id][self.env.now]=0
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def run(self):
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while True:
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aware_neighbors_1_time_step=[]
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#Outside effects
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if random.random() < settings.innovation_prob:
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if self.state['id'] == 0:
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self.state['id'] = 1
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myList.append(self.id)
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networkStatus[self.id][self.env.now]=1
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self.time_awareness = self.env.now #Para saber cuando se han contagiado
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yield self.env.timeout(settings.timeout)
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else:
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yield self.env.timeout(settings.timeout)
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#Imitation effects
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if self.state['id'] == 0:
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aware_neighbors = self.get_neighboring_agents(state_id=1)
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for x in aware_neighbors:
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if x.time_awareness == (self.env.now-1):
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aware_neighbors_1_time_step.append(x)
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num_neighbors_aware = len(aware_neighbors_1_time_step)
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if random.random() < (settings.imitation_prob*num_neighbors_aware):
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myList.append(self.id)
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self.state['id'] = 1
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networkStatus[self.id][self.env.now]=1
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yield self.env.timeout(settings.timeout)
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else:
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yield self.env.timeout(settings.timeout)
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class ZombieOutbreak(BaseNetworkAgent):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.bite_prob = settings.bite_prob
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networkStatus[self.id][self.env.now]=0
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def run(self):
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while True:
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if random.random() < settings.heal_prob:
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if self.state['id'] == 1:
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self.zombify()
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yield self.env.timeout(settings.timeout)
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else:
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yield self.env.timeout(settings.timeout)
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else:
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if self.state['id'] == 1:
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print("Soy el zombie " + str(self.id) + " y me voy a curar porque el num aleatorio ha sido " + str(num))
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networkStatus[self.id][self.env.now]=0
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if self.id in myList:
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myList.remove(self.id)
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self.state['id'] = 0
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yield self.env.timeout(settings.timeout)
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else:
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yield self.env.timeout(settings.timeout)
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def zombify(self):
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normal_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in normal_neighbors:
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if random.random() < self.bite_prob:
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print("Soy el zombie " + str(self.id) + " y voy a contagiar a " + str(neighbor.id))
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neighbor.state['id'] = 1 # zombie
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myList.append(neighbor.id)
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networkStatus[self.id][self.env.now]=1
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networkStatus[neighbor.id][self.env.now]=1
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print(self.env.now, "Soy el zombie: "+ str(self.id), "Mi vecino es: "+ str(neighbor.id), sep='\t')
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break
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##############
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# Simulation #
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##############
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=BigMarketModel,
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max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
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sim.run_simulation()
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###########
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# Results #
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###########
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myList = sorted(myList, key=int)
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#print("Los zombies son: " + str(myList))
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trial = BaseLoggingAgent.open_trial_state_history(dir_path='sim_01', trial_id=0)
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status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) for t,g in trial.items()]
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#################
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# Visualization #
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#################
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# print("Empresa1")
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# print (enterprise1Status)
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# print("Empresa2")
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# print (enterprise2Status)
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for y in allEnterprisesEmotionList:
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for x in range(0, settings.number_of_nodes):
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emotionStatusAux=[]
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enterpriseStatus = allEnterprisesEmotionList[y]
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for tiempo in enterpriseStatus[x]:
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if tiempo != 'id':
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prec = 2
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output = math.floor(enterpriseStatus[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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emotionStatusAux.append((output,tiempo,None))
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keyword = 'enterprise'+str(y)+'Emotion'
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G.add_node(x, keyword = emotionStatusAux)
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# for x in range(0, settings.number_of_nodes):
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# emotionStatusAux2=[]
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# for tiempo in enterprise2Status[x]:
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# if tiempo != 'id':
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# prec = 2
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# output = math.floor(enterprise2Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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# emotionStatusAux2.append((output,tiempo,None))
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# G.add_node(x, enterprise2emotion= emotionStatusAux2)
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|
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print("Done!")
|
|
|
|
#lista = nx.nodes(G)
|
|
#print('Nodos: ' + str(lista))
|
|
# for x in range(0, settings.number_of_nodes):
|
|
# networkStatusAux=[]
|
|
# for tiempo in networkStatus[x]:
|
|
# if tiempo != 'id':
|
|
# networkStatusAux.append((networkStatus[x][tiempo],tiempo,None))
|
|
# G.add_node(x, status= networkStatusAux)
|
|
#print(networkStatus)
|
|
|
|
print(allEnterprisesEmotionList)
|
|
with open('data.txt', 'w') as outfile:
|
|
json.dump(allEnterprisesEmotionList, outfile)
|
|
|
|
nx.write_gexf(G,"test.gexf", version="1.2draft")
|
|
plt.plot(status_census)
|
|
plt.draw() # pyplot draw()
|
|
plt.savefig("status.png")
|
|
#print(networkStatus)
|
|
#nx.draw(G)
|
|
#plt.show()
|
|
#plt.savefig("path.png")
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|
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