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https://github.com/gsi-upm/soil
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Visualizacion con dos parametros, sentimientos negativos y positivos.
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@ -21,7 +21,7 @@ def init():
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global tweet_probability_enterprises
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global tweet_probability_enterprises
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network_type=1
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network_type=1
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number_of_nodes=200
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number_of_nodes=50
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max_time=1000
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max_time=1000
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num_trials=1
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num_trials=1
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timeout=10
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timeout=10
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@ -45,8 +45,8 @@ def init():
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##Users
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##Users
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tweet_probability_users = 0.44
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tweet_probability_users = 0.44
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tweet_relevant_probability = 0.25
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tweet_relevant_probability = 0.25
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tweet_probability_about = [0, 0]
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tweet_probability_about = [0.25, 0.25]
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sentiment_about = [0, 0] #Valores por defecto
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sentiment_about = [0, 0] #Valores por defecto
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##Enterprises
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##Enterprises
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tweet_probability_enterprises = [0.5, 0.5]
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tweet_probability_enterprises = [0.3, 0.3]
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80
soil.py
80
soil.py
@ -31,9 +31,14 @@ if settings.network_type == 2:
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myList=[] # List just for debugging
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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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networkStatus=[] # This list will contain the status of every node of the network
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emotionStatus=[]
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emotionStatus=[]
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enterprise1Status=[]
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enterprise2Status=[]
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for x in range(0, settings.number_of_nodes):
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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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networkStatus.append({'id':x})
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emotionStatus.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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# Initialize agent states. Let's assume everyone is normal.
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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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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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@ -69,15 +74,16 @@ class BigMarketModel(BaseNetworkAgent):
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self.tweet_probability_about = settings.tweet_probability_about #Lista
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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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self.sentiment_about = settings.sentiment_about #Lista
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networkStatus[self.id][self.env.now]=self.state['id']
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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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#emotionStatus[self.id][self.env.now]=0
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def run(self):
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def run(self):
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while True:
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while True:
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if(self.id < 2): # Empresa
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if(self.id < 2): # Empresa
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self.enterpriseBehaviour()
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self.enterpriseBehaviour()
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else: # Usuario
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else: # Usuario
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self.userBehaviour()
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#self.userBehaviour()
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pass
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yield self.env.timeout(settings.timeout)
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yield self.env.timeout(settings.timeout)
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@ -88,16 +94,21 @@ class BigMarketModel(BaseNetworkAgent):
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aware_neighbors = self.get_neighboring_agents(state_id=2) #Nodos vecinos usuarios
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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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for x in aware_neighbors:
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if random.uniform(0,10) < 5:
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if random.uniform(0,10) < 5:
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x.sentiment_about[self.id] += 0.01 #Aumenta para empresa
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x.sentiment_about[self.id] += 0.1 #Aumenta para empresa
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else:
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else:
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x.sentiment_about[self.id] -= 0.01 #Reduce para empresa
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x.sentiment_about[self.id] -= 0.1 #Reduce para empresa
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# Establecemos limites
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# Establecemos limites
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if 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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x.sentiment_about[self.id] = 1
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if 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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x.sentiment_about[self.id] = -1
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#Guardamos estado para visualizacion
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emotionStatus[x.id][self.env.now]=x.sentiment_about[self.id]
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#Visualización
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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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@ -106,7 +117,6 @@ class BigMarketModel(BaseNetworkAgent):
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if random.random() < self.tweet_probability: #Twittea
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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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if random.random() < self.tweet_relevant_probability: #Twittea algo relevante
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#Probabilidad de tweet para cada empresa
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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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for i in range(len(self.tweet_probability_about)):
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random_num = random.random()
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random_num = random.random()
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@ -122,16 +132,37 @@ class BigMarketModel(BaseNetworkAgent):
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#POSITIVO
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#POSITIVO
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self.userTweets("positive",i)
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self.userTweets("positive",i)
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def userTweets(self,sentiment,enterprise):
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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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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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for x in aware_neighbors:
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if sentiment == "positive":
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if sentiment == "positive":
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x.sentiment_about[enterprise] +=0
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x.sentiment_about[enterprise] +=0.003
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elif sentiment == "negative":
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elif sentiment == "negative":
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x.sentiment_about[enterprise] -=0
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x.sentiment_about[enterprise] -=0.003
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else:
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else:
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pass
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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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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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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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@ -372,14 +403,37 @@ status_census = [sum([1 for node_id, state in g.items() if state['id'] == 1]) fo
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# Visualization #
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# Visualization #
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#################
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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 x in range(0, settings.number_of_nodes):
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for x in range(0, settings.number_of_nodes):
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emotionStatusAux=[]
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emotionStatusAux=[]
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for tiempo in emotionStatus[x]:
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# for tiempo in emotionStatus[x]:
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# if tiempo != 'id':
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# prec = 2
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# output = math.floor(emotionStatus[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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# emotionStatusAux.append((output,tiempo,None))
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# G.add_node(x, emotion= emotionStatusAux)
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# del emotionStatusAux[:]
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for tiempo in enterprise1Status[x]:
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if tiempo != 'id':
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if tiempo != 'id':
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prec = 2
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prec = 2
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output = math.floor(emotionStatus[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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output = math.floor(enterprise1Status[x][tiempo] * (10 ** prec)) / (10 ** prec) #Para tener 2 decimales solo
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emotionStatusAux.append((output,tiempo,None))
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emotionStatusAux.append((output,tiempo,None))
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G.add_node(x, emotion= emotionStatusAux)
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G.add_node(x, enterprise1emotion= 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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#lista = nx.nodes(G)
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#lista = nx.nodes(G)
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#print('Nodos: ' + str(lista))
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#print('Nodos: ' + str(lista))
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status.png
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status.png
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