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https://github.com/gsi-upm/soil
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321 lines
12 KiB
Python
321 lines
12 KiB
Python
from nxsim import BaseNetworkAgent
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import random
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import settings
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settings.init()
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##############################
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# Variables initializitation #
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##############################
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def init():
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global networkStatus
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networkStatus = {} # Dict that will contain the status of every agent in the network
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sentimentCorrelationNodeArray=[]
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for x in range(0, settings.number_of_nodes):
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sentimentCorrelationNodeArray.append({'id':x})
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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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####################
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# Available models #
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####################
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class ComportamientoBase(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._attrs = {}
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@property
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def attrs(self):
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now = self.env.now
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if now not in self._attrs:
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self._attrs[now] = {}
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return self._attrs[now]
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@attrs.setter
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def attrs(self, value):
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self._attrs[self.env.now] = value
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def run(self):
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while True:
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self.step(self.env.now)
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yield self.env.timeout(settings.timeout)
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def step(self, now):
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networkStatus['agente_%s'% self.id] = self.a_json()
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def a_json(self):
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final = {}
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for stamp, attrs in self._attrs.items():
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for a in attrs:
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if a not in final:
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final[a] = {}
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final[a][stamp] = attrs[a]
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return final
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class BigMarketModel(ComportamientoBase):
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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.enterprises = settings.enterprises
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self.type = ""
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self.number_of_enterprises = len(settings.enterprises)
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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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def step(self, now):
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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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super().step(now)
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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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x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = 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(self.number_of_enterprises):
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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=self.number_of_enterprises) #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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x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
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class SentimentCorrelationModel(ComportamientoBase):
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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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sentimentCorrelationNodeArray[self.id][self.env.now]=0
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def step(self, now):
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self.behaviour()
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super().step(now)
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def behaviour(self):
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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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sentimentCorrelationNodeArray[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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self.attrs['sentiment'] = self.state['id']
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if(num<anger_prob):
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self.state['id'] = 1
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sentimentCorrelationNodeArray[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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self.state['id'] = 2
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sentimentCorrelationNodeArray[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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self.state['id'] = 3
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sentimentCorrelationNodeArray[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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self.state['id'] = 4
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sentimentCorrelationNodeArray[self.id][self.env.now]=4
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self.time_awareness[self.state['id']-1] = self.env.now
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self.attrs['sentiment'] = self.state['id']
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class BassModel(ComportamientoBase):
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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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sentimentCorrelationNodeArray[self.id][self.env.now]=0
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def step(self, now):
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self.behaviour()
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super().step(now)
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def behaviour(self):
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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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sentimentCorrelationNodeArray[self.id][self.env.now]=1
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else:
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pass
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self.attrs['status'] = self.state['id']
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return
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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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self.state['id'] = 1
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sentimentCorrelationNodeArray[self.id][self.env.now]=1
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else:
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pass
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self.attrs['status'] = self.state['id']
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class IndependentCascadeModel(ComportamientoBase):
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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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sentimentCorrelationNodeArray[self.id][self.env.now]=0
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def step(self,now):
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self.behaviour()
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super().step(now)
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def behaviour(self):
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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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sentimentCorrelationNodeArray[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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else:
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pass
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self.attrs['status'] = self.state['id']
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return
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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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self.state['id'] = 1
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sentimentCorrelationNodeArray[self.id][self.env.now]=1
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else:
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pass
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self.attrs['status'] = self.state['id']
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return
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