mirror of
https://github.com/gsi-upm/soil
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598 lines
23 KiB
Python
598 lines
23 KiB
Python
from nxsim import BaseNetworkAgent
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import numpy as np
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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 initialization #
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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 BaseBehaviour(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['agent_%s'% self.id] = self.to_json()
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def to_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 ControlModelM2(BaseBehaviour):
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#Init infected
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init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
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init_states[random.randint(0,settings.number_of_nodes-1)] = {'id':1}
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# Init beacons
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
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init_states[random.randint(0, settings.number_of_nodes-1)] = {'id': 4}
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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.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
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self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
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self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
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self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
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self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
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self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
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def step(self, now):
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if self.state['id'] == 0: #Neutral
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self.neutral_behaviour()
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elif self.state['id'] == 1: #Infected
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self.infected_behaviour()
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elif self.state['id'] == 2: #Cured
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self.cured_behaviour()
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elif self.state['id'] == 3: #Vaccinated
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self.vaccinated_behaviour()
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elif self.state['id'] == 4: #Beacon-off
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self.beacon_off_behaviour()
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elif self.state['id'] == 5: #Beacon-on
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self.beacon_on_behaviour()
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self.attrs['status'] = self.state['id']
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super().step(now)
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def neutral_behaviour(self):
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# Infected
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors)>0:
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if random.random() < self.prob_neutral_making_denier:
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self.state['id'] = 3 # Vaccinated making denier
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def infected_behaviour(self):
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# Neutral
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_infect:
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neighbor.state['id'] = 1 # Infected
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def cured_behaviour(self):
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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def vaccinated_behaviour(self):
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Generate anti-rumor
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infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_2:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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def beacon_off_behaviour(self):
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors) > 0:
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self.state['id'] == 5 #Beacon on
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def beacon_on_behaviour(self):
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# Cure (M2 feature added)
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors_infected:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 3 # Vaccinated
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infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_infected:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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class SpreadModelM2(BaseBehaviour):
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init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
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init_states[random.randint(0,settings.number_of_nodes)] = {'id':1}
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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.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance)
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self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance)
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self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance)
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self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance)
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self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance)
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self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance)
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self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance)
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def step(self, now):
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if self.state['id'] == 0: #Neutral
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self.neutral_behaviour()
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elif self.state['id'] == 1: #Infected
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self.infected_behaviour()
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elif self.state['id'] == 2: #Cured
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self.cured_behaviour()
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elif self.state['id'] == 3: #Vaccinated
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self.vaccinated_behaviour()
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self.attrs['status'] = self.state['id']
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super().step(now)
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def neutral_behaviour(self):
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# Infected
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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if len(infected_neighbors)>0:
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if random.random() < self.prob_neutral_making_denier:
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self.state['id'] = 3 # Vaccinated making denier
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def infected_behaviour(self):
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# Neutral
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_infect:
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neighbor.state['id'] = 1 # Infected
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def cured_behaviour(self):
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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def vaccinated_behaviour(self):
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# Cure
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infected_neighbors = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors:
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if random.random() < self.prob_cured_healing_infected:
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neighbor.state['id'] = 2 # Cured
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# Vaccinate
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neutral_neighbors = self.get_neighboring_agents(state_id=0)
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for neighbor in neutral_neighbors:
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if random.random() < self.prob_cured_vaccinate_neutral:
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neighbor.state['id'] = 3 # Vaccinated
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# Generate anti-rumor
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infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
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for neighbor in infected_neighbors_2:
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if random.random() < self.prob_generate_anti_rumor:
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neighbor.state['id'] = 2 # Cured
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class SISaModel(BaseBehaviour):
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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.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob, settings.standard_variance)
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self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob,settings.standard_variance)
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self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob,settings.standard_variance)
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self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob,settings.standard_variance)
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self.discontent_neutral = np.random.normal(settings.discontent_neutral,settings.standard_variance)
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self.discontent_content = np.random.normal(settings.discontent_content,settings.variance_d_c)
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self.content_discontent = np.random.normal(settings.content_discontent,settings.variance_c_d)
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self.content_neutral = np.random.normal(settings.content_neutral,settings.standard_variance)
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def step(self, now):
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if self.state['id'] == 0:
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self.neutral_behaviour()
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if self.state['id'] == 1:
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self.discontent_behaviour()
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if self.state['id'] == 2:
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self.content_behaviour()
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self.attrs['status'] = self.state['id']
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super().step(now)
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def neutral_behaviour(self):
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#Spontaneus effects
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if random.random() < self.neutral_discontent_spon_prob:
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self.state['id'] = 1
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if random.random() < self.neutral_content_spon_prob:
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self.state['id'] = 2
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#Infected
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discontent_neighbors = self.get_neighboring_agents(state_id=1)
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if random.random() < len(discontent_neighbors)*self.neutral_discontent_infected_prob:
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self.state['id'] = 1
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content_neighbors = self.get_neighboring_agents(state_id=2)
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if random.random() < len(content_neighbors)*self.neutral_content_infected_prob:
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self.state['id'] = 2
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def discontent_behaviour(self):
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#Healing
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if random.random() < self.discontent_neutral:
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self.state['id'] = 0
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#Superinfected
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content_neighbors = self.get_neighboring_agents(state_id=2)
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if random.random() < len(content_neighbors)*self.discontent_content:
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self.state['id'] = 2
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def content_behaviour(self):
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#Healing
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if random.random() < self.content_neutral:
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self.state['id'] = 0
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#Superinfected
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discontent_neighbors = self.get_neighboring_agents(state_id=1)
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if random.random() < len(discontent_neighbors)*self.content_discontent:
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self.state['id'] = 1
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class BigMarketModel(BaseBehaviour):
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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 #List
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self.sentiment_about = settings.sentiment_about #List
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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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for i in range(self.number_of_enterprises): # So that it never is set to 0 if there are not changes (logs)
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self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
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super().step(now)
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def enterpriseBehaviour(self):
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if random.random()< self.tweet_probability: #Tweets
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aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbour users
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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) #Nodes neighbours users
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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(BaseBehaviour):
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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): #In this model we have 4 sentiments
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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)
|
|
|
|
|
|
joyful_neighbors = self.get_neighboring_agents(state_id=2)
|
|
for x in joyful_neighbors:
|
|
if x.time_awareness[1] > (self.env.now-500):
|
|
joyful_neighbors_1_time_step.append(x)
|
|
num_neighbors_joyful = len(joyful_neighbors_1_time_step)
|
|
|
|
|
|
sad_neighbors = self.get_neighboring_agents(state_id=3)
|
|
for x in sad_neighbors:
|
|
if x.time_awareness[2] > (self.env.now-500):
|
|
sad_neighbors_1_time_step.append(x)
|
|
num_neighbors_sad = len(sad_neighbors_1_time_step)
|
|
|
|
|
|
disgusted_neighbors = self.get_neighboring_agents(state_id=4)
|
|
for x in disgusted_neighbors:
|
|
if x.time_awareness[3] > (self.env.now-500):
|
|
disgusted_neighbors_1_time_step.append(x)
|
|
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
|
|
|
|
|
|
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob)
|
|
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob)
|
|
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob)
|
|
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob)
|
|
outside_effects_prob= settings.outside_effects_prob
|
|
|
|
|
|
num = random.random()
|
|
|
|
|
|
if(num<outside_effects_prob):
|
|
self.state['id'] = random.randint(1,4)
|
|
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] #It is stored when it has been infected for the dynamic network
|
|
self.time_awareness[self.state['id']-1] = self.env.now
|
|
self.attrs['sentiment'] = self.state['id']
|
|
|
|
|
|
|
|
if(num<anger_prob):
|
|
|
|
self.state['id'] = 1
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
|
self.time_awareness[self.state['id']-1] = self.env.now
|
|
elif (num<joy_prob+anger_prob and num>anger_prob):
|
|
|
|
self.state['id'] = 2
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=2
|
|
self.time_awareness[self.state['id']-1] = self.env.now
|
|
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
|
|
|
|
|
|
self.state['id'] = 3
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=3
|
|
self.time_awareness[self.state['id']-1] = self.env.now
|
|
elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
|
|
|
|
|
|
self.state['id'] = 4
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=4
|
|
self.time_awareness[self.state['id']-1] = self.env.now
|
|
|
|
self.attrs['sentiment'] = self.state['id']
|
|
|
|
|
|
class BassModel(BaseBehaviour):
|
|
def __init__(self, environment=None, agent_id=0, state=()):
|
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
self.innovation_prob = settings.innovation_prob
|
|
self.imitation_prob = settings.imitation_prob
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=0
|
|
|
|
def step(self, now):
|
|
self.behaviour()
|
|
super().step(now)
|
|
|
|
def behaviour(self):
|
|
#Outside effects
|
|
if random.random() < settings.innovation_prob:
|
|
if self.state['id'] == 0:
|
|
self.state['id'] = 1
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
|
else:
|
|
pass
|
|
|
|
self.attrs['status'] = self.state['id']
|
|
return
|
|
|
|
#Imitation effects
|
|
if self.state['id'] == 0:
|
|
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
|
num_neighbors_aware = len(aware_neighbors)
|
|
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
|
self.state['id'] = 1
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
|
|
|
else:
|
|
pass
|
|
self.attrs['status'] = self.state['id']
|
|
|
|
|
|
class IndependentCascadeModel(BaseBehaviour):
|
|
def __init__(self, environment=None, agent_id=0, state=()):
|
|
super().__init__(environment=environment, agent_id=agent_id, state=state)
|
|
self.innovation_prob = settings.innovation_prob
|
|
self.imitation_prob = settings.imitation_prob
|
|
self.time_awareness = 0
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=0
|
|
|
|
def step(self,now):
|
|
self.behaviour()
|
|
super().step(now)
|
|
|
|
def behaviour(self):
|
|
aware_neighbors_1_time_step=[]
|
|
#Outside effects
|
|
if random.random() < settings.innovation_prob:
|
|
if self.state['id'] == 0:
|
|
self.state['id'] = 1
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
|
self.time_awareness = self.env.now #Para saber cuando se han contagiado
|
|
|
|
else:
|
|
pass
|
|
|
|
self.attrs['status'] = self.state['id']
|
|
return
|
|
|
|
#Imitation effects
|
|
if self.state['id'] == 0:
|
|
aware_neighbors = self.get_neighboring_agents(state_id=1)
|
|
for x in aware_neighbors:
|
|
if x.time_awareness == (self.env.now-1):
|
|
aware_neighbors_1_time_step.append(x)
|
|
num_neighbors_aware = len(aware_neighbors_1_time_step)
|
|
if random.random() < (settings.imitation_prob*num_neighbors_aware):
|
|
self.state['id'] = 1
|
|
sentimentCorrelationNodeArray[self.id][self.env.now]=1
|
|
else:
|
|
pass
|
|
|
|
self.attrs['status'] = self.state['id']
|
|
return
|