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Code Issues Releases Wiki Activity

Settings modules

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This commit is contained in:
Tasio Mendez 2017-04-21 13:55:42 +02:00
parent dd4ce15a3d
commit f29f5fa5bf
11 changed files with 262 additions and 210 deletions
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2
models/BaseBehaviour/BaseBehaviour.py
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@ -2,8 +2,6 @@ import settings
from nxsim import BaseNetworkAgent from nxsim import BaseNetworkAgent
from .. import networkStatus from .. import networkStatus
settings.init()
class BaseBehaviour(BaseNetworkAgent): class BaseBehaviour(BaseNetworkAgent):

11
models/BassModel/BassModel.py
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@ -1,10 +1,7 @@
import settings
import random import random
from ..BaseBehaviour import * from ..BaseBehaviour import *
from .. import sentimentCorrelationNodeArray from .. import sentimentCorrelationNodeArray
settings.init()
class BassModel(BaseBehaviour): class BassModel(BaseBehaviour):
""" """
@ -16,8 +13,8 @@ class BassModel(BaseBehaviour):
def __init__(self, environment=None, agent_id=0, state=()): def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state) super().__init__(environment=environment, agent_id=agent_id, state=state)
self.innovation_prob = settings.innovation_prob self.innovation_prob = environment.innovation_prob
self.imitation_prob = settings.imitation_prob self.imitation_prob = environment.imitation_prob
sentimentCorrelationNodeArray[self.id][self.env.now] = 0 sentimentCorrelationNodeArray[self.id][self.env.now] = 0
def step(self, now): def step(self, now):
@ -26,7 +23,7 @@ class BassModel(BaseBehaviour):
def behaviour(self): def behaviour(self):
# Outside effects # Outside effects
if random.random() < settings.innovation_prob: if random.random() < self.innovation_prob:
if self.state['id'] == 0: if self.state['id'] == 0:
self.state['id'] = 1 self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now] = 1 sentimentCorrelationNodeArray[self.id][self.env.now] = 1
@ -40,7 +37,7 @@ class BassModel(BaseBehaviour):
if self.state['id'] == 0: if self.state['id'] == 0:
aware_neighbors = self.get_neighboring_agents(state_id=1) aware_neighbors = self.get_neighboring_agents(state_id=1)
num_neighbors_aware = len(aware_neighbors) num_neighbors_aware = len(aware_neighbors)
if random.random() < (settings.imitation_prob*num_neighbors_aware): if random.random() < (self.imitation_prob*num_neighbors_aware):
self.state['id'] = 1 self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now] = 1 sentimentCorrelationNodeArray[self.id][self.env.now] = 1

20
models/BigMarketModel/BigMarketModel.py
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@ -1,9 +1,6 @@
import settings
import random import random
from ..BaseBehaviour import * from ..BaseBehaviour import *
settings.init()
class BigMarketModel(BaseBehaviour): class BigMarketModel(BaseBehaviour):
""" """
@ -22,28 +19,27 @@ class BigMarketModel(BaseBehaviour):
sentiment_about [Array] sentiment_about [Array]
""" """
def __init__(self, environment=None, agent_id=0, state=()): def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state) super().__init__(environment=environment, agent_id=agent_id, state=state)
self.enterprises = settings.enterprises self.enterprises = environment.enterprises
self.type = "" self.type = ""
self.number_of_enterprises = len(settings.enterprises) self.number_of_enterprises = len(environment.enterprises)
if self.id < self.number_of_enterprises: # Enterprises if self.id < self.number_of_enterprises: # Enterprises
self.state['id']=self.id self.state['id']=self.id
self.type="Enterprise" self.type="Enterprise"
self.tweet_probability = settings.tweet_probability_enterprises[self.id] self.tweet_probability = environment.tweet_probability_enterprises[self.id]
else: # normal users else: # normal users
self.state['id']=self.number_of_enterprises self.state['id']=self.number_of_enterprises
self.type="User" self.type="User"
self.tweet_probability = settings.tweet_probability_users self.tweet_probability = environment.tweet_probability_users
self.tweet_relevant_probability = settings.tweet_relevant_probability self.tweet_relevant_probability = environment.tweet_relevant_probability
self.tweet_probability_about = settings.tweet_probability_about # List self.tweet_probability_about = environment.tweet_probability_about # List
self.sentiment_about = settings.sentiment_about # List self.sentiment_about = environment.sentiment_about # List
def step(self, now): def step(self, now):
if(self.id < self.number_of_enterprises): # Enterprise if self.id < self.number_of_enterprises: # Enterprise
self.enterpriseBehaviour() self.enterpriseBehaviour()
else: # Usuario else: # Usuario
self.userBehaviour() self.userBehaviour()

26
models/BigMarketModel/SISaModel.py
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@ -1,10 +1,7 @@
import settings
import random import random
import numpy as np import numpy as np
from ..BaseBehaviour import * from ..BaseBehaviour import *
settings.init()
class SISaModel(BaseBehaviour): class SISaModel(BaseBehaviour):
""" """
@ -35,19 +32,20 @@ class SISaModel(BaseBehaviour):
def __init__(self, environment=None, agent_id=0, state=()): def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state) super().__init__(environment=environment, agent_id=agent_id, state=state)
self.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob, self.neutral_discontent_spon_prob = np.random.normal(environment.neutral_discontent_spon_prob,
settings.standard_variance) environment.standard_variance)
self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob, self.neutral_discontent_infected_prob = np.random.normal(environment.neutral_discontent_infected_prob,
settings.standard_variance) environment.standard_variance)
self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob, settings.standard_variance) self.neutral_content_spon_prob = np.random.normal(environment.neutral_content_spon_prob,
self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob, environment.standard_variance)
settings.standard_variance) self.neutral_content_infected_prob = np.random.normal(environment.neutral_content_infected_prob,
environment.standard_variance)
self.discontent_neutral = np.random.normal(settings.discontent_neutral, settings.standard_variance) self.discontent_neutral = np.random.normal(environment.discontent_neutral, environment.standard_variance)
self.discontent_content = np.random.normal(settings.discontent_content, settings.variance_d_c) self.discontent_content = np.random.normal(environment.discontent_content, environment.variance_d_c)
self.content_discontent = np.random.normal(settings.content_discontent, settings.variance_c_d) self.content_discontent = np.random.normal(environment.content_discontent, environment.variance_c_d)
self.content_neutral = np.random.normal(settings.content_neutral, settings.standard_variance) self.content_neutral = np.random.normal(environment.content_neutral, environment.standard_variance)
def step(self, now): def step(self, now):
if self.state['id'] == 0: if self.state['id'] == 0:

11
models/IndependentCascadeModel/IndependentCascadeModel.py
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@ -1,10 +1,7 @@
import settings
import random import random
from ..BaseBehaviour import * from ..BaseBehaviour import *
from .. import sentimentCorrelationNodeArray from .. import sentimentCorrelationNodeArray
settings.init()
class IndependentCascadeModel(BaseBehaviour): class IndependentCascadeModel(BaseBehaviour):
""" """
@ -16,8 +13,8 @@ class IndependentCascadeModel(BaseBehaviour):
def __init__(self, environment=None, agent_id=0, state=()): def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state) super().__init__(environment=environment, agent_id=agent_id, state=state)
self.innovation_prob = settings.innovation_prob self.innovation_prob = environment.innovation_prob
self.imitation_prob = settings.imitation_prob self.imitation_prob = environment.imitation_prob
self.time_awareness = 0 self.time_awareness = 0
sentimentCorrelationNodeArray[self.id][self.env.now] = 0 sentimentCorrelationNodeArray[self.id][self.env.now] = 0
@ -28,7 +25,7 @@ class IndependentCascadeModel(BaseBehaviour):
def behaviour(self): def behaviour(self):
aware_neighbors_1_time_step = [] aware_neighbors_1_time_step = []
# Outside effects # Outside effects
if random.random() < settings.innovation_prob: if random.random() < self.innovation_prob:
if self.state['id'] == 0: if self.state['id'] == 0:
self.state['id'] = 1 self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now] = 1 sentimentCorrelationNodeArray[self.id][self.env.now] = 1
@ -46,7 +43,7 @@ class IndependentCascadeModel(BaseBehaviour):
if x.time_awareness == (self.env.now-1): if x.time_awareness == (self.env.now-1):
aware_neighbors_1_time_step.append(x) aware_neighbors_1_time_step.append(x)
num_neighbors_aware = len(aware_neighbors_1_time_step) num_neighbors_aware = len(aware_neighbors_1_time_step)
if random.random() < (settings.imitation_prob*num_neighbors_aware): if random.random() < (self.imitation_prob*num_neighbors_aware):
self.state['id'] = 1 self.state['id'] = 1
sentimentCorrelationNodeArray[self.id][self.env.now] = 1 sentimentCorrelationNodeArray[self.id][self.env.now] = 1
else: else:

22
models/ModelM2/ControlModelM2.py
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@ -4,8 +4,6 @@ import numpy as np
from ..BaseBehaviour import * from ..BaseBehaviour import *
from .. import init_states from .. import init_states
settings.init()
class ControlModelM2(BaseBehaviour): class ControlModelM2(BaseBehaviour):
""" """
@ -36,16 +34,22 @@ class ControlModelM2(BaseBehaviour):
def __init__(self, environment=None, agent_id=0, state=()): def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state) super().__init__(environment=environment, agent_id=agent_id, state=state)
self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance) self.prob_neutral_making_denier = np.random.normal(environment.prob_neutral_making_denier,
environment.standard_variance)
self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance) self.prob_infect = np.random.normal(environment.prob_infect, environment.standard_variance)
self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance) self.prob_cured_healing_infected = np.random.normal(environment.prob_cured_healing_infected,
self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance) environment.standard_variance)
self.prob_cured_vaccinate_neutral = np.random.normal(environment.prob_cured_vaccinate_neutral,
environment.standard_variance)
self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance) self.prob_vaccinated_healing_infected = np.random.normal(environment.prob_vaccinated_healing_infected,
self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance) environment.standard_variance)
self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance) self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.prob_vaccinated_vaccinate_neutral,
environment.standard_variance)
self.prob_generate_anti_rumor = np.random.normal(environment.prob_generate_anti_rumor,
environment.standard_variance)
def step(self, now): def step(self, now):

22
models/ModelM2/SpreadModelM2.py
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@ -4,8 +4,6 @@ import numpy as np
from ..BaseBehaviour import * from ..BaseBehaviour import *
from .. import init_states from .. import init_states
settings.init()
class SpreadModelM2(BaseBehaviour): class SpreadModelM2(BaseBehaviour):
""" """
@ -31,16 +29,22 @@ class SpreadModelM2(BaseBehaviour):
def __init__(self, environment=None, agent_id=0, state=()): def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state) super().__init__(environment=environment, agent_id=agent_id, state=state)
self.prob_neutral_making_denier = np.random.normal(settings.prob_neutral_making_denier, settings.standard_variance) self.prob_neutral_making_denier = np.random.normal(environment.prob_neutral_making_denier,
environment.standard_variance)
self.prob_infect = np.random.normal(settings.prob_infect, settings.standard_variance) self.prob_infect = np.random.normal(environment.prob_infect, environment.standard_variance)
self.prob_cured_healing_infected = np.random.normal(settings.prob_cured_healing_infected, settings.standard_variance) self.prob_cured_healing_infected = np.random.normal(environment.prob_cured_healing_infected,
self.prob_cured_vaccinate_neutral = np.random.normal(settings.prob_cured_vaccinate_neutral, settings.standard_variance) environment.standard_variance)
self.prob_cured_vaccinate_neutral = np.random.normal(environment.prob_cured_vaccinate_neutral,
environment.standard_variance)
self.prob_vaccinated_healing_infected = np.random.normal(settings.prob_vaccinated_healing_infected, settings.standard_variance) self.prob_vaccinated_healing_infected = np.random.normal(environment.prob_vaccinated_healing_infected,
self.prob_vaccinated_vaccinate_neutral = np.random.normal(settings.prob_vaccinated_vaccinate_neutral, settings.standard_variance) environment.standard_variance)
self.prob_generate_anti_rumor = np.random.normal(settings.prob_generate_anti_rumor, settings.standard_variance) self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.prob_vaccinated_vaccinate_neutral,
environment.standard_variance)
self.prob_generate_anti_rumor = np.random.normal(environment.prob_generate_anti_rumor,
environment.standard_variance)
def step(self, now): def step(self, now):

27
models/SentimentCorrelationModel/SentimentCorrelationModel.py
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@ -1,10 +1,7 @@
import settings
import random import random
from ..BaseBehaviour import * from ..BaseBehaviour import *
from .. import sentimentCorrelationNodeArray from .. import sentimentCorrelationNodeArray
settings.init()
class SentimentCorrelationModel(BaseBehaviour): class SentimentCorrelationModel(BaseBehaviour):
""" """
@ -22,11 +19,11 @@ class SentimentCorrelationModel(BaseBehaviour):
def __init__(self, environment=None, agent_id=0, state=()): def __init__(self, environment=None, agent_id=0, state=()):
super().__init__(environment=environment, agent_id=agent_id, state=state) super().__init__(environment=environment, agent_id=agent_id, state=state)
self.outside_effects_prob = settings.outside_effects_prob self.outside_effects_prob = environment.outside_effects_prob
self.anger_prob = settings.anger_prob self.anger_prob = environment.anger_prob
self.joy_prob = settings.joy_prob self.joy_prob = environment.joy_prob
self.sadness_prob = settings.sadness_prob self.sadness_prob = environment.sadness_prob
self.disgust_prob = settings.disgust_prob self.disgust_prob = environment.disgust_prob
self.time_awareness = [] self.time_awareness = []
for i in range(4): # In this model we have 4 sentiments for i in range(4): # In this model we have 4 sentiments
self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
@ -67,15 +64,15 @@ class SentimentCorrelationModel(BaseBehaviour):
disgusted_neighbors_1_time_step.append(x) disgusted_neighbors_1_time_step.append(x)
num_neighbors_disgusted = len(disgusted_neighbors_1_time_step) num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
anger_prob= settings.anger_prob+(len(angry_neighbors_1_time_step)*settings.anger_prob) anger_prob = self.anger_prob+(len(angry_neighbors_1_time_step)*self.anger_prob)
joy_prob= settings.joy_prob+(len(joyful_neighbors_1_time_step)*settings.joy_prob) joy_prob = self.joy_prob+(len(joyful_neighbors_1_time_step)*self.joy_prob)
sadness_prob = settings.sadness_prob+(len(sad_neighbors_1_time_step)*settings.sadness_prob) sadness_prob = self.sadness_prob+(len(sad_neighbors_1_time_step)*self.sadness_prob)
disgust_prob = settings.disgust_prob+(len(disgusted_neighbors_1_time_step)*settings.disgust_prob) disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob)
outside_effects_prob= settings.outside_effects_prob outside_effects_prob = self.outside_effects_prob
num = random.random() num = random.random()
if(num<outside_effects_prob): if num<outside_effects_prob:
self.state['id'] = random.randint(1,4) 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 sentimentCorrelationNodeArray[self.id][self.env.now]=self.state['id'] # It is stored when it has been infected for the dynamic network
@ -95,13 +92,11 @@ class SentimentCorrelationModel(BaseBehaviour):
self.time_awareness[self.state['id']-1] = self.env.now self.time_awareness[self.state['id']-1] = self.env.now
elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob): elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
self.state['id'] = 3 self.state['id'] = 3
sentimentCorrelationNodeArray[self.id][self.env.now]=3 sentimentCorrelationNodeArray[self.id][self.env.now]=3
self.time_awareness[self.state['id']-1] = self.env.now 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): elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
self.state['id'] = 4 self.state['id'] = 4
sentimentCorrelationNodeArray[self.id][self.env.now]=4 sentimentCorrelationNodeArray[self.id][self.env.now]=4
self.time_awareness[self.state['id']-1] = self.env.now self.time_awareness[self.state['id']-1] = self.env.now

45
settings.py
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@ -1,44 +1,6 @@
# settings.py # General configuration
def init():
global number_of_nodes
global max_time
global num_trials
global bite_prob
global network_type
global heal_prob
global innovation_prob
global imitation_prob
global timeout
global outside_effects_prob
global anger_prob
global joy_prob
global sadness_prob
global disgust_prob
global tweet_probability_users
global tweet_relevant_probability
global tweet_probability_about
global sentiment_about
global tweet_probability_enterprises
global enterprises
global neutral_discontent_spon_prob
global neutral_discontent_infected_prob
global neutral_content_spon_prob
global neutral_content_infected_prob
global discontent_content
global discontent_neutral
global content_discontent
global content_neutral
global variance_d_c
global variance_c_d
global standard_variance
global prob_neutral_making_denier
global prob_infect
global prob_cured_healing_infected
global prob_cured_vaccinate_neutral
global prob_vaccinated_healing_infected
global prob_vaccinated_vaccinate_neutral
global prob_generate_anti_rumor
# Network settings
network_type = 1 network_type = 1
number_of_nodes = 1000 number_of_nodes = 1000
max_time = 50 max_time = 50
@ -98,6 +60,3 @@ def init():
prob_vaccinated_healing_infected = 0.035 prob_vaccinated_healing_infected = 0.035
prob_vaccinated_vaccinate_neutral = 0.035 prob_vaccinated_vaccinate_neutral = 0.035
prob_generate_anti_rumor = 0.035 prob_generate_anti_rumor = 0.035

104
settings_org.py Normal file
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@ -0,0 +1,104 @@
# settings.py
def init():
global number_of_nodes
global max_time
global num_trials
global bite_prob
global timeout
global network_type
global heal_prob
global innovation_prob
global imitation_prob
global outside_effects_prob
global anger_prob
global joy_prob
global sadness_prob
global disgust_prob
global tweet_probability_users
global tweet_relevant_probability
global tweet_probability_about
global sentiment_about
global tweet_probability_enterprises
global enterprises
global neutral_discontent_spon_prob
global neutral_discontent_infected_prob
global neutral_content_spon_prob
global neutral_content_infected_prob
global discontent_content
global discontent_neutral
global content_discontent
global content_neutral
global variance_d_c
global variance_c_d
global standard_variance
global prob_neutral_making_denier
global prob_infect
global prob_cured_healing_infected
global prob_cured_vaccinate_neutral
global prob_vaccinated_healing_infected
global prob_vaccinated_vaccinate_neutral
global prob_generate_anti_rumor
# Network settings
network_type = 1
number_of_nodes = 1000
max_time = 50
num_trials = 1
timeout = 2
# Zombie model
bite_prob = 0.01 # 0-1
heal_prob = 0.01 # 0-1
# Bass model
innovation_prob = 0.001
imitation_prob = 0.005
# Sentiment Correlation model
outside_effects_prob = 0.2
anger_prob = 0.06
joy_prob = 0.05
sadness_prob = 0.02
disgust_prob = 0.02
# Big Market model
## Names
enterprises = ["BBVA", "Santander", "Bankia"]
## Users
tweet_probability_users = 0.44
tweet_relevant_probability = 0.25
tweet_probability_about = [0.15, 0.15, 0.15]
sentiment_about = [0, 0, 0] # Default values
## Enterprises
tweet_probability_enterprises = [0.3, 0.3, 0.3]
# SISa
neutral_discontent_spon_prob = 0.04
neutral_discontent_infected_prob = 0.04
neutral_content_spon_prob = 0.18
neutral_content_infected_prob = 0.02
discontent_neutral = 0.13
discontent_content = 0.07
variance_d_c = 0.02
content_discontent = 0.009
variance_c_d = 0.003
content_neutral = 0.088
standard_variance = 0.055
# Spread Model M2 and Control Model M2
prob_neutral_making_denier = 0.035
prob_infect = 0.075
prob_cured_healing_infected = 0.035
prob_cured_vaccinate_neutral = 0.035
prob_vaccinated_healing_infected = 0.035
prob_vaccinated_vaccinate_neutral = 0.035
prob_generate_anti_rumor = 0.035

6
soil.py
View File

@ -8,7 +8,6 @@ import models
import math import math
import json import json
settings.init() # Loads all the data from settings
#################### ####################
# Network creation # # Network creation #
@ -22,6 +21,7 @@ if settings.network_type == 2:
G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None) G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
# More types of networks can be added here # More types of networks can be added here
############## ##############
# Simulation # # Simulation #
############## ##############
@ -29,12 +29,13 @@ if settings.network_type == 2:
sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2, sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2,
max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0) max_time=settings.max_time, num_trials=settings.num_trials, logging_interval=1.0)
sim.run_simulation() sim.run_simulation()
########### ###########
# Results # # Results #
########### ###########
x_values = [] x_values = []
infected_values = [] infected_values = []
neutral_values = [] neutral_values = []
@ -86,7 +87,6 @@ plt.savefig('control_model.png')
# Visualization # # Visualization #
################# #################
for x in range(0, settings.number_of_nodes): for x in range(0, settings.number_of_nodes):
for attribute in models.networkStatus["agent_%s"%x]: for attribute in models.networkStatus["agent_%s"%x]:
emotionStatusAux=[] emotionStatusAux=[]