Separate models in modules

models/BigMarketModel/BigMarketModel.py

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+import settings
+import random
+from ..BaseBehaviour import *
+
+settings.init()
+
+
+class BigMarketModel(BaseBehaviour):
+    """
+    Settings:
+        Names:
+            enterprises [Array]
+            
+            tweet_probability_enterprises [Array]
+        Users:
+            tweet_probability_users
+            
+            tweet_relevant_probability
+            
+            tweet_probability_about [Array]
+            
+            sentiment_about [Array]
+    """
+
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.enterprises = settings.enterprises
+        self.type = ""
+        self.number_of_enterprises = len(settings.enterprises)
+
+        if self.id < self.number_of_enterprises: # Enterprises
+            self.state['id']=self.id
+            self.type="Enterprise"
+            self.tweet_probability = settings.tweet_probability_enterprises[self.id]
+        else:                       # normal users
+            self.state['id']=self.number_of_enterprises
+            self.type="User"
+            self.tweet_probability = settings.tweet_probability_users
+            self.tweet_relevant_probability = settings.tweet_relevant_probability
+            self.tweet_probability_about = settings.tweet_probability_about # List
+            self.sentiment_about = settings.sentiment_about # List
+
+    def step(self, now):
+
+        if(self.id < self.number_of_enterprises): # Enterprise
+            self.enterpriseBehaviour()
+        else:  # Usuario
+            self.userBehaviour()
+            for i in range(self.number_of_enterprises):       # So that it never is set to 0 if there are not changes (logs)
+                self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
+
+        super().step(now)
+
+    def enterpriseBehaviour(self):
+
+        if random.random()< self.tweet_probability: # Tweets
+            aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbour users
+            for x in aware_neighbors:
+                if random.uniform(0,10) < 5:
+                    x.sentiment_about[self.id] += 0.1 # Increments for enterprise
+                else:
+                    x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise
+
+                # Establecemos limites
+                if x.sentiment_about[self.id] > 1:
+                    x.sentiment_about[self.id] = 1
+                if x.sentiment_about[self.id]< -1:
+                    x.sentiment_about[self.id] = -1
+
+                x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
+
+    def userBehaviour(self):
+
+        if random.random() < self.tweet_probability: # Tweets
+            if random.random() < self.tweet_relevant_probability: # Tweets something relevant
+                # Tweet probability per enterprise
+                for i in range(self.number_of_enterprises):
+                    random_num = random.random()
+                    if random_num < self.tweet_probability_about[i]:
+                        # The condition is fulfilled, sentiments are evaluated towards that enterprise
+                        if self.sentiment_about[i] < 0:
+                            # NEGATIVO
+                            self.userTweets("negative",i)
+                        elif self.sentiment_about[i] == 0:
+                            # NEUTRO
+                            pass
+                        else:
+                            # POSITIVO
+                            self.userTweets("positive",i)
+
+    def userTweets(self,sentiment,enterprise):
+        aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) #Nodes neighbours users
+        for x in aware_neighbors:
+            if sentiment == "positive":
+                x.sentiment_about[enterprise] +=0.003
+            elif sentiment == "negative":
+                x.sentiment_about[enterprise] -=0.003
+            else:
+                pass
+
+            # Establecemos limites
+            if x.sentiment_about[enterprise] > 1:
+                x.sentiment_about[enterprise] = 1
+            if x.sentiment_about[enterprise] < -1:
+                x.sentiment_about[enterprise] = -1
+
+            x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]

models/BigMarketModel/SISaModel.py

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+import settings
+import random
+import numpy as np
+from ..BaseBehaviour import *
+
+settings.init()
+
+
+class SISaModel(BaseBehaviour):
+    """
+    Settings:
+        neutral_discontent_spon_prob
+        
+        neutral_discontent_infected_prob
+        
+        neutral_content_spong_prob
+        
+        neutral_content_infected_prob
+        
+        discontent_neutral
+        
+        discontent_content
+        
+        variance_d_c
+        
+        content_discontent
+        
+        variance_c_d
+        
+        content_neutral
+        
+        standard_variance
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+
+        self.neutral_discontent_spon_prob = np.random.normal(settings.neutral_discontent_spon_prob,
+                                                             settings.standard_variance)
+        self.neutral_discontent_infected_prob = np.random.normal(settings.neutral_discontent_infected_prob,
+                                                                 settings.standard_variance)
+        self.neutral_content_spon_prob = np.random.normal(settings.neutral_content_spon_prob, settings.standard_variance)
+        self.neutral_content_infected_prob = np.random.normal(settings.neutral_content_infected_prob,
+                                                              settings.standard_variance)
+
+        self.discontent_neutral = np.random.normal(settings.discontent_neutral, settings.standard_variance)
+        self.discontent_content = np.random.normal(settings.discontent_content, settings.variance_d_c)
+
+        self.content_discontent = np.random.normal(settings.content_discontent, settings.variance_c_d)
+        self.content_neutral = np.random.normal(settings.content_neutral, settings.standard_variance)
+
+    def step(self, now):
+        if self.state['id'] == 0:
+            self.neutral_behaviour()
+        if self.state['id'] == 1:
+            self.discontent_behaviour()
+        if self.state['id'] == 2:
+            self.content_behaviour()
+
+        self.attrs['status'] = self.state['id']
+        super().step(now)
+
+    def neutral_behaviour(self):
+        # Spontaneous effects
+        if random.random() < self.neutral_discontent_spon_prob:
+            self.state['id'] = 1
+        if random.random() < self.neutral_content_spon_prob:
+            self.state['id'] = 2
+
+        # Infected
+        discontent_neighbors = self.get_neighboring_agents(state_id=1)
+        if random.random() < len(discontent_neighbors) * self.neutral_discontent_infected_prob:
+            self.state['id'] = 1
+        content_neighbors = self.get_neighboring_agents(state_id=2)
+        if random.random() < len(content_neighbors) * self.neutral_content_infected_prob:
+            self.state['id'] = 2
+
+    def discontent_behaviour(self):
+        # Healing
+        if random.random() < self.discontent_neutral:
+            self.state['id'] = 0
+
+        # Superinfected
+        content_neighbors = self.get_neighboring_agents(state_id=2)
+        if random.random() < len(content_neighbors) * self.discontent_content:
+            self.state['id'] = 2
+
+    def content_behaviour(self):
+        # Healing
+        if random.random() < self.content_neutral:
+            self.state['id'] = 0
+
+        # Superinfected
+        discontent_neighbors = self.get_neighboring_agents(state_id=1)
+        if random.random() < len(discontent_neighbors) * self.content_discontent:
+            self.state['id'] = 1

models/BigMarketModel/__init__.py

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+from .BigMarketModel import BigMarketModel
+from .SISaModel import SISaModel