Merge branch 'models' of https://lab.cluster.gsi.dit.upm.es/soil/soil into models

requirements.txt

@@ -0,0 +1,5 @@
+nxsim
+simpy
+networkx
+numpy
+matplotlib

settings.py

@@ -1,5 +1,7 @@
 # General configuration
 
+import json
+
 # Network settings
 network_type = 1
 number_of_nodes = 1000
@@ -7,6 +9,12 @@ max_time = 50
 num_trials = 1
 timeout = 2
 
+
+with open('simulation_settings.json', 'r') as f:
+    environment_params = json.load(f)
+
+'''
+
 environment_params = {
     # Zombie model
     'bite_prob': 0.01,  # 0-1
@@ -62,3 +70,4 @@ environment_params = {
     'prob_vaccinated_vaccinate_neutral': 0.035,
     'prob_generate_anti_rumor': 0.035
 }
+'''

simulation_settings.json

@@ -0,0 +1,53 @@
+{
+
+    "agent": ["BaseBehaviour","SISaModel","ControlModelM2"],
+
+
+    "bite_prob": 0.01,
+    "heal_prob": 0.01,
+
+    "innovation_prob": 0.001,
+    "imitation_prob": 0.005,
+
+    "outside_effects_prob": 0.2,
+    "anger_prob": 0.06,
+    "joy_prob": 0.05,
+    "sadness_prob": 0.02,
+    "disgust_prob": 0.02,
+
+    "enterprises": ["BBVA", "Santander", "Bankia"],
+
+    "tweet_probability_users": 0.44,
+    "tweet_relevant_probability": 0.25,
+    "tweet_probability_about": [0.15, 0.15, 0.15],
+    "sentiment_about": [0, 0, 0],  
+
+    "tweet_probability_enterprises": [0.3, 0.3, 0.3],
+
+    "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,
+
+
+    "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
+}

soil.py

@@ -8,34 +8,35 @@ import models
 import math
 import json
 
+#################
+# Visualization #
+#################
 
-####################
-# Network creation #
-####################
+def visualization(graph_name):
 
-if settings.network_type == 0:
-    G = nx.complete_graph(settings.number_of_nodes)
-if settings.network_type == 1:
-    G = nx.barabasi_albert_graph(settings.number_of_nodes, 10)
-if settings.network_type == 2:
-    G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
-# More types of networks can be added here
+    for x in range(0, settings.number_of_nodes):
+        for attribute in models.networkStatus["agent_%s" % x]:
+            emotionStatusAux = []
+            for t_step in models.networkStatus["agent_%s" % x][attribute]:
+                prec = 2
+                output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec)  # 2 decimals
+                emotionStatusAux.append((output, t_step,None))
+            attributes = {}
+            attributes[attribute] = emotionStatusAux
+            G.add_node(x, attributes)
 
 
-##############
-# Simulation #
-##############
+    print("Done!")
 
-sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2, max_time=settings.max_time,
-                        num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
-
-sim.run_simulation()
+    with open('data.txt', 'w') as outfile:
+        json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
 
+    nx.write_gexf(G, graph_name+".gexf", version="1.2draft")
 
 ###########
 # Results #
 ###########
-
+def results(model_name):
     x_values = []
     infected_values = []
     neutral_values = []
@@ -74,34 +75,59 @@ for time in range(0, settings.max_time):
             vaccinated_values.append(value_vaccinated)
             activity = False
 
-infected_line = plt.plot(x_values, infected_values, label='Infected')
-neutral_line = plt.plot(x_values, neutral_values, label='Neutral')
-cured_line = plt.plot(x_values, cured_values, label='Cured')
-vaccinated_line = plt.plot(x_values, vaccinated_values, label='Vaccinated')
-plt.legend()
-plt.savefig('control_model.png')
+    fig1 = plt.figure()
+    ax1 = fig1.add_subplot(111)
+
+    infected_line = ax1.plot(x_values, infected_values, label='Infected')
+    neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')
+    cured_line = ax1.plot(x_values, cured_values, label='Cured')
+    vaccinated_line = ax1.plot(x_values, vaccinated_values, label='Vaccinated')
+    ax1.legend()
+    fig1.savefig(model_name+'.png')
     # plt.show()
 
 
-#################
-# Visualization #
-#################
+####################
+# Network creation #
+####################
+
+if settings.network_type == 0:
+    G = nx.complete_graph(settings.number_of_nodes)
+if settings.network_type == 1:
+    G = nx.barabasi_albert_graph(settings.number_of_nodes, 10)
+if settings.network_type == 2:
+    G = nx.margulis_gabber_galil_graph(settings.number_of_nodes, None)
+# More types of networks can be added here
+
+
+##############
+# Simulation #
+##############
+
+agents = settings.environment_params['agent']
+
+
+
+print("Using Agent(s): {agents}".format(agents=agents))
+
+if len(agents) > 1:
+    for agent in agents:
+        sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.max_time,
+                        num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
+        sim.run_simulation()
+        print(str(agent))
+        results(str(agent))
+        visualization(str(agent))
+else:
+    agent = agents[0]
+    sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.max_time,
+                        num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
+    sim.run_simulation()
+    results(str(agent))
+    visualization(str(agent))
+
 
-for x in range(0, settings.number_of_nodes):
-    for attribute in models.networkStatus["agent_%s" % x]:
-        emotionStatusAux = []
-        for t_step in models.networkStatus["agent_%s" % x][attribute]:
-            prec = 2
-            output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec)  # 2 decimals
-            emotionStatusAux.append((output, t_step,None))
-        attributes = {}
-        attributes[attribute] = emotionStatusAux
-        G.add_node(x, attributes)
 
 
-print("Done!")
 
-with open('data.txt', 'w') as outfile:
-    json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
 
-nx.write_gexf(G, "test.gexf", version="1.2draft")