mirror of
https://github.com/gsi-upm/soil
synced 2024-11-13 23:12:28 +00:00
requirements added for faster install, settings separated in JSON file, now is possible to run different agent simulations at a time
This commit is contained in:
parent
aaf5f709f1
commit
f8d8345d5a
5
requirements.txt
Normal file
5
requirements.txt
Normal file
@ -0,0 +1,5 @@
|
|||||||
|
nxsim
|
||||||
|
simpy
|
||||||
|
networkx
|
||||||
|
numpy
|
||||||
|
matplotlib
|
@ -1,5 +1,7 @@
|
|||||||
# General configuration
|
# General configuration
|
||||||
|
|
||||||
|
import json
|
||||||
|
|
||||||
# Network settings
|
# Network settings
|
||||||
network_type = 1
|
network_type = 1
|
||||||
number_of_nodes = 1000
|
number_of_nodes = 1000
|
||||||
@ -7,6 +9,12 @@ max_time = 50
|
|||||||
num_trials = 1
|
num_trials = 1
|
||||||
timeout = 2
|
timeout = 2
|
||||||
|
|
||||||
|
|
||||||
|
with open('simulation_settings.json', 'r') as f:
|
||||||
|
environment_params = json.load(f)
|
||||||
|
|
||||||
|
'''
|
||||||
|
|
||||||
environment_params = {
|
environment_params = {
|
||||||
# Zombie model
|
# Zombie model
|
||||||
'bite_prob': 0.01, # 0-1
|
'bite_prob': 0.01, # 0-1
|
||||||
@ -62,3 +70,4 @@ environment_params = {
|
|||||||
'prob_vaccinated_vaccinate_neutral': 0.035,
|
'prob_vaccinated_vaccinate_neutral': 0.035,
|
||||||
'prob_generate_anti_rumor': 0.035
|
'prob_generate_anti_rumor': 0.035
|
||||||
}
|
}
|
||||||
|
'''
|
53
simulation_settings.json
Normal file
53
simulation_settings.json
Normal file
@ -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
|
||||||
|
}
|
170
soil.py
170
soil.py
@ -8,6 +8,84 @@ import models
|
|||||||
import math
|
import math
|
||||||
import json
|
import json
|
||||||
|
|
||||||
|
#################
|
||||||
|
# Visualization #
|
||||||
|
#################
|
||||||
|
|
||||||
|
def visualization(graph_name):
|
||||||
|
|
||||||
|
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, graph_name+".gexf", version="1.2draft")
|
||||||
|
|
||||||
|
###########
|
||||||
|
# Results #
|
||||||
|
###########
|
||||||
|
def results(model_name):
|
||||||
|
x_values = []
|
||||||
|
infected_values = []
|
||||||
|
neutral_values = []
|
||||||
|
cured_values = []
|
||||||
|
vaccinated_values = []
|
||||||
|
|
||||||
|
attribute_plot = 'status'
|
||||||
|
for time in range(0, settings.max_time):
|
||||||
|
value_infectados = 0
|
||||||
|
value_neutral = 0
|
||||||
|
value_cured = 0
|
||||||
|
value_vaccinated = 0
|
||||||
|
real_time = time * settings.timeout
|
||||||
|
activity = False
|
||||||
|
for x in range(0, settings.number_of_nodes):
|
||||||
|
if attribute_plot in models.networkStatus["agent_%s" % x]:
|
||||||
|
if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
|
||||||
|
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1: ## Infected
|
||||||
|
value_infectados += 1
|
||||||
|
activity = True
|
||||||
|
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ## Neutral
|
||||||
|
value_neutral += 1
|
||||||
|
activity = True
|
||||||
|
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2: ## Cured
|
||||||
|
value_cured += 1
|
||||||
|
activity = True
|
||||||
|
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3: ## Vaccinated
|
||||||
|
value_vaccinated += 1
|
||||||
|
activity = True
|
||||||
|
|
||||||
|
if activity:
|
||||||
|
x_values.append(real_time)
|
||||||
|
infected_values.append(value_infectados)
|
||||||
|
neutral_values.append(value_neutral)
|
||||||
|
cured_values.append(value_cured)
|
||||||
|
vaccinated_values.append(value_vaccinated)
|
||||||
|
activity = False
|
||||||
|
|
||||||
|
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()
|
||||||
|
|
||||||
|
|
||||||
####################
|
####################
|
||||||
# Network creation #
|
# Network creation #
|
||||||
@ -26,82 +104,30 @@ if settings.network_type == 2:
|
|||||||
# Simulation #
|
# Simulation #
|
||||||
##############
|
##############
|
||||||
|
|
||||||
sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2, max_time=settings.max_time,
|
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)
|
num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
|
||||||
|
sim.run_simulation()
|
||||||
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))
|
||||||
|
|
||||||
|
|
||||||
###########
|
|
||||||
# Results #
|
|
||||||
###########
|
|
||||||
|
|
||||||
x_values = []
|
|
||||||
infected_values = []
|
|
||||||
neutral_values = []
|
|
||||||
cured_values = []
|
|
||||||
vaccinated_values = []
|
|
||||||
|
|
||||||
attribute_plot = 'status'
|
|
||||||
for time in range(0, settings.max_time):
|
|
||||||
value_infectados = 0
|
|
||||||
value_neutral = 0
|
|
||||||
value_cured = 0
|
|
||||||
value_vaccinated = 0
|
|
||||||
real_time = time * settings.timeout
|
|
||||||
activity = False
|
|
||||||
for x in range(0, settings.number_of_nodes):
|
|
||||||
if attribute_plot in models.networkStatus["agent_%s" % x]:
|
|
||||||
if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
|
|
||||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1: ## Infected
|
|
||||||
value_infectados += 1
|
|
||||||
activity = True
|
|
||||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ## Neutral
|
|
||||||
value_neutral += 1
|
|
||||||
activity = True
|
|
||||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2: ## Cured
|
|
||||||
value_cured += 1
|
|
||||||
activity = True
|
|
||||||
if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3: ## Vaccinated
|
|
||||||
value_vaccinated += 1
|
|
||||||
activity = True
|
|
||||||
|
|
||||||
if activity:
|
|
||||||
x_values.append(real_time)
|
|
||||||
infected_values.append(value_infectados)
|
|
||||||
neutral_values.append(value_neutral)
|
|
||||||
cured_values.append(value_cured)
|
|
||||||
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')
|
|
||||||
# plt.show()
|
|
||||||
|
|
||||||
|
|
||||||
#################
|
|
||||||
# Visualization #
|
|
||||||
#################
|
|
||||||
|
|
||||||
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")
|
|
||||||
|
Loading…
Reference in New Issue
Block a user