mirror of
https://github.com/gsi-upm/soil
synced 2024-11-13 06:52:28 +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:
Alberto Pascual
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5
requirements.txt
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5
requirements.txt
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@ -0,0 +1,5 @@
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nxsim
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simpy
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networkx
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numpy
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matplotlib
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@ -1,5 +1,7 @@
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# General configuration
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import json
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# Network settings
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network_type = 1
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number_of_nodes = 1000
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@ -7,6 +9,12 @@ max_time = 50
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num_trials = 1
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timeout = 2
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with open('simulation_settings.json', 'r') as f:
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environment_params = json.load(f)
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'''
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environment_params = {
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# Zombie model
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'bite_prob': 0.01, # 0-1
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@ -62,3 +70,4 @@ environment_params = {
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'prob_vaccinated_vaccinate_neutral': 0.035,
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'prob_generate_anti_rumor': 0.035
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}
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'''
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53
simulation_settings.json
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53
simulation_settings.json
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@ -0,0 +1,53 @@
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{
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"agent": ["BaseBehaviour","SISaModel","ControlModelM2"],
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"bite_prob": 0.01,
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"heal_prob": 0.01,
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"innovation_prob": 0.001,
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"imitation_prob": 0.005,
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"outside_effects_prob": 0.2,
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"anger_prob": 0.06,
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"joy_prob": 0.05,
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"sadness_prob": 0.02,
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"disgust_prob": 0.02,
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"enterprises": ["BBVA", "Santander", "Bankia"],
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"tweet_probability_users": 0.44,
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"tweet_relevant_probability": 0.25,
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"tweet_probability_about": [0.15, 0.15, 0.15],
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"sentiment_about": [0, 0, 0],
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"tweet_probability_enterprises": [0.3, 0.3, 0.3],
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"neutral_discontent_spon_prob": 0.04,
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"neutral_discontent_infected_prob": 0.04,
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"neutral_content_spon_prob": 0.18,
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"neutral_content_infected_prob": 0.02,
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"discontent_neutral": 0.13,
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"discontent_content": 0.07,
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"variance_d_c": 0.02,
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"content_discontent": 0.009,
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"variance_c_d": 0.003,
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"content_neutral": 0.088,
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"standard_variance": 0.055,
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"prob_neutral_making_denier": 0.035,
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"prob_infect": 0.075,
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"prob_cured_healing_infected": 0.035,
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"prob_cured_vaccinate_neutral": 0.035,
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"prob_vaccinated_healing_infected": 0.035,
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"prob_vaccinated_vaccinate_neutral": 0.035,
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"prob_generate_anti_rumor": 0.035
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}
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170
soil.py
170
soil.py
@ -8,6 +8,84 @@ import models
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import math
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import json
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#################
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# Visualization #
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#################
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def visualization(graph_name):
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for x in range(0, settings.number_of_nodes):
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for attribute in models.networkStatus["agent_%s" % x]:
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emotionStatusAux = []
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for t_step in models.networkStatus["agent_%s" % x][attribute]:
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prec = 2
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output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals
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emotionStatusAux.append((output, t_step,None))
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attributes = {}
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attributes[attribute] = emotionStatusAux
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G.add_node(x, attributes)
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print("Done!")
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with open('data.txt', 'w') as outfile:
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json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
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nx.write_gexf(G, graph_name+".gexf", version="1.2draft")
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###########
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# Results #
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###########
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def results(model_name):
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x_values = []
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infected_values = []
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neutral_values = []
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cured_values = []
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vaccinated_values = []
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attribute_plot = 'status'
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for time in range(0, settings.max_time):
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value_infectados = 0
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value_neutral = 0
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value_cured = 0
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value_vaccinated = 0
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real_time = time * settings.timeout
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activity = False
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for x in range(0, settings.number_of_nodes):
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if attribute_plot in models.networkStatus["agent_%s" % x]:
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if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1: ## Infected
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value_infectados += 1
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activity = True
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ## Neutral
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value_neutral += 1
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activity = True
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2: ## Cured
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value_cured += 1
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activity = True
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3: ## Vaccinated
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value_vaccinated += 1
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activity = True
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if activity:
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x_values.append(real_time)
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infected_values.append(value_infectados)
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neutral_values.append(value_neutral)
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cured_values.append(value_cured)
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vaccinated_values.append(value_vaccinated)
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activity = False
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fig1 = plt.figure()
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ax1 = fig1.add_subplot(111)
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infected_line = ax1.plot(x_values, infected_values, label='Infected')
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neutral_line = ax1.plot(x_values, neutral_values, label='Neutral')
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cured_line = ax1.plot(x_values, cured_values, label='Cured')
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vaccinated_line = ax1.plot(x_values, vaccinated_values, label='Vaccinated')
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ax1.legend()
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fig1.savefig(model_name+'.png')
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# plt.show()
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####################
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# Network creation #
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@ -26,82 +104,30 @@ if settings.network_type == 2:
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# Simulation #
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##############
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=ControlModelM2, max_time=settings.max_time,
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agents = settings.environment_params['agent']
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print("Using Agent(s): {agents}".format(agents=agents))
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if len(agents) > 1:
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for agent in agents:
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.max_time,
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num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
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sim.run_simulation()
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sim.run_simulation()
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print(str(agent))
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results(str(agent))
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visualization(str(agent))
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else:
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agent = agents[0]
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sim = NetworkSimulation(topology=G, states=init_states, agent_type=locals()[agent], max_time=settings.max_time,
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num_trials=settings.num_trials, logging_interval=1.0, **settings.environment_params)
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sim.run_simulation()
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results(str(agent))
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visualization(str(agent))
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###########
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# Results #
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###########
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x_values = []
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infected_values = []
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neutral_values = []
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cured_values = []
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vaccinated_values = []
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attribute_plot = 'status'
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for time in range(0, settings.max_time):
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value_infectados = 0
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value_neutral = 0
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value_cured = 0
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value_vaccinated = 0
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real_time = time * settings.timeout
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activity = False
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for x in range(0, settings.number_of_nodes):
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if attribute_plot in models.networkStatus["agent_%s" % x]:
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if real_time in models.networkStatus["agent_%s" % x][attribute_plot]:
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 1: ## Infected
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value_infectados += 1
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activity = True
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 0: ## Neutral
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value_neutral += 1
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activity = True
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 2: ## Cured
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value_cured += 1
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activity = True
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if models.networkStatus["agent_%s" % x][attribute_plot][real_time] == 3: ## Vaccinated
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value_vaccinated += 1
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activity = True
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if activity:
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x_values.append(real_time)
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infected_values.append(value_infectados)
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neutral_values.append(value_neutral)
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cured_values.append(value_cured)
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vaccinated_values.append(value_vaccinated)
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activity = False
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infected_line = plt.plot(x_values, infected_values, label='Infected')
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neutral_line = plt.plot(x_values, neutral_values, label='Neutral')
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cured_line = plt.plot(x_values, cured_values, label='Cured')
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vaccinated_line = plt.plot(x_values, vaccinated_values, label='Vaccinated')
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plt.legend()
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plt.savefig('control_model.png')
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# plt.show()
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#################
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# Visualization #
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#################
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for x in range(0, settings.number_of_nodes):
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for attribute in models.networkStatus["agent_%s" % x]:
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emotionStatusAux = []
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for t_step in models.networkStatus["agent_%s" % x][attribute]:
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prec = 2
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output = math.floor(models.networkStatus["agent_%s" % x][attribute][t_step] * (10 ** prec)) / (10 ** prec) # 2 decimals
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emotionStatusAux.append((output, t_step,None))
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attributes = {}
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attributes[attribute] = emotionStatusAux
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G.add_node(x, attributes)
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print("Done!")
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with open('data.txt', 'w') as outfile:
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json.dump(models.networkStatus, outfile, sort_keys=True, indent=4, separators=(',', ': '))
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nx.write_gexf(G, "test.gexf", version="1.2draft")
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