def test_optimization():
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'..\data', "fluCase")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel=True
# ensemble.processes = 12
stats, pop = ensemble.perform_outcome_optimization(obj_function = obj_function_multi,
reporting_interval=10,
weights=(MAXIMIZE, MAXIMIZE),
pop_size=100,
nr_of_generations=5,
crossover_rate=0.5,
mutation_rate=0.05)
res = stats.hall_of_fame.keys
x = [entry.values[0] for entry in res]
y = [entry.values[1] for entry in res]
print len(x), len(y)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x,y)
ax.set_ylabel("deceased population")
ax.set_xlabel("infected fraction")
plt.show()
def test_optimization():
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'..\data', "fluCase")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel=True
stats, pop = ensemble.perform_outcome_optimization(obj_function = obj_function_multi,
reporting_interval=100,
weights=(MAXIMIZE, MAXIMIZE),
pop_size=100,
nr_of_generations=20,
crossover_rate=0.5,
mutation_rate=0.05,
caching=False)
res = stats.hall_of_fame.keys
print len(stats.tried_solutions.values())
def outcome_optimize():
ema_logging.log_to_stderr(ema_logging.INFO)
model = TestModel("", 'simpleModel') #instantiate the model
ensemble = ModelEnsemble() #instantiate an ensemble
ensemble.set_model_structure(model) #set the model on the ensemble
policy = {"name": "test",
"L1": 1,
"L2": 1}
ensemble.add_policy(policy)
def obj_func(results):
return results['y']
results = ensemble.perform_outcome_optimization(obj_function=obj_func,
minimax = 'minimize',
nrOfGenerations = 1000,
nrOfPopMembers = 10)
graph_errorbars_raw(results['stats'])
plt.show()
def test_optimization():
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'../models', "fluCase")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel=True
pop_size = 8
nr_of_generations = 10
eps = np.array([1e-3, 1e6])
stats, pop = ensemble.perform_outcome_optimization(obj_function = obj_function_multi,
algorithm=epsNSGA2,
reporting_interval=100,
weights=(MAXIMIZE, MAXIMIZE),
pop_size=pop_size,
nr_of_generations=nr_of_generations,
crossover_rate=0.8,
mutation_rate=0.05,
eps=eps)
fn = '../data/test optimization save.bz2'