def maxmin_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
ensemble.parallel = True
ensemble.processes = 12
def obj_function1(outcomes):
return outcomes['y']
policy_levers = { "L1": (0,1),
"L2": (0,1)}
results = ensemble.perform_maximin_optimization(obj_function1 = obj_function1,
policy_levers = policy_levers,
minimax1='minimize',
nrOfGenerations1=50,
nrOfPopMembers1=200,
minimax2 = "maximize",
nrOfGenerations2 = 50,
nrOfPopMembers2 = 100,
)
graph_errorbars_raw(results['stats'])
plt.show()
def visualize_optimization():
import cPickle
results = cPickle.load(open("..\..\..\models\CANER\Flu\SD\maximintest.cPickle"))
# best_case, best_individual_score, results = results
graph_errorbars_raw(results['stats'])
graph_pop_heatmap_raw(results['raw'])
plt.show()
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 robust_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_levers = { "L1": (0,1),
"L2": (0,1)}
def obj_func(results):
return np.average(results['y'])
results = ensemble.perform_robust_optimization(cases=1000,
obj_function=obj_func,
policy_levers=policy_levers,
minimax='minimize',
nrOfGenerations=50,
nrOfPopMembers=20 )
graph_errorbars_raw(results['stats'])
plt.show()
outcome = outcomes['deceased population region 1']
zeros = np.zeros((outcome.shape[0], 1))
zeros[outcome[:,-1]<1000000] = 1
value = np.sum(zeros)/zeros.shape[0]
return value
if __name__ == "__main__":
# ema_logging.log_to_stderr(ema_logging.INFO)
# model = FluModel(r'..\..\..\models\CANER\Flu\SD', "fluCase")
#
# ensemble = ModelEnsemble()
# ensemble.set_model_structure(model)
# ensemble.parallel = True
#
# policy_levers = {'trackperiod': (1,8),
# 'delaytime': (0.01,2)}
#
# res = ensemble.perform_robust_optimization(cases=1000,
# obj_function=obj_func,
# policy_levers = policy_levers,
# nrOfPopMembers=50,
# nrOfGenerations=50,
# crossoverRate=0.7,
# mutationRate=0.01)
# cPickle.dump(res, open(r'FLU robust optimization results.cPickle', 'w'))
res = cPickle.load(open(r'FLU robust optimization results.cPickle', 'r'))
graph_pop_heatmap_raw(res['raw'])
graph_errorbars_raw(res['stats'])
plt.show()
