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()
ParameterUncertainty((0.45,0.55),
"tH"),
ParameterUncertainty((0.1,0.3),
"kk")]
#specification of the outcomes
outcomes = [Outcome("B4:B1076", time=True), #we can refer to a range in the normal way
Outcome("P_t", time=True)] # we can also use named range
#name of the sheet
sheet = "Sheet1"
#relative path to the Excel file
workbook = r'\excel example.xlsx'
if __name__ == "__main__":
ema_logging.log_to_stderr(level=ema_logging.INFO)
model = ExcelModel(r"./models/excelModel", "predatorPrey")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True #turn on parallel computing
ensemble.processes = 2 #using only 2 cores
#run 100 experiments
nr_experiments = 100
results = ensemble.perform_experiments(nr_experiments)
# 'TimeHorizonGov2':50,
# 'TimeHorizonGov3':50,
# 'TimeHorizonGov4':50,
# 'TimeHorizonGov5':50,
# 'TimeHorizonInd1':50,
# 'TimeHorizonInd2':50,
# 'TimeHorizonInd3':50,
# 'TimeHorizonInd4':50,
# 'TimeHorizonInd5':50}
# msi2 = EVO(r"./models", 'longTimeHorizon', defaults=defaults)
msi1 = EVO('./models', 'full')
#instantiate an ensemble
ensemble = ModelEnsemble()
#set the model on the ensemble
ensemble.add_model_structure(msi1)
# ensemble.add_model_structure(msi2)
ensemble.parallel = True
ensemble.processes = 36
#perform experiments
nr_experiments = 1000
results = ensemble.perform_experiments(nr_experiments,
reporting_interval=100)
fn = r'.\data\full {} exp {} rep.tar.gz'.format(nr_experiments,
msi1.nr_replications)
save_results(results, fn)
