ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
policy_levers = {'Trigger a': {'type':'list', 'values':[0, 0.25, 0.5, 0.75, 1]},
'Trigger b': {'type':'list', 'values':[0, 0.25, 0.5, 0.75, 1]},
'Trigger c': {'type':'list', 'values':[0, 0.25, 0.5, 0.75, 1]}}
cases = ensemble._generate_samples(10, UNION)[0]
ensemble.add_policy({"name":None})
experiments = [entry for entry in ensemble._generate_experiments(cases)]
for entry in experiments:
entry.pop("model")
entry.pop("policy")
cases = experiments
stats, pop = ensemble.perform_robust_optimization(cases=cases,
reporting_interval=100,
obj_function=obj_func,
policy_levers=policy_levers,
weights = (MINIMIZE,)*2,
nr_of_generations=20,
algorithm=epsNSGA2,
pop_size=4,
crossover_rate=0.5,
mutation_rate=0.02,
caching=True,
eps=[0.01, 0.01]
)
'type': 'list',
'values': [0, 0.25, 0.5, 0.75, 1]
},
'Trigger c': {
'type': 'list',
'values': [0, 0.25, 0.5, 0.75, 1]
}
}
cases = ensemble._generate_samples(10, UNION)[0]
ensemble.add_policy({"name": None})
experiments = [entry for entry in ensemble._generate_experiments(cases)]
for entry in experiments:
entry.pop("model")
entry.pop("policy")
cases = experiments
stats, pop = ensemble.perform_robust_optimization(
cases=cases,
reporting_interval=100,
obj_function=obj_func,
policy_levers=policy_levers,
weights=(MINIMIZE, ) * 2,
nr_of_generations=20,
algorithm=epsNSGA2,
pop_size=4,
crossover_rate=0.5,
mutation_rate=0.02,
caching=True,
eps=[0.01, 0.01])
