if __name__ == "__main__": ema_logging.log_to_stderr(ema_logging.INFO) model = DummyModel(r"", "dummy") np.random.seed(123456789) 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,
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,