def test_optimization(): if os.name != 'nt': return ema_logging.log_to_stderr(ema_logging.INFO) model = FluModel(r'../models', "fluCase") ensemble = ModelEnsemble() ensemble.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)
def test_vensim_model(self): #instantiate a model wd = r'../models' model = VensimExampleModel(wd, "simpleModel") #instantiate an ensemble ensemble = ModelEnsemble() #set the model on the ensemble ensemble.model_structure = model nr_runs = 10 experiments, outcomes = ensemble.perform_experiments(nr_runs) self.assertEqual(experiments.shape[0], nr_runs) self.assertIn('TIME', outcomes.keys()) self.assertIn(model.outcomes[0].name, outcomes.keys())
def test_running_lookup_uncertainties(self): ''' This is the more comprehensive test, given that the lookup uncertainty replaces itself with a bunch of other uncertainties, check whether we can successfully run a set of experiments and get results back. We assert that the uncertainties are correctly replaced by analyzing the experiments array. ''' if os.name != 'nt': return model = LookupTestModel(r'../models/', 'lookupTestModel') #model.step = 4 #reduce data to be stored ensemble = ModelEnsemble() ensemble.model_structure = model ensemble.perform_experiments(10)
def test_running_lookup_uncertainties(self): ''' This is the more comprehensive test, given that the lookup uncertainty replaces itself with a bunch of other uncertainties, check whether we can successfully run a set of experiments and get results back. We assert that the uncertainties are correctly replaced by analyzing the experiments array. ''' if os.name != 'nt': return model = LookupTestModel( r'../models/', 'lookupTestModel') #model.step = 4 #reduce data to be stored ensemble = ModelEnsemble() ensemble.model_structure = model ensemble.perform_experiments(10)
def test_optimization(): if os.name != 'nt': return ema_logging.log_to_stderr(ema_logging.INFO) model = FluModel(r'../models', "fluCase") ensemble = ModelEnsemble() ensemble.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)
This class represents a simple example of how one can extent the basic ModelStructureInterface in order to do EMA on a simple model coded in Python directly ''' #specify uncertainties uncertainties = [ ParameterUncertainty((0.1, 10), "x1"), ParameterUncertainty((-0.01, 0.01), "x2"), ParameterUncertainty((-0.01, 0.01), "x3") ] #specify outcomes outcomes = [Outcome('y')] def model_init(self, policy, kwargs): pass def run_model(self, case): """Method for running an instantiated model structure """ self.output[ self.outcomes[0].name] = case['x1'] * case['x2'] + case['x3'] if __name__ == '__main__': ema_logging.log_to_stderr(ema_logging.INFO) model = SimplePythonModel(None, 'simpleModel') #instantiate the model ensemble = ModelEnsemble() #instantiate an ensemble ensemble.parallel = True ensemble.model_structure = model #set the model on the ensemble results = ensemble.perform_experiments(1000) #run 1000 experiments
"effect of high energy on further recovery lookup", self, 0, 1.25), LookupUncertainty( 'hearne2', [(-2, 2), (-1, 1), (0, 100), (20, 120), (0.5, 1.5), (0.5, 2)], "effect of hours worked on energy recovery lookup", self, 0, 1.5), LookupUncertainty('approximation', [(-0.5, 0.35), (3, 5), (1, 10), (0.2, 0.4), (0, 120)], "effect of hours worked on energy drain lookup", self, 0, 3), LookupUncertainty( 'hearne1', [(0, 1), (0, 0.15), (1, 1.5), (0.75, 1.25)], "effect of low energy on further depletion lookup", self, 0, 1) ] self._delete_lookup_uncertainties() if __name__ == "__main__": ema_logging.log_to_stderr(ema_logging.INFO) model = Burnout(r'./models/burnout', "burnout") ensemble = ModelEnsemble() ensemble.model_structure = model #run policy with old cases results = ensemble.perform_experiments(100) lines(results, 'Energy Level', density=BOXPLOT) plt.show()
"susceptible to immune population delay time region 1"), ParameterUncertainty((0.5,2), "susceptible to immune population delay time region 2"), ParameterUncertainty((0.01, 5), "root contact rate region 1"), ParameterUncertainty((0.01, 5), "root contact ratio region 2"), ParameterUncertainty((0, 0.15), "infection ratio region 1"), ParameterUncertainty((0, 0.15), "infection rate region 2"), ParameterUncertainty((10, 100), "normal contact rate region 1"), ParameterUncertainty((10, 200), "normal contact rate region 2")] if __name__ == "__main__": ema_logging.log_to_stderr(ema_logging.INFO) model = FluModel(r'./models/flu', "fluCase") ensemble = ModelEnsemble() ensemble.model_structure = model ensemble.parallel = True #turn on parallel processing nr_experiments = 1000 results = ensemble.perform_experiments(nr_experiments) fh = r'./data/{} flu cases no policy.tar.gz'.format(nr_experiments) save_results(results, fh)
susceptible_population_region_2 = susceptible_population_region_2_NEXT immune_population_region_1 = immune_population_region_1_NEXT immune_population_region_2 = immune_population_region_2_NEXT deceased_population_region_1.append(deceased_population_region_1_NEXT) deceased_population_region_2.append(deceased_population_region_2_NEXT) #End of main code return (runTime, deceased_population_region_1) #, Max_infected, Max_time) if __name__ == "__main__": np.random.seed(150) #set the seed for replication purposes ema_logging.log_to_stderr(ema_logging.INFO) fluModel = MexicanFlu(None, "mexicanFluExample") ensemble = ModelEnsemble() ensemble.parallel = True ensemble.model_structure = fluModel nr_experiments = 500 results = ensemble.perform_experiments(nr_experiments, reporting_interval=100) lines(results, outcomes_to_show="deceased_population_region_1", show_envelope=True, density=KDE, titles=None, experiments_to_show=np.arange(0, nr_experiments, 10) ) plt.show()
#specify outcomes outcomes = [Outcome('a', time=True)] #specify your uncertainties uncertainties = [ ParameterUncertainty((0, 2.5), "x11"), ParameterUncertainty((-2.5, 2.5), "x12") ] if __name__ == "__main__": #turn on logging ema_logging.log_to_stderr(ema_logging.INFO) #instantiate a model wd = r'./models/vensim example' vensimModel = VensimExampleModel(wd, "simpleModel") #instantiate an ensemble ensemble = ModelEnsemble() #set the model on the ensemble ensemble.model_structure = vensimModel #run in parallel, if not set, FALSE is assumed ensemble.parallel = True #perform experiments result = ensemble.perform_experiments(1000)
#note that this reference to the model should be relative #this relative path will be combined with the workingDirectory model_file = r'\model.vpm' #specify outcomes outcomes = [Outcome('a', time=True)] #specify your uncertainties uncertainties = [ParameterUncertainty((0, 2.5), "x11"), ParameterUncertainty((-2.5, 2.5), "x12")] if __name__ == "__main__": #turn on logging ema_logging.log_to_stderr(ema_logging.INFO) #instantiate a model wd = r'./models/vensim example' vensimModel = VensimExampleModel(wd, "simpleModel") #instantiate an ensemble ensemble = ModelEnsemble() #set the model on the ensemble ensemble.model_structure = vensimModel #run in parallel, if not set, FALSE is assumed ensemble.parallel = True #perform experiments result = ensemble.perform_experiments(1000)
class SimplePythonModel(ModelStructureInterface): ''' This class represents a simple example of how one can extent the basic ModelStructureInterface in order to do EMA on a simple model coded in Python directly ''' #specify uncertainties uncertainties = [ParameterUncertainty((0.1, 10), "x1"), ParameterUncertainty((-0.01,0.01), "x2"), ParameterUncertainty((-0.01,0.01), "x3")] #specify outcomes outcomes = [Outcome('y')] def model_init(self, policy, kwargs): pass def run_model(self, case): """Method for running an instantiated model structure """ self.output[self.outcomes[0].name] = case['x1']*case['x2']+case['x3'] if __name__ == '__main__': ema_logging.log_to_stderr(ema_logging.INFO) model = SimplePythonModel(None, 'simpleModel') #instantiate the model ensemble = ModelEnsemble() #instantiate an ensemble ensemble.parallel = True ensemble.model_structure = model #set the model on the ensemble results = ensemble.perform_experiments(1000) #run 1000 experiments