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_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_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())
''' Created on Mar 15, 2012 .. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl> ''' from analysis import clusterer from util import ema_logging from core import ModelEnsemble from test.scarcity_example import ScarcityModel if __name__ == "__main__": ema_logging.log_to_stderr(ema_logging.INFO) model = ScarcityModel(r'..\..\src\test', "fluCase") ensemble = ModelEnsemble() ensemble.set_model_structure(model) ensemble.parallel = True results = ensemble.perform_experiments(200) clusterer.cluster(data=results, outcome='relative market price', distance='gonenc', cMethod='maxclust', cValue=5, plotDendrogram=False)
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
"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)
outcomes = [ Outcome('sheep', time=True), Outcome('wolves', time=True), Outcome('grass', time=True) # TODO patches not working in reporting ] if __name__ == "__main__": #turn on logging ema_logging.log_to_stderr(ema_logging.INFO) #instantiate a model fh = r"./models/predatorPreyNetlogo" model = PredatorPrey(fh, "simpleModel") #instantiate an ensemble ensemble = ModelEnsemble() #set the model on the ensemble ensemble.model_structure = model #run in parallel, if not set, FALSE is assumed ensemble.parallel = True #perform experiments results = ensemble.perform_experiments(100, reporting_interval=1) plotting.lines(results, density=plotting_util.KDE) plt.show()
else: atomicBehavior.append([last, steps]) last = entry steps = 0 atomicBehavior.append([last, steps]) behavior = [] behavior.append(atomicBehavior.pop(0)) for entry in atomicBehavior: if entry[0] != behavior[-1][0] and entry[1] >2: behavior.append(entry) elif entry[1] <2: continue else: behavior[-1][1] =+ entry[1] behavior = [entry[0] for entry in behavior] return behavior if __name__ == "__main__": ema_logging.log_to_stderr(ema_logging.INFO) model = ScarcityModel(r'..\data', "scarcity") ensemble = ModelEnsemble() ensemble.set_model_structure(model) ensemble.parallel = True results = ensemble.perform_experiments(100) # determineBehavior(results)
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)