Python ModelEnsemble.perform_experiments方法代码示例

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
def transition_test():

    model = EnergyTrans(r"..\..\..\models\EnergyTrans", "fluCase")
    ensemble = ModelEnsemble()
    ensemble.set_model_structure(model)

    ensemble.perform_experiments(cases=10, callback=HDF5Callback)

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
def flu_test():

    model = FluModel(r"..\..\..\models\flu", "fluCase")
    ensemble = ModelEnsemble()
    ensemble.set_model_structure(model)

    ensemble.perform_experiments(cases=10, callback=HDF5Callback)

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
def test_inspect():
    import inspect_test
    model = FluModel(r'..\..\..\models\flu', "fluCase")
    ensemble = ModelEnsemble()
    ensemble.set_model_structure(model)
    
    ensemble.perform_experiments(cases = 10,
                                 callback=inspect_test.InspectCallback)

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
def test_save_results():
    os.remove("test.h5")

    nrOfExperiments = 10
    fileName = "test.h5"
    experimentName = "one_exp_test"

    ensemble = ModelEnsemble()
    ensemble.set_model_structure(FluModel(r"..\..\..\models\flu", "fluCase"))

    ensemble.perform_experiments(
        nrOfExperiments, callback=HDF5Callback, fileName=fileName, experimentName=experimentName
    )

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
 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. 
     
     '''
     model = LookupTestModel( r'../models/', 'lookupTestModel')
     
     #model.step = 4 #reduce data to be stored
     ensemble = ModelEnsemble()
     ensemble.set_model_structure(model)
     
     ensemble.perform_experiments(10)

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
def perform_experiments():
    ema_logging.log_to_stderr(level=ema_logging.INFO)
    model = SalinizationModel(r"C:\workspace\EMA-workbench\models\salinization", "verzilting")
    model.step = 4
    
    ensemble = ModelEnsemble()
    ensemble.set_model_structure(model)
    
    ensemble.parallel = True
    nr_of_experiments = 10000
    results = ensemble.perform_experiments(nr_of_experiments)
    return results

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
def test_multiple_models():
    class Model1(ModelStructureInterface):
        uncertainties = [ParameterUncertainty((0,1),"a"),
                         ParameterUncertainty((0,1),"b")]
        
        outcomes = [Outcome("test")]
        
        def model_init(self, policy, kwargs):
            pass
        
        def run_model(self, case):
            self.output['test'] = 1

    class Model2(ModelStructureInterface):
        uncertainties = [ParameterUncertainty((0,1),"b"),
                         ParameterUncertainty((0,1),"c")]
        
        outcomes = [Outcome("test")]
        
        def model_init(self, policy, kwargs):
            pass
        
        def run_model(self, case):
            self.output['test'] = 1
    
#    os.remove('test.h5')

    nrOfExperiments = 10
    fileName = 'test.h5'
    experimentName = "one_exp_test"
    
    ensemble = ModelEnsemble()
    ensemble.add_model_structure(Model1('', "test1"))
    ensemble.add_model_structure(Model2('', "test2"))
    
    ensemble.perform_experiments(nrOfExperiments,
                                 callback=HDF5Callback,
                                 fileName=fileName,
                                 experimentName=experimentName)

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
 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.set_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())

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
def test_tree():
    
    log_to_stderr(level= INFO)
        
    model = FluModel(r'..\..\models\flu', "fluCase")
    ensemble = ModelEnsemble()
    ensemble.parallel = True
    ensemble.set_model_structure(model)
    
    policies = [{'name': 'no policy',
                 'file': r'\FLUvensimV1basecase.vpm'},
                {'name': 'static policy',
                 'file': r'\FLUvensimV1static.vpm'},
                {'name': 'adaptive policy',
                 'file': r'\FLUvensimV1dynamic.vpm'}
                ]
    ensemble.add_policies(policies)
    
    results = ensemble.perform_experiments(10)
   
    a_tree = tree(results, classify)

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
def test_feature_selection():
    log_to_stderr(level= INFO)
        
    model = FluModel(r'..\..\models\flu', "fluCase")
    ensemble = ModelEnsemble()
    ensemble.parallel = True
    ensemble.set_model_structure(model)
    
    policies = [{'name': 'no policy',
                 'file': r'\FLUvensimV1basecase.vpm'},
                {'name': 'static policy',
                 'file': r'\FLUvensimV1static.vpm'},
                {'name': 'adaptive policy',
                 'file': r'\FLUvensimV1dynamic.vpm'}
                ]
    ensemble.add_policies(policies)
    
    results = ensemble.perform_experiments(5000)
   
    results = feature_selection(results, classify)
    for entry in results:
        print entry[0] +"\t" + str(entry[1])

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
    ]

    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
    vensimModel = PredatorPrey(r"..\..\models\predatorPreyNetlogo", "simpleModel")

    # instantiate an ensemble
    ensemble = ModelEnsemble()

    # set the model on the ensemble
    ensemble.set_model_structure(vensimModel)

    # run in parallel, if not set, FALSE is assumed
    ensemble.parallel = True

    # perform experiments
    results = ensemble.perform_experiments(100)

    plotting.lines(results, density=plotting.KDE)
    plt.show()

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
                             "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.set_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)

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
        susceptible_population_region_1 = susceptible_population_region_1_NEXT
        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__":
    import expWorkbench.ema_logging as logging
    np.random.seed(150) #set the seed for replication purposes
    logging.log_to_stderr(logging.INFO)
    
    fluModel = MexicanFlu(None, "mexicanFluExample")
    ensemble = ModelEnsemble()
    ensemble.parallel = True
    ensemble.set_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()

# 需要导入模块: from expWorkbench import ModelEnsemble [as 别名]
# 或者: from expWorkbench.ModelEnsemble import perform_experiments [as 别名]
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__":
    model = SimplePythonModel(None, "simpleModel")  # instantiate the model
    ensemble = ModelEnsemble()  # instantiate an ensemble
    ensemble.set_model_structure(model)  # set the model on the ensemble
    results = ensemble.perform_experiments(1000)  # generate 1000 cases