def test_parallel_experiment(self):
"""
Test running an experiment in parallel
Returns
-------
"""
from ema_workbench.connectors import PySDConnector
model = PySDConnector('../models/Teacup.mdl',
uncertainties_dict={'Room Temperature': (33, 120)},
outcomes_list=['Teacup Temperature'])
ensemble = ModelEnsemble() # instantiate an ensemble
ensemble.model_structure = model # set the model on the ensemble
ensemble.parallel = True
results = ensemble.perform_experiments(cases=20)
def test_add_outcomes(self):
from ema_workbench.connectors import PySDConnector
model = PySDConnector('../models/Teacup.mdl',
uncertainties_dict={'Room Temperature': (33, 120)},
outcomes_list=['Teacup Temperature'])
ensemble = ModelEnsemble() # instantiate an ensemble
ensemble.model_structure = model # set the model on the ensemble
ensemble.parallel = False
nr_runs = 10
experiments, outcomes = ensemble.perform_experiments(nr_runs)
self.assertEqual(experiments.shape[0], nr_runs)
self.assertIn('TIME', outcomes.keys())
self.assertIn('Teacup Temperature', outcomes.keys())
def test_parallel_experiment(self):
"""
Test running an experiment in parallel
Returns
-------
"""
relative_path_to_file = '../models/Teacup.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
model = PysdModel(mdl_file=mdl_file)
model.uncertainties = [RealParameter('Room Temperature', 33, 120)]
model.outcomes = [TimeSeriesOutcome('Teacup Temperature')]
ensemble = ModelEnsemble() # instantiate an ensemble
ensemble.model_structures = model # set the model on the ensemble
ensemble.parallel = True
ensemble.perform_experiments(5)
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)
"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)
except KeyError:
ema_logging.warning("key 'file' not found in policy")
super(FluModel, self).model_init(policy, kwargs)
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.INFO)
model = FluModel(r'./models/flu', "flucase")
ensemble = ModelEnsemble()
ensemble.model_structure = model
#add policies
policies = [{'name': 'no policy',
'file': r'\FLUvensimV1basecase.vpm'},
{'name': 'static policy',
'file': r'\FLUvensimV1static.vpm'},
{'name': 'adaptive policy',
'file': r'\FLUvensimV1dynamic.vpm'}
]
ensemble.policies = policies
#turn on parallel processing
ensemble.parallel = True
# run 1000 experiments
nr_runs = 1000
results = ensemble.perform_experiments(nr_runs)
# save the results
# save_results(results, r'./data/{} flu cases.tar.gz'.format(nr_runs))
"aaa"),
ParameterUncertainty((0.45,0.55),
"tH"),
ParameterUncertainty((0.1,0.3),
"kk")]
#specification of the outcomes
outcomes = [Outcome("B4:B1076", time=True), #we can refer to a range in the normal way
Outcome("P_t", time=True)] # we can also use named range
#name of the sheet
sheet = "Sheet1"
#relative path to the Excel file
workbook = r'\excel example.xlsx'
if __name__ == "__main__":
ema_logging.log_to_stderr(level=ema_logging.INFO)
model = ExcelModel(r"./models/excelModel", "predatorPrey")
ensemble = ModelEnsemble()
ensemble.model_structure = model
ensemble.parallel = True #turn on parallel computing
#run 100 experiments
nr_experiments = 100
results = ensemble.perform_experiments(nr_experiments)
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)
