def test_multiple_models(self):
"""
Test running running with two different pysd models
Returns
-------
"""
relative_path_to_file = '../models/Sales_Agent_Market_Building_Dynamics.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
market_model = PysdModel(mdl_file=mdl_file)
market_model.uncertainties = [RealParameter('Startup Subsidy',0, 3),
RealParameter('Startup Subsidy Length', 0, 10)]
market_model.outcomes = [TimeSeriesOutcome('Still Employed')]
relative_path_to_file = '../models/Sales_Agent_Motivation_Dynamics.mdl'
directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
mdl_file = os.path.join(directory, relative_path_to_file)
motivation_model = PysdModel(mdl_file=mdl_file)
motivation_model.uncertainties = [RealParameter('Startup Subsidy', 0, 3),
RealParameter('Startup Subsidy Length', 0, 10)]
motivation_model.outcomes =[TimeSeriesOutcome('Still Employed')]
ensemble = ModelEnsemble() # instantiate an ensemble
ensemble.model_structures = [market_model, motivation_model] # set the model on the ensemble
ensemble.perform_experiments(5)
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_multiple_models(self):
"""
Test running running with two different pysd models
Returns
-------
"""
from ema_workbench.connectors import PySDConnector
market_model = PySDConnector('../models/Sales_Agent_Market_Building_Dynamics.mdl',
uncertainties_dict={'Startup Subsidy': (0, 3),
'Startup Subsidy Length': (0, 10)},
outcomes_list=['Still Employed'])
motivation_model = PySDConnector('../models/Sales_Agent_Market_Building_Dynamics.mdl',
uncertainties_dict={'Startup Subsidy': (0, 3),
'Startup Subsidy Length': (0, 10)},
outcomes_list=['Still Employed'])
ensemble = ModelEnsemble() # instantiate an ensemble
ensemble.model_structures = [market_model, motivation_model] # set the model on the ensemble
results = ensemble.perform_experiments(cases=20)
"effect of perceived adequacy on energy drain lookup", self, 0, 10),
LookupUncertainty('hearne2', [(-2, 2), (-1, 2), (0, 1.5), (0.1, 1.6), (0.5, 1.5), (0.1, 2)],
"effect of perceived adequacy of hours worked lookup", self, 0, 2.5),
LookupUncertainty('hearne2', [(-1, 1), (-1, 1), (0, 0.9), (0.1, 1), (0.5, 1.5), (1, 1.5)],
"effect of energy levels on hours worked lookup", self, 0, 1.5),
LookupUncertainty('hearne2', [(-1, 1), (-1, 1), (0, 0.9), (0.1, 1), (0.5, 1.5), (1, 1.5)],
"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_structures = model
#run policy with old cases
results = ensemble.perform_experiments(100)
lines(results, 'Energy Level', density=BOXPLOT)
plt.show()
RealParameter("delta", 0.93, 0.99)]
#specify outcomes
model.outcomes = [ScalarOutcome("max_P",),
ScalarOutcome("utility"),
ScalarOutcome("inertia"),
ScalarOutcome("reliability")]
# override some of the defaults of the model
model.constants = [Constant('alpha', 0.41),
Constant('nsamples', 150),]
# set levers, one for each time step
model.levers = [RealParameter(str(i), 0, 0.1) for i in range(100)]
ensemble = ModelEnsemble() #instantiate an ensemble
ensemble.model_structures = model #set the model on the ensemble
ensemble.parallel = True
ensemble.processes = 1
# generate some random policies by sampling over levers
policies, levers, n = samplers.sample_levers(ensemble.model_structures, 4,
sampler=samplers.MonteCarloSampler())
# policies is a generator, so let's exhaust the generator
policies = [policy for policy in policies]
# policy name defaults to a repr(dict), let's rename
for i, policy in enumerate(policies):
policy.name = str(i)
# let's set the policies on the ensemble
