def test_determine_parameters(self):
function = mock.Mock()
model_a = Model("A", function)
model_a.uncertainties = [
RealParameter('a', 0, 1),
RealParameter('b', 0, 1),
]
function = mock.Mock()
model_b = Model("B", function)
model_b.uncertainties = [
RealParameter('b', 0, 1),
RealParameter('c', 0, 1),
]
models = [model_a, model_b]
parameters = determine_parameters(models, 'uncertainties', union=True)
for model in models:
for unc in model.uncertainties:
self.assertIn(unc.name, parameters.keys())
parameters = determine_parameters(models, 'uncertainties', union=False)
self.assertIn('b', parameters.keys())
self.assertNotIn('c', parameters.keys())
self.assertNotIn('a', parameters.keys())
def test_determine_parameters(self):
function = mock.Mock()
model_a = Model("A", function)
model_a.uncertainties = [RealParameter('a', 0, 1),
RealParameter('b', 0, 1),]
function = mock.Mock()
model_b = Model("B", function)
model_b.uncertainties = [RealParameter('b', 0, 1),
RealParameter('c', 0, 1),]
models = [model_a, model_b]
parameters = determine_parameters(models, 'uncertainties', union=True)
for model in models:
for unc in model.uncertainties:
self.assertIn(unc.name, parameters.keys())
parameters = determine_parameters(models, 'uncertainties', union=False)
self.assertIn('b', parameters.keys())
self.assertNotIn('c', parameters.keys())
self.assertNotIn('a', parameters.keys())
std_performance = np.std(outcome)
objs.append(float(mean_performance))
objs.append(float(std_performance))
return objs
if __name__ == '__main__':
ema_logging.log_to_stderr(ema_logging.INFO)
#instantiate the model
model = Model('lakeproblem', function=lake_problem)
#specify uncertainties
model.uncertainties = [RealParameter("b", 0.1, 0.45),
RealParameter("q", 2.0, 4.5),
RealParameter("mean", 0.01, 0.05),
RealParameter("stdev", 0.001, 0.005),
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, 0.05) for i in range(100)]
objs.append(float(std_performance))
return objs
if __name__ == '__main__':
ema_logging.log_to_stderr(ema_logging.INFO)
#instantiate the model
model = Model('lakeproblem', function=lake_problem)
#specify uncertainties
model.uncertainties = [
RealParameter("b", 0.1, 0.45),
RealParameter("q", 2.0, 4.5),
RealParameter("mean", 0.01, 0.05),
RealParameter("stdev", 0.001, 0.005),
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),