def test_to_robust_problem(self, mocked_platypus):
mocked_model = Model('test', function=mock.Mock())
mocked_model.levers = [
RealParameter('a', 0, 1),
RealParameter('b', 0, 1)
]
mocked_model.uncertainties = [
RealParameter('c', 0, 1),
RealParameter('d', 0, 1)
]
mocked_model.outcomes = [ScalarOutcome('x'), ScalarOutcome('y')]
scenarios = 5
robustness_functions = [
ScalarOutcome('mean x', variable_name='x', function=mock.Mock()),
ScalarOutcome('mean y', variable_name='y', function=mock.Mock())
]
problem = to_robust_problem(mocked_model, scenarios,
robustness_functions)
self.assertEqual('robust', problem.searchover)
for entry in problem.parameters:
self.assertIn(entry.name, mocked_model.levers.keys())
self.assertEqual(['a', 'b'], problem.parameter_names)
self.assertEqual(['mean x', 'mean y'], problem.outcome_names)
def test_to_problem(self, mocked_platypus):
mocked_model = Model('test', function=mock.Mock())
mocked_model.levers = [
RealParameter('a', 0, 1),
RealParameter('b', 0, 1)
]
mocked_model.uncertainties = [
RealParameter('c', 0, 1),
RealParameter('d', 0, 1)
]
mocked_model.outcomes = [ScalarOutcome('x'), ScalarOutcome('y')]
mocked_model.constraints = []
searchover = 'levers'
problem = to_problem(mocked_model, searchover)
self.assertEqual(searchover, problem.searchover)
for entry in problem.parameters:
self.assertIn(entry.name, mocked_model.levers.keys())
self.assertIn(entry, list(mocked_model.levers))
for entry in problem.outcome_names:
self.assertIn(entry.name, mocked_model.outcomes.keys())
searchover = 'uncertainties'
problem = to_problem(mocked_model, searchover)
self.assertEqual(searchover, problem.searchover)
for entry in problem.parameters:
self.assertIn(entry.name, mocked_model.uncertainties.keys())
self.assertIn(entry, list(mocked_model.uncertainties))
for entry in problem.outcome_names:
self.assertIn(entry.name, mocked_model.outcomes.keys())
def test_init(self):
# let's add some uncertainties to this
uncs = [RealParameter("a", 0, 1), RealParameter("b", 0, 1)]
outcomes = [
ScalarOutcome("scalar"),
ArrayOutcome("array", shape=(10, )),
TimeSeriesOutcome("timeseries")
]
callback = DefaultCallback(uncs, [], outcomes, nr_experiments=100)
self.assertEqual(callback.i, 0)
self.assertEqual(callback.nr_experiments, 100)
self.assertEqual(callback.cases.shape[0], 100)
self.assertEqual(callback.outcomes, [o.name for o in outcomes])
names = callback.cases.columns.values.tolist()
names = set(names)
self.assertEqual(names, {'a', 'b', 'policy', 'model', 'scenario'})
self.assertNotIn('scalar', callback.results)
self.assertNotIn('timeseries', callback.results)
self.assertIn('array', callback.results)
a = np.all(np.isnan(callback.results['array']))
self.assertTrue(a)
# with levers
levers = [RealParameter('c', 0, 10)]
callback = DefaultCallback(uncs, levers, outcomes, nr_experiments=100)
self.assertEqual(callback.i, 0)
self.assertEqual(callback.nr_experiments, 100)
self.assertEqual(callback.cases.shape[0], 100)
self.assertEqual(callback.outcomes, [o.name for o in outcomes])
names = callback.cases.columns.values.tolist()
names = set(names)
self.assertEqual(names, {'a', 'b', 'c', 'policy', 'model', 'scenario'})
self.assertNotIn('scalar', callback.results)
self.assertNotIn('timeseries', callback.results)
self.assertIn('array', callback.results)
a = np.all(np.isnan(callback.results['array']))
self.assertTrue(a)
RealParameter('delta', 0.93, 0.99)
]
# lake_model.levers = [RealParameter("c1", -2, 2),
# RealParameter("c2", -2, 2),
# RealParameter("r1", 0, 2), #[0,2]
# RealParameter("r2", 0, 2), #
# RealParameter("w1", 0, 1)
# ]
lake_model.levers = [
RealParameter(str(i), 0, 0.1) for i in range(lake_model.time_horizon)
]
lake_model.outcomes = [
ScalarOutcome('max_P', kind=ScalarOutcome.MINIMIZE),
ScalarOutcome('utility', kind=ScalarOutcome.MAXIMIZE),
ScalarOutcome('inertia', kind=ScalarOutcome.MINIMIZE),
ScalarOutcome('reliability', kind=ScalarOutcome.MAXIMIZE)
]
lake_model.constants = [
Constant('alpha', 0.41),
Constant('nsamples', 100),
Constant('timehorizon', lake_model.time_horizon)
]
scenarios = ['Ref', 77, 96, 130, 181]
random_scenarios = [81, 289, 391, 257]
policies = []