def test_constraints(building, parameters):
"""Testing for expected output with certain constraints, also acts as a test for using NSGAII"""
objectives = ['Electricity:Facility', 'Gas:Facility']
problem = EPProblem(inputs=parameters,
outputs=objectives,
constraints=['CO2:Facility'], constraint_bounds=['>=750'])
evaluator = EvaluatorEP(problem, building)
results = NSGAII(evaluator, evaluations=10, population_size=2)
value = results.iloc[0]['CO2:Facility'] + results.iloc[0]['violation']
assert value >= 750, f'Constraint did not effect output, value should be above 750 but was: {value}'
#Check to make sure the output changes with different constraints
problem = EPProblem(inputs=parameters,
outputs=objectives,
constraints=['CO2:Facility'], constraint_bounds=['<=750'])
evaluator = EvaluatorEP(problem, building)
results = NSGAII(evaluator, evaluations=10, population_size=2)
value = results.iloc[0]['CO2:Facility'] - results.iloc[0]['violation']
assert value <= 750, f'Constraint did not effect output, value should be below 750 but was: {value}'
#change this to 0 to see stdout and stderr
assert 1
def problem():
parameters = expand_plist(
{'Mass NonRes Wall Insulation':
{'Thickness': (0.01, 0.99)},
'NonRes Fixed Assembly Window':
{'U-Factor':(0.1,5),
'Solar Heat Gain Coefficient':(0.01,0.99)}
})
return EPProblem(parameters, ['Electricity:Facility'])
def problem():
problem = EPProblem([
Parameter(
FieldSelector(object_name='Mass NonRes Wall Insulation',
field_name='Thickness'),
RangeParameter(min_val=0.01, max_val=0.99)),
Parameter(
FieldSelector(class_name='Construction',
object_name='ext-slab',
field_name='Outside Layer'),
CategoryParameter(options=('HW CONCRETE', 'Invalid Material')))
])
return problem
def problem():
parameters = [
Parameter(
FieldSelector(object_name='Mass NonRes Wall Insulation',
field_name='Thickness'))
]
objectives = ['Electricity:Facility',
'Gas:Facility'] # the default is just 'Electricity:Facility'
problem = EPProblem(
parameters,
objectives) #EPP Problem automatically converts these to MeterReaders
return problem
def test_init(building, parameters):
"""Testing the initialization and functionality of an adaptive surrogate model"""
problem = EPProblem(parameters)
evaluator = EvaluatorEP(problem, building)
inputs = sampling.dist_sampler(sampling.seeded_sampler, problem, 10)
inputs = sampling.add_extremes(inputs, problem)
#using the custom class for our own model training
k = KirgingEval(reference=evaluator)
k.do_infill(inputs)
k.model.train()
k.infill(5)
#change this to 0 to see stdout and stderr
assert 1
def test_objectives(building, parameters):
"""Testing custom functions and basic objective creation"""
def variance(result):
return result.data['Value'].var()
objectives = [MeterReader('Electricity:Facility', name='Electricity Usage'),
MeterReader('Electricity:Facility',func=variance, name='Electricity Variance')]
problem = EPProblem(inputs=parameters, outputs=objectives)
evaluator = EvaluatorEP(problem, building)
samples = sampling.dist_sampler(sampling.seeded_sampler, problem, 10)
results = evaluator.df_apply(samples, keep_input=True)
value = results.iloc[0]['Electricity Variance']
assert np.isclose(value, 829057663033101.1), f'Unexpected value when using custom function:{value}'
#change this to 0 to see stdout and stderr
assert 1
def problem(parameters):
objectives = ['Electricity:Facility']
problem = EPProblem(parameters, objectives)
return problem
def problem(parameters):
objectives = ['Electricity:Facility', 'Gas:Facility']
return EPProblem(parameters, objectives)