def test_ridge(data):
model = MetaModel().readCSV(data)
model.setResponse("Electricity:Facility [J](Hourly)")
model.addPredictor("Environment:Site Outdoor Air Drybulb Temperature [C](Hourly)")
model.addPredictor("Environment:Site Outdoor Air Humidity Ratio [kgWater/kgDryAir](Hourly)")
model.bayesRidge().getScores()
print(model.scores)
def test_persist(data):
model = MetaModel().readCSV(data)
model.setResponse("Electricity:Facility [J](Hourly)")
model.addPredictor("Environment:Site Outdoor Air Drybulb Temperature [C](Hourly)")
model.addPredictor("Environment:Site Outdoor Air Humidity Ratio [kgWater/kgDryAir](Hourly)")
model.fitSVR(cost=10, epsilon=0.2).getScores()
model.persistMeta('testPickle.p')
newModel = pickle.load(open('testPickle.p', 'rb'))
print(newModel.scores)
def test_all(data):
model = MetaModel().readCSV(data)
model.setResponse("Electricity:Facility [J](Hourly)")
model.addPredictor("Environment:Site Outdoor Air Drybulb Temperature [C](Hourly)")
model.addPredictor("Environment:Site Outdoor Air Humidity Ratio [kgWater/kgDryAir](Hourly)")
model.addPredictor("BASEMENT:Zone Thermostat Heating Setpoint Temperature [C](Hourly)")
model.addPredictor("CORE_MID:Zone Thermostat Heating Setpoint Temperature [C](Hourly)")
model.addPredictor("CORE_TOP:Zone Thermostat Cooling Setpoint Temperature [C](Hourly)")
model.bayesRidge().fitSVR(cost=10, epsilon=0.2).trees(numberOfTrees=300).getScores()
print(model.scores)
model.fit(model.SVR)
model.persistMeta('../../data/demoMeta.p')
def test_setResponse():
model = MetaModel()
model.setResponse("energy")
print (model.response)