class SurrogateModel(object):
def __init__(self, fitness, configuration, controller):
self.fitness = fitness
self.configuration = configuration
self.classifier = Classifier()
self.regressor = Regressor(controller)
def train(self, pop):
raise NotImplementedError('SurrogateModel is an abstract class, this '
'should not be called.')
def model_particles(self, particles):
MU, S2 = self.regressor.predict(particles)
return self.classifier.predict(particles), MU, S2
def add_training_instance(self, part, code, fitness):
pass
def __getstate__(self):
# Don't pickle fitness and configuration
d = dict(self.__dict__)
del d['fitness']
del d['configuration']
return d
class DummySurrogateModel(SurrogateModel):
## TODO - add dummy regressor/classifier
def __init__(self, configuration, controller, fitness):
super(DummySurrogateModel, self).__init__(configuration,
controller,
fitness)
self.regressor = Regressor(controller, configuration)
self.classifier = Classifier()
def get_regressor(self):
return self.regressor
def get_classifier(self):
return self.classifier
def predict(self, particles):
MU, S2 = self.regressor.predict(particles)
return self.classifier.predict(particles), MU, S2
def train(self, hypercube):
self.was_trained = True
return True
def model_particle(self, particle):
return 0, 0, 0
def contains_training_instance(self, part):
return False
def model_failed(self, part):
return False
def get_state_dictionary(self):
return {}
def set_state_dictionary(self, dict):
pass
def get_copy(self):
model_copy = DummySurrogateModel(self.configuration, self.controller)
return model_copy
