def test_fixed_effect_lr_lbfgs_model_creation(self):
fe_model = ModelFactory.get_model(
base_training_params=setup_fake_base_training_params(training_stage=constants.FIXED_EFFECT,
model_type=constants.LOGISTIC_REGRESSION),
raw_model_params=self.model_params)
# Assert the type of model
self.assertIsInstance(fe_model, FixedEffectLRModelLBFGS)
def test_random_effect_custom_logistic_regression_model_creation(self):
re_model = ModelFactory.get_model(
base_training_params=setup_fake_base_training_params(
training_stage=constants.RANDOM_EFFECT,
model_type=constants.LOGISTIC_REGRESSION),
raw_model_params=self.model_params)
self.assertIsInstance(re_model, RandomEffectLRLBFGSModel)
def get_driver(base_training_params, raw_model_params):
"""
Create driver and associated dependencies, based on type. Only linear and DeText models are supported
for now
:param base_training_params: Parsed base training parameters common to all models. This could including
path to training data, validation data, metadata file path, learning rate etc.
:param raw_model_params: Raw model parameters, representing model-specific requirements. For example, a
CNN might expose filter_size as a parameter, a text-based model might expose the size it's word embedding matrix
as a parameter
:return: Fixed or Random effect driver
"""
driver = DriverFactory.drivers[base_training_params.stage]
model = ModelFactory.get_model(base_training_params, raw_model_params)
logger.info(f"Instantiating model {model} and driver {driver}")
return driver(base_training_params=base_training_params, model=model)
def get_driver(base_training_params, raw_model_params):
"""
Create driver and associated dependencies, based on type. Only linear, estimator-based models supported
for now
:param base_training_params: Parsed base training parameters common to all models. This could including
path to training data, validation data, metadata file path, learning rate etc.
:param raw_model_params: Raw model parameters, representing model-specific requirements. For example, a
CNN might expose filter_size as a parameter, a text-based model might expose the size it's word embedding matrix
as a parameter
:return: Fixed or Random effect driver
"""
driver_type = base_training_params[constants.STAGE]
model = ModelFactory.get_model(base_training_params, raw_model_params)
if driver_type == constants.FIXED_EFFECT:
logger.info("Instantiating fixed effect model and driver")
driver = FixedEffectDriver(base_training_params=base_training_params, model=model)
elif driver_type == constants.RANDOM_EFFECT:
logger.info("Instantiating random effect model and driver")
driver = RandomEffectDriver(base_training_params=base_training_params, model=model)
else:
raise Exception("Unknown training stage")
return driver