def testCalibratedLinearRegressor(self, feature_names, output_calibration,
use_bias, average_loss):
self._ResetAllBackends()
feature_columns = [
feature_column for feature_column in self.boston_feature_columns
if feature_column.name in feature_names
]
feature_configs = [
feature_config for feature_config in self.boston_feature_configs
if feature_config.name in feature_names
]
model_config = configs.CalibratedLinearConfig(
use_bias=use_bias,
regularizer_configs=[
configs.RegularizerConfig(name='output_calib_hessian', l2=1e-4),
],
output_calibration=output_calibration,
feature_configs=feature_configs)
estimator = estimators.CannedRegressor(
feature_columns=feature_columns,
model_config=model_config,
feature_analysis_input_fn=self._GetBostonTrainInputFn(num_epochs=1),
optimizer=tf.keras.optimizers.Adam(0.01))
estimator.train(input_fn=self._GetBostonTrainInputFn(num_epochs=200))
results = estimator.evaluate(input_fn=self._GetBostonTestInputFn())
logging.info('Calibrated linear regressor results:')
logging.info(results)
self.assertLess(results['average_loss'], average_loss)
def testCalibratedLatticeEnsembleRegressor(self, feature_names, lattices,
num_lattices, lattice_rank,
separate_calibrators,
output_calibration,
average_loss):
self._ResetAllBackends()
feature_columns = [
feature_column for feature_column in self.boston_feature_columns
if feature_column.name in feature_names
]
feature_configs = [
feature_config for feature_config in self.boston_feature_configs
if feature_config.name in feature_names
]
if lattices == 'rtl_layer':
# RTL Layer only supports monotonicity and bound constraints.
feature_configs = copy.deepcopy(feature_configs)
for feature_config in feature_configs:
feature_config.lattice_size = 2
feature_config.unimodality = 'none'
feature_config.reflects_trust_in = None
feature_config.dominates = None
feature_config.regularizer_configs = None
model_config = configs.CalibratedLatticeEnsembleConfig(
regularizer_configs=[
configs.RegularizerConfig(name='torsion', l2=1e-5),
configs.RegularizerConfig(name='output_calib_hessian',
l2=1e-5),
],
feature_configs=feature_configs,
lattices=lattices,
num_lattices=num_lattices,
lattice_rank=lattice_rank,
separate_calibrators=separate_calibrators,
output_calibration=output_calibration,
)
estimator = estimators.CannedRegressor(
feature_columns=feature_columns,
model_config=model_config,
feature_analysis_input_fn=self._GetBostonTrainInputFn(
num_epochs=1),
prefitting_input_fn=self._GetBostonTrainInputFn(num_epochs=50),
optimizer=tf.keras.optimizers.Adam(0.05),
prefitting_optimizer=tf.keras.optimizers.Adam(0.05))
estimator.train(input_fn=self._GetBostonTrainInputFn(num_epochs=200))
results = estimator.evaluate(input_fn=self._GetBostonTestInputFn())
logging.info('Calibrated lattice ensemble regressor results:')
logging.info(results)
self.assertLess(results['average_loss'], average_loss)
def testCalibratedLatticeEnsembleFix2dConstraintViolations(
self, feature_names, lattices, num_lattices, lattice_rank,
expected_lattices):
self._ResetAllBackends()
feature_columns = [
feature_column for feature_column in self.boston_feature_columns
if feature_column.name in feature_names
]
feature_configs = [
feature_config for feature_config in self.boston_feature_configs
if feature_config.name in feature_names
]
model_config = configs.CalibratedLatticeEnsembleConfig(
feature_configs=feature_configs,
lattices=lattices,
num_lattices=num_lattices,
lattice_rank=lattice_rank,
)
estimator = estimators.CannedRegressor(
feature_columns=feature_columns,
model_config=model_config,
feature_analysis_input_fn=self._GetBostonTrainInputFn(
num_epochs=1),
prefitting_input_fn=self._GetBostonTrainInputFn(num_epochs=50),
optimizer=tf.keras.optimizers.Adam(0.05),
prefitting_optimizer=tf.keras.optimizers.Adam(0.05))
estimator.train(input_fn=self._GetBostonTrainInputFn(num_epochs=200))
# Serving input fn is used to create saved models.
serving_input_fn = (
tf.estimator.export.build_parsing_serving_input_receiver_fn(
feature_spec=fc.make_parse_example_spec(feature_columns)))
saved_model_path = estimator.export_saved_model(
estimator.model_dir, serving_input_fn)
logging.info('Model exported to %s', saved_model_path)
model = estimators.get_model_graph(saved_model_path)
lattices = []
for node in model.nodes:
if isinstance(node, model_info.LatticeNode):
lattices.append([
input_node.input_node.name
for input_node in node.input_nodes
])
self.assertLen(lattices, len(expected_lattices))
for lattice, expected_lattice in zip(lattices, expected_lattices):
self.assertCountEqual(lattice, expected_lattice)
def testCalibratedLatticeEnsembleRegressor(self, feature_names, lattices,
num_lattices, lattice_rank,
separate_calibrators,
output_calibration,
average_loss):
self._ResetAllBackends()
feature_columns = [
feature_column for feature_column in self.boston_feature_columns
if feature_column.name in feature_names
]
feature_configs = [
feature_config for feature_config in self.boston_feature_configs
if feature_config.name in feature_names
]
model_config = configs.CalibratedLatticeEnsembleConfig(
regularizer_configs=[
configs.RegularizerConfig(name='torsion', l2=1e-5),
configs.RegularizerConfig(name='output_calib_hessian',
l2=1e-5),
],
feature_configs=feature_configs,
lattices=lattices,
num_lattices=num_lattices,
lattice_rank=lattice_rank,
separate_calibrators=separate_calibrators,
output_calibration=output_calibration,
)
estimator = estimators.CannedRegressor(
feature_columns=feature_columns,
model_config=model_config,
feature_analysis_input_fn=self._GetBostonTrainInputFn(
num_epochs=1),
prefitting_input_fn=self._GetBostonTrainInputFn(num_epochs=50),
optimizer=tf.keras.optimizers.Adam(0.05),
prefitting_optimizer=tf.keras.optimizers.Adam(0.05))
estimator.train(input_fn=self._GetBostonTrainInputFn(num_epochs=200))
results = estimator.evaluate(input_fn=self._GetBostonTestInputFn())
logging.info('Calibrated lattice ensemble regressor results:')
logging.info(results)
self.assertLess(results['average_loss'], average_loss)
