def testEmptyNonMonotonicLatticeSizeExpectsError(self):
hparams = tfl_hparams.CalibratedEtlHParams(feature_names=['x'])
hparams.set_param('non_monotonic_num_lattices', 2)
hparams.set_param('non_monotonic_lattice_rank', 2)
with self.assertRaisesRegexp(
ValueError,
'Hyperparameter configuration cannot be used in the calibrated etl '
'estimator.'):
calibrated_etl.calibrated_etl_classifier(hparams=hparams)
def testWrongLatticeRegularization(self):
hparams = tfl_hparams.CalibratedEtlHParams(feature_names=['x'])
hparams.set_param('non_monotonic_num_lattices', 2)
hparams.set_param('non_monotonic_lattice_size', 2)
hparams.set_param('nno_monotonic_lattice_rank', 2)
hparams.set_feature_param('x', 'lattice_l1_reg', 0.1)
hparams.set_feature_param('x', 'lattice_l2_reg', 0.1)
hparams.set_feature_param('x', 'lattice_l1_torsion_reg', 0.1)
hparams.set_feature_param('x', 'lattice_l1_torsion_reg', 0.1)
with self.assertRaisesRegexp(
ValueError,
'Hyperparameter configuration cannot be used in the calibrated etl '
'estimator.'):
calibrated_etl.calibrated_etl_classifier(hparams=hparams)
def _CalibratedEtlClassifier(self, feature_columns, **hparams_args):
def init_fn():
return keypoints_initialization.uniform_keypoints_for_signal(
_NUM_KEYPOINTS, -1., 1., 0., 1.)
hparams = tfl_hparams.CalibratedEtlHParams(
num_keypoints=_NUM_KEYPOINTS,
monotonic_num_lattices=1,
monotonic_lattice_rank=1,
monotonic_lattice_size=2,
non_monotonic_num_lattices=1,
non_monotonic_lattice_rank=1,
non_monotonic_lattice_size=2,
**hparams_args)
# Turn off monotonic calibrator.
hparams.set_param('calibration_monotonic', None)
hparams.set_param('learning_rate', 0.1)
return calibrated_etl.calibrated_etl_classifier(
feature_columns=feature_columns,
hparams=hparams,
keypoints_initializers_fn=init_fn)
def testCalibratedEtlMonotonicClassifierTraining(self):
# Construct the following training pair.
#
# Training: (x, y)
# ([0., 0.], 0.0)
# ([0., 1.], 1.0)
# ([1., 0.], 1.0)
# ([1., 1.], 0.0)
#
# which is not a monotonic function. Then check the forcing monotonicity
# resulted in the following monotonicity or not.
# f(0, 0) <= f(0, 1), f(0, 0) <= f(1, 0), f(0, 1) <= f(1, 1),
# f(1, 0) < = f(1, 1).
x0 = np.array([0.0, 0.0, 1.0, 1.0])
x1 = np.array([0.0, 1.0, 0.0, 1.0])
x_samples = {'x0': x0, 'x1': x1}
training_y = np.array([[False], [True], [True], [False]])
train_input_fn = numpy_io.numpy_input_fn(x=x_samples,
y=training_y,
batch_size=4,
num_epochs=1000,
shuffle=False)
test_input_fn = numpy_io.numpy_input_fn(x=x_samples,
y=None,
shuffle=False)
# Define monotonic lattice classifier.
feature_columns = [
feature_column_lib.numeric_column('x0'),
feature_column_lib.numeric_column('x1'),
]
def init_fn():
return keypoints_initialization.uniform_keypoints_for_signal(
2, 0., 1., 0., 1.)
hparams = tfl_hparams.CalibratedEtlHParams(num_keypoints=2,
monotonic_num_lattices=2,
monotonic_lattice_rank=2,
monotonic_lattice_size=2)
hparams.set_param('calibration_monotonic', +1)
hparams.set_param('lattice_monotonic', True)
hparams.set_param('learning_rate', 0.1)
estimator = calibrated_etl.calibrated_etl_classifier(
feature_columns=feature_columns,
hparams=hparams,
keypoints_initializers_fn=init_fn)
estimator.train(input_fn=train_input_fn)
predictions = [
results['logits'][0]
for results in estimator.predict(input_fn=test_input_fn)
]
self.assertEqual(len(predictions), 4)
# Check monotonicity. Note that projection has its own precision, so we
# add a small number.
self.assertLess(predictions[0], predictions[1] + 1e-6)
self.assertLess(predictions[0], predictions[2] + 1e-6)
self.assertLess(predictions[1], predictions[3] + 1e-6)
self.assertLess(predictions[2], predictions[3] + 1e-6)
