def testCalibratedRtlMonotonicClassifierTraining(self):
# Construct the following training/testing pair.
#
# Training: (x, y)
# ([0., 0.], 0.0)
# ([0., 1.], 1.0)
# ([1., 0.], 1.0)
# ([1., 1.], 0.0)
#
# Test: (x, y)
# ([0., 0.], 0.0)
# ([0., 1.], 1.0)
# ([1., 0.], 1.0)
# ([1., 1.], 1.0)
#
# Note that training example has a noisy sample, ([1., 1.], 0.0), and test
# examples are generated by the logical-OR function. Therefore by enforcing
# increasing monotonicity to all features, we should be able to work well
# in the test examples.
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]])
test_y = np.array([[False], [True], [True], [True]])
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=test_y, 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.CalibratedRtlHParams(
num_keypoints=2, num_lattices=3, lattice_rank=2)
# Monotonic calibrated lattice.
hparams.set_param('monotonicity', +1)
hparams.set_param('learning_rate', 0.1)
hparams.set_param('interpolation_type', 'hypercube')
estimator = calibrated_rtl.calibrated_rtl_classifier(
feature_columns=feature_columns,
hparams=hparams,
keypoints_initializers_fn=init_fn)
estimator.train(input_fn=train_input_fn)
results = estimator.evaluate(input_fn=test_input_fn)
# We should expect 1.0 accuracy.
self.assertGreater(results['accuracy'], 0.999)
def setUp(self):
self.hparams = tfl_hparams.CalibratedRtlHParams(feature_names=['x'])
self.hparams.set_param('lattice_size', 2)
self.hparams.set_param('calibrator_output_min', 0)
self.hparams.set_param('calibrator_output_max', 1)
self.hparams.set_param('calibration_bound', True)
self.hparams.set_param('lattice_rank', 2)
self.hparams.set_param('num_lattices', 10)
self.empty_estimator = calibrated_rtl.calibrated_rtl_classifier(
hparams=self.hparams)
def _CalibratedRtlClassifier(self,
feature_columns,
num_lattices=1,
lattice_rank=1,
**hparams_args):
def init_fn():
return keypoints_initialization.uniform_keypoints_for_signal(
_NUM_KEYPOINTS, -1., 1., 0., 1.)
hparams = tfl_hparams.CalibratedRtlHParams(
num_keypoints=_NUM_KEYPOINTS,
num_lattices=num_lattices,
lattice_rank=lattice_rank,
**hparams_args)
# Turn off monotonic calibrator.
hparams.set_param('calibration_monotonic', None)
hparams.set_param('learning_rate', 0.1)
return calibrated_rtl.calibrated_rtl_classifier(
feature_columns=feature_columns,
hparams=hparams,
keypoints_initializers_fn=init_fn)