def test_random_feature_posterior_approximation(self):
"""Tests random feature GP's ability in approximating exact GP posterior."""
# Set momentum = 0.5 so posterior precision matrix is 0.5 * (I + K).
gp_cov_momentum = 0.5
gp_cov_ridge_penalty = 1.
num_inducing = 1024
rfgp_model = gaussian_process.RandomFeatureGaussianProcess(
units=1,
num_inducing=num_inducing,
normalize_input=False,
gp_kernel_type='gaussian',
gp_cov_momentum=gp_cov_momentum,
gp_cov_ridge_penalty=gp_cov_ridge_penalty)
# Computes posterior covariance on test data.
_, _ = rfgp_model(self.x_tr, training=True)
_, gp_cov_ts = rfgp_model(self.x_ts, training=False)
# Scale up covariance estimate since prec matrix is down-scaled by momentum.
post_kernel_computed = gp_cov_ts * gp_cov_momentum
post_kernel_expected = _compute_posterior_kernel(
self.x_tr, self.x_ts, self.rbf_kern_func, gp_cov_ridge_penalty)
np.testing.assert_allclose(post_kernel_computed, post_kernel_expected,
**self.cov_tolerance)
def test_no_matrix_update_during_test(self):
"""Tests if the precision matrix is not updated during testing."""
rfgp_model = gaussian_process.RandomFeatureGaussianProcess(units=1)
# Training.
_, gp_covmat_null = rfgp_model(self.x_tr, training=True)
precision_mat_before_test = rfgp_model._gp_cov_layer.precision_matrix
# Testing.
_ = rfgp_model(self.x_ts, training=False)
precision_mat_after_test = rfgp_model._gp_cov_layer.precision_matrix
self.assertAllClose(
gp_covmat_null, tf.eye(self.num_train_sample), atol=1e-4)
self.assertAllClose(
precision_mat_before_test, precision_mat_after_test, atol=1e-4)
def test_random_feature_prior_approximation(self):
"""Tests random feature GP's ability in approximating exact GP prior."""
num_inducing = 10240
rfgp_model = gaussian_process.RandomFeatureGaussianProcess(
units=1,
num_inducing=num_inducing,
normalize_input=False,
gp_kernel_type='gaussian',
return_random_features=True)
# Extract random features.
_, _, gp_feature = rfgp_model(self.x_tr, training=True)
gp_feature_np = gp_feature.numpy()
prior_kernel_computed = gp_feature_np.dot(gp_feature_np.T)
prior_kernel_expected = self.rbf_kern_func(self.x_tr, self.x_tr)
np.testing.assert_allclose(prior_kernel_computed,
prior_kernel_expected, **self.cov_tolerance)
def test_state_saving_and_loading(self):
"""Tests if the loaded model returns same results."""
input_data = np.random.random((1, 2))
rfgp_model = gaussian_process.RandomFeatureGaussianProcess(units=1)
inputs = tf.keras.Input((2, ), batch_size=1)
outputs = rfgp_model(inputs)
model = tf.keras.Model(inputs, outputs)
gp_output, gp_covmat = model.predict(input_data)
# Save and then load the model.
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir)
saved_model_dir = os.path.join(temp_dir, 'rfgp_model')
model.save(saved_model_dir)
new_model = tf.keras.models.load_model(saved_model_dir)
gp_output_new, gp_covmat_new = new_model.predict(input_data)
self.assertAllClose(gp_output, gp_output_new, atol=1e-4)
self.assertAllClose(gp_covmat, gp_covmat_new, atol=1e-4)