def test_rff_sampler_returns_same_posterior_from_each_calculation_method(
) -> None:
model = QuadraticMeanAndRBFKernel(
noise_variance=tf.constant(1.0, dtype=tf.float64))
model.kernel = (
gpflow.kernels.RBF()
) # need a gpflow kernel object for random feature decompositions
x_range = tf.linspace(0.0, 1.0, 5)
x_range = tf.cast(x_range, dtype=tf.float64)
xs = tf.reshape(
tf.stack(tf.meshgrid(x_range, x_range, indexing="ij"), axis=-1),
(-1, 2))
ys = quadratic(xs)
dataset = Dataset(xs, ys)
sampler = RandomFourierFeatureThompsonSampler(dataset, model, 100)
sampler.get_trajectory()
posterior_1 = sampler._prepare_theta_posterior_in_design_space()
posterior_2 = sampler._prepare_theta_posterior_in_gram_space()
npt.assert_allclose(posterior_1.loc, posterior_2.loc, rtol=0.02)
npt.assert_allclose(posterior_1.scale_tril,
posterior_2.scale_tril,
rtol=0.02)
def test_rff_sampler_does_pre_calc_during_first_trajectory_call() -> None:
model = QuadraticMeanAndRBFKernel(
noise_variance=tf.constant(1.0, dtype=tf.float64))
model.kernel = gpflow.kernels.RBF()
dataset = Dataset(tf.constant([[-2.0]], dtype=tf.float64),
tf.constant([[4.1]], dtype=tf.float64))
sampler = RandomFourierFeatureThompsonSampler(dataset, model, 100)
assert sampler._pre_calc is False
sampler.get_trajectory()
assert sampler._pre_calc is True
def test_rff_sampler_returns_trajectory_function_with_correct_shaped_output(
num_evals: int) -> None:
model = QuadraticMeanAndRBFKernel(
noise_variance=tf.constant(1.0, dtype=tf.float64))
model.kernel = (
gpflow.kernels.RBF()
) # need a gpflow kernel object for random feature decompositions
dataset = Dataset(tf.constant([[-2.0]], dtype=tf.float64),
tf.constant([[4.1]], dtype=tf.float64))
sampler = RandomFourierFeatureThompsonSampler(dataset, model, 100)
trajectory = sampler.get_trajectory()
xs = tf.linspace([-10.0], [10.0], num_evals)
tf.debugging.assert_shapes([(trajectory(xs), [num_evals, 1])])
def test_rff_sampler_returns_deterministic_trajectory() -> None:
model = QuadraticMeanAndRBFKernel(
noise_variance=tf.constant(1.0, dtype=tf.float64))
model.kernel = (
gpflow.kernels.RBF()
) # need a gpflow kernel object for random feature decompositions
x_range = tf.linspace(0.0, 1.0, 5)
x_range = tf.cast(x_range, dtype=tf.float64)
xs = tf.reshape(
tf.stack(tf.meshgrid(x_range, x_range, indexing="ij"), axis=-1),
(-1, 2))
ys = quadratic(xs)
dataset = Dataset(xs, ys)
sampler = RandomFourierFeatureThompsonSampler(dataset, model, 100)
trajectory = sampler.get_trajectory()
trajectory_eval_1 = trajectory(xs)
trajectory_eval_2 = trajectory(xs)
npt.assert_allclose(trajectory_eval_1, trajectory_eval_2)
