def test_rff_sampler_returns_correctly_shaped_samples(
sample_min_value: bool, sample_size: int
) -> None:
search_space = Box([0.0, 0.0], [1.0, 1.0])
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(
sample_size, model, dataset, num_features=100, sample_min_value=sample_min_value
)
query_points = search_space.sample(100)
thompson_samples = sampler.sample(query_points)
if sample_min_value:
tf.debugging.assert_shapes([(thompson_samples, [sample_size, 1])])
else:
tf.debugging.assert_shapes([(thompson_samples, [sample_size, 2])])
def test_rff_sampler_raises_for_invalid_number_of_features(
num_features: 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)
)
with pytest.raises(TF_DEBUGGING_ERROR_TYPES):
RandomFourierFeatureThompsonSampler(1, model, dataset, num_features=num_features)
def test_discrete_thompson_sampling_acquire_returns_correct_shape(
num_fourier_features: bool, num_query_points: int
) -> None:
search_space = Box(tf.constant([-2.2, -1.0]), tf.constant([1.3, 3.3]))
ts = DiscreteThompsonSampling(100, num_query_points, num_fourier_features=num_fourier_features)
dataset = Dataset(tf.zeros([1, 2], dtype=tf.float64), tf.zeros([1, 1], dtype=tf.float64))
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
query_points = ts.acquire_single(search_space, model, dataset=dataset)
npt.assert_array_equal(query_points.shape, tf.constant([num_query_points, 2]))
def test_rff_sampler_sample_raises_for_invalid_at_shape(
shape: ShapeLike,
) -> 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(1, model, dataset, num_features=100)
with pytest.raises(TF_DEBUGGING_ERROR_TYPES):
sampler.sample(tf.zeros(shape))
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(1, model, dataset, num_features=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(1, model, dataset, num_features=100)
trajectory = sampler.get_trajectory()
trajectory_eval_1 = trajectory(xs)
trajectory_eval_2 = trajectory(xs)
npt.assert_allclose(trajectory_eval_1, trajectory_eval_2)
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(1, model, dataset, num_features=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_thompson_samples_are_minima() -> None:
search_space = Box([0.0, 0.0], [1.0, 1.0])
model = QuadraticMeanAndRBFKernel(noise_variance=tf.constant(1e-5, 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(
1, model, dataset, num_features=100, sample_min_value=True
)
query_points = search_space.sample(100)
query_points = tf.concat([dataset.query_points, query_points], 0)
thompson_samples = sampler.sample(query_points)
fmean, _ = model.predict(dataset.query_points)
assert max(thompson_samples) < min(fmean)
