def test_gumbel_samples_are_minima() -> None: search_space = Box([0, 0], [1, 1]) 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) model = QuadraticMeanAndRBFKernel() gumbel_sampler = GumbelSampler(5, model) query_points = search_space.sample(100) query_points = tf.concat([dataset.query_points, query_points], 0) gumbel_samples = gumbel_sampler.sample(query_points) fmean, _ = model.predict(dataset.query_points) assert max(gumbel_samples) < min(fmean)
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)