search_space = Box([0, 0], [1, 1])
fig = plot_function_plotly(scaled_branin, search_space.lower, search_space.upper, grid_density=20)
fig.update_layout(height=400, width=400)
fig.show()
# %% [markdown]
# We begin our Bayesian active learning from a two-point initial design built from a space-filling Halton sequence.
# %%
import trieste
observer = trieste.objectives.utils.mk_observer(scaled_branin)
num_initial_points = 4
initial_query_points = search_space.sample_halton(num_initial_points)
initial_data = observer(initial_query_points)
# %% [markdown]
# ## Surrogate model
#
# Just like in sequential optimization, we fit a surrogate Gaussian process model to the initial data.
# %%
import gpflow
from trieste.models.gpflow.models import GaussianProcessRegression
def build_model(data):
variance = tf.math.reduce_variance(data.observations)
def test_box_haltom_sampling_returns_different_points_for_different_call() -> None:
box = Box(tf.zeros((3,)), tf.ones((3,)))
halton_samples_1 = box.sample_halton(num_samples=100)
halton_samples_2 = box.sample_halton(num_samples=100)
npt.assert_raises(AssertionError, npt.assert_allclose, halton_samples_1, halton_samples_2)
def test_box_halton_sampling_raises_for_invalid_sample_size(num_samples: int) -> None:
with pytest.raises(TF_DEBUGGING_ERROR_TYPES):
box = Box(tf.zeros((3,)), tf.ones((3,)))
box.sample_halton(num_samples)
def test_box_halton_sampling_returns_same_points_for_same_seed(seed: int) -> None:
box = Box(tf.zeros((3,)), tf.ones((3,)))
halton_samples_1 = box.sample_halton(num_samples=100, seed=seed)
halton_samples_2 = box.sample_halton(num_samples=100, seed=seed)
npt.assert_allclose(halton_samples_1, halton_samples_2)
def test_box_halton_sampling_returns_correct_shape(num_samples: int) -> None:
box = Box(tf.zeros((3,)), tf.ones((3,)))
halton_samples = box.sample_halton(num_samples)
_assert_correct_number_of_unique_constrained_samples(num_samples, box, halton_samples)
