def _batcher_bs_100(
dataset: Dataset,
batch_size: int) -> Iterable[tuple[TensorType, TensorType]]:
ds = tf.data.Dataset.from_tensor_slices(dataset.astuple())
ds = ds.shuffle(100)
ds = ds.batch(batch_size)
ds = ds.repeat()
return iter(ds)
def build_stacked_independent_objectives_model(
data: Dataset) -> ModelStack:
gprs = []
for idx in range(2):
single_obj_data = Dataset(
data.query_points, tf.gather(data.observations, [idx], axis=1))
variance = tf.math.reduce_variance(single_obj_data.observations)
kernel = gpflow.kernels.Matern52(
variance, tf.constant([0.2, 0.2], tf.float64))
gpr = gpflow.models.GPR(single_obj_data.astuple(),
kernel,
noise_variance=1e-5)
gpflow.utilities.set_trainable(gpr.likelihood, False)
gprs.append((GaussianProcessRegression(gpr), 1))
return ModelStack(*gprs)
def _batcher_2(dataset: Dataset, batch_size: int):
return dataset.astuple()
def _assert_data(self, dataset: Dataset) -> None:
qp, obs = dataset.astuple()
expected_obs = data.observations[..., self._output_dims]
assert_datasets_allclose(dataset, Dataset(qp, expected_obs))
def _batcher_full_batch(dataset: Dataset,
batch_size: int) -> tuple[TensorType, TensorType]:
return dataset.astuple()
