Fix trajectory sampling to handle active dims

tests/unit/models/gpflow/test_sampler.py

@@ -18,6 +18,7 @@
 from typing import Any, Callable, List, Tuple, Type
 from unittest.mock import MagicMock
 
+import gpflow
 import numpy.testing as npt
 import pytest
 import tensorflow as tf
@@ -30,6 +31,7 @@
     GaussianProcess,
     QuadraticMeanAndRBFKernel,
     QuadraticMeanAndRBFKernelWithSamplers,
+    gpr_model,
     quadratic_mean_rbf_kernel_model,
     rbf,
     svgp_model,
@@ -39,6 +41,7 @@
 from trieste.models.gpflow import (
     BatchReparametrizationSampler,
     DecoupledTrajectorySampler,
+    GaussianProcessRegression,
     IndependentReparametrizationSampler,
     RandomFourierFeatureTrajectorySampler,
     SparseVariational,
@@ -602,6 +605,56 @@ def test_rff_trajectory_update_trajectory_updates_and_doesnt_retrace(
     assert trajectory.__call__._get_tracing_count() == 1  # type: ignore
 
 
+@pytest.mark.parametrize(
+    "sampler_type", [RandomFourierFeatureTrajectorySampler, DecoupledTrajectorySampler]
+)
+@pytest.mark.parametrize(
+    "num_dimensions, active_dims",
+    [
+        (2, [0]),
+        (2, [1]),
+        (5, [1, 4]),
+        (5, [3, 2, 0]),
+    ],
+)
+@random_seed
+def test_trajectory_sampler_respects_active_dims(
+    sampler_type: Type[RandomFourierFeatureTrajectorySampler],
+    num_dimensions: int,
+    active_dims: List[int],
+) -> None:
+    # Test that the trajectory sampler respects the active_dims setting in a GPflow model.
+    num_points = 10
+    query_points = tf.random.uniform((num_points, num_dimensions), dtype=tf.float64)
+    dataset = Dataset(query_points, quadratic(query_points))
+
+    model = GaussianProcessRegression(gpr_model(dataset.query_points, dataset.observations))
+    model.model.kernel = gpflow.kernels.Matern52(active_dims=active_dims)
+
+    num_active_dims = len(active_dims)
+    active_dims_mask = tf.scatter_nd(
+        tf.expand_dims(active_dims, -1), [True] * num_active_dims, (num_dimensions,)
+    )
+    x_rnd = tf.random.uniform((num_points, num_dimensions), dtype=tf.float64)
+    x_fix = tf.constant(0.5, shape=(num_points, num_dimensions), dtype=tf.float64)
+    # We vary values only on the irrelevant dimensions.
+    x_test = tf.where(active_dims_mask, x_fix, x_rnd)
+
+    batch_size = 2
+    x_test_with_batching = tf.expand_dims(x_test, -2)
+    x_test_with_batching = tf.tile(x_test_with_batching, [1, batch_size, 1])  # [N, B, D]
+    trajectory_sampler = sampler_type(model)
+    trajectory = trajectory_sampler.get_trajectory()
+
+    model_eval = trajectory(x_test_with_batching)
+    assert model_eval.shape == (num_points, batch_size, 1)
+    # The output should be constant since data only varies on irrelevant dimensions.
+    npt.assert_array_almost_equal(
+        tf.math.reduce_std(model_eval, axis=0),
+        tf.constant(0.0, shape=(batch_size, 1), dtype=tf.float64),
+    )
+
+
 @pytest.mark.parametrize("num_features", [0, -2])
 def test_decoupled_trajectory_sampler_raises_for_invalid_number_of_features(
     num_features: int,

trieste/models/gpflow/sampler.py

@@ -328,9 +328,9 @@ def _get_kernel_function(kernel: Kernel) -> Callable[[TensorType, TensorType], t
     # Select between a multioutput kernel and a single-output kernel.
     def K(X: TensorType, X2: Optional[TensorType] = None) -> tf.Tensor:
         if _is_multioutput_kernel(kernel):
-            return kernel.K(X, X2, full_output_cov=False)  # [L, M, M]
+            return kernel(X, X2, full_cov=True, full_output_cov=False)  # [L, M, M]
         else:
-            return tf.expand_dims(kernel.K(X, X2), axis=0)  # [1, M, M]
+            return tf.expand_dims(kernel(X, X2), axis=0)  # [1, M, M]
 
     return K
 
@@ -781,6 +781,7 @@ def __init__(
             dummy_X = model.get_inducing_variables()[0][0:1, :]
         else:
             dummy_X = model.get_internal_data().query_points[0:1, :]
+        dummy_X = self.kernel.slice(dummy_X, None)[0]  # Keep only the active dims from the kernel.
 
         # Always build the weights and biases. This is important for saving the trajectory (using
         # tf.saved_model.save) before it has been used.
@@ -793,6 +794,13 @@ def resample(self) -> None:
         self.b.assign(self._bias_init(tf.shape(self.b), dtype=self._dtype))
         self.W.assign(self._weights_init(tf.shape(self.W), dtype=self._dtype))
 
+    def call(self, x: TensorType) -> TensorType:  # [N, D] -> [N, F] or [L, N, F]
+        """
+        Evaluate the basis functions at ``x``
+        """
+        x = self.kernel.slice(x, None)[0]  # Keep only the active dims from the kernel.
+        return super().call(x)  # [N, F] or [L, N, F]
+
 
 class ResampleableDecoupledFeatureFunctions(ResampleableRandomFourierFeatureFunctions):
     """