Remove encoder from FeatureDecompositionTrajectorySampler

trieste/models/gpflow/sampler.py

@@ -29,7 +29,6 @@
 from gpflux.math import compute_A_inv_b
 from typing_extensions import Protocol, TypeGuard, runtime_checkable
 
-from ...space import EncoderFunction
 from ...types import TensorType
 from ...utils import DEFAULTS, flatten_leading_dims
 from ..interfaces import (
@@ -44,7 +43,6 @@
     TrajectoryFunction,
     TrajectoryFunctionClass,
     TrajectorySampler,
-    get_encoder,
 )
 
 _IntTensorType = Union[tf.Tensor, int]
@@ -399,7 +397,6 @@ def get_trajectory(self) -> TrajectoryFunction:
             feature_functions=self._feature_functions,
             weight_sampler=weight_sampler,
             mean_function=self._mean_function,
-            encoder=get_encoder(self._model),
         )
 
     def update_trajectory(self, trajectory: TrajectoryFunction) -> TrajectoryFunction:
@@ -876,18 +873,15 @@ def __init__(
         feature_functions: Callable[[TensorType], TensorType],
         weight_sampler: Callable[[int], TensorType],
         mean_function: Callable[[TensorType], TensorType],
-        encoder: EncoderFunction | None = None,
     ):
         """
         :param feature_functions: Set of feature function.
         :param weight_sampler: New sampler that generates feature weight samples.
         :param mean_function: The underlying model's mean function.
-        :param encoder: Optional encoder with which to transform input points.
         """
         self._feature_functions = feature_functions
         self._mean_function = mean_function
         self._weight_sampler = weight_sampler
-        self._encoder = encoder
         self._initialized = tf.Variable(False)
 
         self._weights_sample = tf.Variable(  # dummy init to be updated before trajectory evaluation
@@ -902,9 +896,6 @@ def __init__(
     def __call__(self, inputs: TensorType) -> TensorType:  # [N, B, D] -> [N, B, L]
         """Call trajectory function."""
 
-        if self._encoder is not None:
-            inputs = self._encoder(inputs)
-
         if not self._initialized:  # work out desired batch size from input
             self._batch_size.assign(tf.shape(inputs)[-2])  # B
             self.resample()  # sample B feature weights

trieste/models/interfaces.py

@@ -970,10 +970,3 @@ def conditional_predict_y(
         return self.conditional_predict_y_encoded(
             self.encode(query_points), self.encode(additional_data)
         )
-
-
-def get_encoder(model: ProbabilisticModel) -> EncoderFunction | None:
-    """Helper function for getting an encoder from model (which may or may not have one)."""
-    if isinstance(model, EncodedProbabilisticModel):
-        return model.encoder
-    return None