Make log abstract

secondmind-labs · Nov 27, 2023 · 967016d · 967016d
1 parent 1abeaa4
commit 967016d
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Showing 7 changed files with 27 additions and 2 deletions.
diff --git a/tests/unit/models/gpflow/test_interface.py b/tests/unit/models/gpflow/test_interface.py
@@ -14,7 +14,7 @@
 
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, Optional
 
 import gpflow
 import numpy.testing as npt
@@ -37,6 +37,9 @@ def optimize(self, dataset: Dataset) -> None:
     def update(self, dataset: Dataset) -> None:
         return
 
+    def log(self, dataset: Optional[Dataset] = None) -> None:
+        return
+
 
 class _QuadraticGPModel(GPModel):
     def __init__(self) -> None:

diff --git a/tests/unit/models/gpflux/test_interface.py b/tests/unit/models/gpflux/test_interface.py
@@ -14,6 +14,8 @@
 
 from __future__ import annotations
 
+from typing import Optional
+
 import gpflow
 import numpy.testing as npt
 import pytest
@@ -70,6 +72,9 @@ def sample(self, query_points: TensorType, num_samples: int) -> TensorType:
     def update(self, dataset: Dataset) -> None:
         return
 
+    def log(self, dataset: Optional[Dataset] = None) -> None:
+        return
+
 
 class _QuadraticGPModel(DeepGP):
     def __init__(

diff --git a/tests/unit/test_bayesian_optimizer.py b/tests/unit/test_bayesian_optimizer.py
@@ -491,6 +491,9 @@ def update(self, dataset: Dataset) -> NoReturn:
         def optimize(self, dataset: Dataset) -> NoReturn:
             assert False
 
+        def log(self, dataset: Optional[Dataset] = None) -> None:
+            return
+
     class _UnusableRule(AcquisitionRule[NoReturn, Box, ProbabilisticModel]):
         def acquire(
             self,

diff --git a/tests/util/models/gpflow/models.py b/tests/util/models/gpflow/models.py
@@ -121,6 +121,9 @@ def covariance_between_points(
         ]
         return tf.concat(covs, axis=-3)
 
+    def log(self, dataset: Optional[Dataset] = None) -> None:
+        return
+
 
 class GaussianProcessWithoutNoise(GaussianMarginal, SupportsPredictJoint, HasReparamSampler):
     """A (static) Gaussian process over a vector random variable with independent reparam sampler

diff --git a/trieste/models/gpflow/models.py b/trieste/models/gpflow/models.py
@@ -1659,6 +1659,9 @@ def covariance_with_top_fidelity(self, query_points: TensorType) -> TensorType:
 
         return f_var
 
+    def log(self, dataset: Optional[Dataset] = None) -> None:
+        return
+
 
 class MultifidelityNonlinearAutoregressive(
     TrainableProbabilisticModel, SupportsPredictY, SupportsCovarianceWithTopFidelity
@@ -2033,3 +2036,6 @@ def covariance_with_top_fidelity(self, query_points: TensorType) -> TensorType:
         cov = tfp.stats.covariance(signal_sample, max_fidelity_sample)[:, :, 0]
 
         return cov
+
+    def log(self, dataset: Optional[Dataset] = None) -> None:
+        return
diff --git a/trieste/models/interfaces.py b/trieste/models/interfaces.py
@@ -84,13 +84,14 @@ def sample(self, query_points: TensorType, num_samples: int) -> TensorType:
         """
         raise NotImplementedError
 
+    @abstractmethod
     def log(self, dataset: Optional[Dataset] = None) -> None:
         """
         Log model-specific information at a given optimization step.
 
         :param dataset: Optional data that can be used to log additional data-based model summaries.
         """
-        return
+        raise NotImplementedError
 
 
 @runtime_checkable

diff --git a/trieste/models/keras/interface.py b/trieste/models/keras/interface.py
@@ -22,6 +22,7 @@
 from check_shapes import inherit_check_shapes
 from typing_extensions import Protocol, runtime_checkable
 
+from ...data import Dataset
 from ...types import TensorType
 from ..interfaces import ProbabilisticModel
 from ..optimizer import KerasOptimizer
@@ -76,6 +77,9 @@ def sample(self, query_points: TensorType, num_samples: int) -> TensorType:
             """
         )
 
+    def log(self, dataset: Optional[Dataset] = None) -> None:
+        return
+
 
 @runtime_checkable
 class DeepEnsembleModel(ProbabilisticModel, Protocol):