GP Criterion

CHANGELOG.md

@@ -15,6 +15,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Gaussian process component factories
 - Support for GPyTorch objects (kernels, means, likelihood) as Gaussian process
   components, enabling full low-level customization
+- Configurable fitting criterion for Gaussian process hyperparameter optimization
 - Factories for all Gaussian process components
 - `CHEN`, `EDBO` and `EDBO_SMOOTHED` presets for `GaussianProcessSurrogate`
 - `TypeSelector` and `NameSelector` classes for parameter selection in kernel factories

baybe/surrogates/gaussian_process/components/__init__.py

@@ -1,5 +1,10 @@
 """Gaussian process surrogate components."""
 
+from baybe.surrogates.gaussian_process.components.fit_criterion import (
+    FitCriterion,
+    FitCriterionFactoryProtocol,
+    PlainFitCriterionFactory,
+)
 from baybe.surrogates.gaussian_process.components.kernel import (
     KernelFactoryProtocol,
     PlainKernelFactory,
@@ -15,6 +20,10 @@
 )
 
 __all__ = [
+    # Fit Criterion
+    "FitCriterion",
+    "FitCriterionFactoryProtocol",
+    "PlainFitCriterionFactory",
     # Kernel
     "KernelFactoryProtocol",
     "PlainKernelFactory",

baybe/surrogates/gaussian_process/components/fit_criterion.py

@@ -0,0 +1,46 @@
+"""Fitting criteria for the Gaussian process surrogate."""
+
+from __future__ import annotations
+
+from enum import Enum
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from gpytorch.likelihoods import Likelihood as GPyTorchLikelihood
+    from gpytorch.mlls import MarginalLogLikelihood
+    from gpytorch.models import GP as GPyTorchModel
+
+
+class FitCriterion(Enum):
+    """Available fitting criteria for GP hyperparameter optimization."""
+
+    MARGINAL_LOG_LIKELIHOOD = "MARGINAL_LOG_LIKELIHOOD"
+    """Exact marginal log-likelihood."""
+
+    LEAVE_ONE_OUT_PSEUDOLIKELIHOOD = "LEAVE_ONE_OUT_PSEUDOLIKELIHOOD"
+    """Leave-one-out cross-validation pseudo-likelihood."""
+
+    def to_gpytorch(
+        self, likelihood: GPyTorchLikelihood, model: GPyTorchModel
+    ) -> MarginalLogLikelihood:
+        """Create the corresponding GPyTorch MLL object."""
+        import gpytorch
+
+        mll_class = {
+            FitCriterion.MARGINAL_LOG_LIKELIHOOD: gpytorch.ExactMarginalLogLikelihood,
+            FitCriterion.LEAVE_ONE_OUT_PSEUDOLIKELIHOOD: gpytorch.mlls.LeaveOneOutPseudoLikelihood,  # noqa: E501
+        }[self]
+        return mll_class(likelihood, model)
+
+
+# Delayed import to avoid circular dependency
+from baybe.surrogates.gaussian_process.components.generic import (  # noqa: E402
+    GPComponentFactoryProtocol,
+    PlainGPComponentFactory,
+)
+
+FitCriterionFactoryProtocol = GPComponentFactoryProtocol[FitCriterion]
+"""A protocol defining the interface for fit criterion factories."""
+
+PlainFitCriterionFactory = PlainGPComponentFactory[FitCriterion]
+"""A trivial factory that returns a fixed fit criterion."""

baybe/surrogates/gaussian_process/components/generic.py

@@ -14,8 +14,9 @@
 from baybe.searchspace import SearchSpace
 from baybe.serialization.core import block_serialization_hook, converter
 from baybe.serialization.mixin import SerialMixin
+from baybe.surrogates.gaussian_process.components.fit_criterion import FitCriterion
 
-BayBEGPComponent: TypeAlias = Kernel
+BayBEGPComponent: TypeAlias = Kernel | FitCriterion
 
 if TYPE_CHECKING:
     from gpytorch.kernels import Kernel as GPyTorchKernel
@@ -44,15 +45,24 @@ class GPComponentType(Enum):
     LIKELIHOOD = "LIKELIHOOD"
     """Gaussian process likelihood."""
 
+    CRITERION = "CRITERION"
+    """Gaussian process fitting criterion."""
+
     def get_types(self) -> tuple[type, ...]:
         """Get the accepted BayBE and GPyTorch types for this component."""
-        types = []
+        types: list[type[GPComponent]] = []
 
         # Add BayBE type if applicable
         if self is GPComponentType.KERNEL:
             from baybe.kernels.base import Kernel
 
             types.append(Kernel)
+        elif self is GPComponentType.CRITERION:
+            from baybe.surrogates.gaussian_process.components.fit_criterion import (
+                FitCriterion,
+            )
+
+            types.append(FitCriterion)
 
         # Add GPyTorch type if available
         if sys.modules.get("gpytorch") is not None:
@@ -85,7 +95,7 @@ def _is_gpytorch_component_class(obj: Any, /) -> bool:
 
 def _validate_component(instance: Any, attribute: Attribute, value: Any) -> None:
     """Validate that an object is a BayBE or a GPyTorch GP component."""
-    if isinstance(value, Kernel) or _is_gpytorch_component_class(type(value)):
+    if isinstance(value, BayBEGPComponent) or _is_gpytorch_component_class(type(value)):
         return
 
     raise TypeError(

baybe/surrogates/gaussian_process/core.py

@@ -19,6 +19,10 @@
 from baybe.parameters.categorical import TaskParameter
 from baybe.searchspace.core import SearchSpace
 from baybe.surrogates.base import Surrogate
+from baybe.surrogates.gaussian_process.components.fit_criterion import (
+    FitCriterion,
+    FitCriterionFactoryProtocol,
+)
 from baybe.surrogates.gaussian_process.components.generic import (
     GPComponentType,
     to_component_factory,
@@ -35,6 +39,7 @@
     GaussianProcessPreset,
 )
 from baybe.surrogates.gaussian_process.presets.baybe import (
+    BayBEFitCriterionFactory,
     BayBEKernelFactory,
     BayBELikelihoodFactory,
     BayBEMeanFactory,
@@ -178,6 +183,21 @@ class GaussianProcessSurrogate(Surrogate):
         * :class:`gpytorch.likelihoods.Likelihood`
     """
 
+    criterion_factory: FitCriterionFactoryProtocol = field(
+        alias="criterion_or_factory",
+        factory=BayBEFitCriterionFactory,
+        converter=partial(  # type: ignore[misc]
+            to_component_factory, component_type=GPComponentType.CRITERION
+        ),
+        validator=is_callable(),
+    )
+    """The fitting criterion for Gaussian process hyperparameter optimization.
+
+    Accepts:
+        * :class:`.components.fit_criterion.FitCriterion`
+        * :class:`.components.fit_criterion.FitCriterionFactoryProtocol`
+    """
+
     # TODO: type should be Optional[botorch.models.SingleTaskGP] but is currently
     #   omitted due to: https://github.com/python-attrs/cattrs/issues/531
     _model = field(init=False, default=None, eq=False)
@@ -195,6 +215,7 @@ def from_preset(
         likelihood_or_factory: LikelihoodFactoryProtocol
         | GPyTorchLikelihood
         | None = None,
+        criterion_or_factory: FitCriterion | FitCriterionFactoryProtocol | None = None,
     ) -> Self:
         """Create a Gaussian process surrogate from one of the defined presets."""
         preset = GaussianProcessPreset(preset)
@@ -204,13 +225,16 @@ def from_preset(
         )
         module = importlib.import_module(module_name)
 
-        kernel = kernel_or_factory or getattr(module, "PresetKernelFactory")()
-        mean = mean_or_factory or getattr(module, "PresetMeanFactory")()
-        likelihood = (
-            likelihood_or_factory or getattr(module, "PresetLikelihoodFactory")()
+        kernel = kernel_or_factory or getattr(module, "PRESET_KERNEL_FACTORY")
+        mean = mean_or_factory or getattr(module, "PRESET_MEAN_FACTORY")
+        likelihood = likelihood_or_factory or getattr(
+            module, "PRESET_LIKELIHOOD_FACTORY"
+        )
+        criterion = criterion_or_factory or getattr(
+            module, "PRESET_FIT_CRITERION_FACTORY"
         )
 
-        return cls(kernel, mean, likelihood)
+        return cls(kernel, mean, likelihood, criterion)
 
     @override
     def to_botorch(self) -> GPyTorchModel:
@@ -237,7 +261,6 @@ def _posterior(self, candidates_comp_scaled: Tensor, /) -> Posterior:
     @override
     def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
         import botorch
-        import gpytorch
         from botorch.models.transforms import Normalize, Standardize
 
         assert self._searchspace is not None  # provided by base class
@@ -281,6 +304,9 @@ def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
         ### Likelihood
         likelihood = self.likelihood_factory(context.searchspace, train_x, train_y)
 
+        ### Criterion
+        criterion = self.criterion_factory(context.searchspace, train_x, train_y)
+
         ### Model construction and fitting
         self._model = botorch.models.SingleTaskGP(
             train_x,
@@ -291,18 +317,7 @@ def _fit(self, train_x: Tensor, train_y: Tensor) -> None:
             covar_module=kernel,
             likelihood=likelihood,
         )
-
-        # TODO: This is still a temporary workaround to avoid overfitting seen in
-        #  low-dimensional TL cases. More robust settings are being researched.
-        if context.n_task_dimensions > 0:
-            mll = gpytorch.mlls.LeaveOneOutPseudoLikelihood(
-                self._model.likelihood, self._model
-            )
-        else:
-            mll = gpytorch.ExactMarginalLogLikelihood(
-                self._model.likelihood, self._model
-            )
-
+        mll = criterion.to_gpytorch(self._model.likelihood, self._model)
         botorch.fit.fit_gpytorch_mll(mll)
 
     @override
@@ -311,6 +326,9 @@ def __str__(self) -> str:
             to_string("Kernel factory", self.kernel_factory, single_line=True),
             to_string("Mean factory", self.mean_factory, single_line=True),
             to_string("Likelihood factory", self.likelihood_factory, single_line=True),
+            to_string(
+                "Fit criterion factory", self.criterion_factory, single_line=True
+            ),
         ]
         return to_string(super().__str__(), *fields)
 

baybe/surrogates/gaussian_process/presets/__init__.py

@@ -1,46 +1,60 @@
 """Gaussian process surrogate presets."""
 
+# Criterion
+from baybe.surrogates.gaussian_process.components.fit_criterion import FitCriterion
+
 # Default preset
 from baybe.surrogates.gaussian_process.presets.baybe import (
+    BayBEFitCriterionFactory,
     BayBEKernelFactory,
     BayBELikelihoodFactory,
     BayBEMeanFactory,
 )
 
 # Chen preset
-from baybe.surrogates.gaussian_process.presets.chen import CHENKernelFactory
+from baybe.surrogates.gaussian_process.presets.chen import (
+    CHENFitCriterionFactory,
+    CHENKernelFactory,
+)
 
 # Core
 from baybe.surrogates.gaussian_process.presets.core import GaussianProcessPreset
 
 # EDBO preset
 from baybe.surrogates.gaussian_process.presets.edbo import (
+    EDBOFitCriterionFactory,
     EDBOKernelFactory,
     EDBOLikelihoodFactory,
     EDBOMeanFactory,
 )
 
 # Smoothed EDBO preset
 from baybe.surrogates.gaussian_process.presets.edbo_smoothed import (
+    SmoothedEDBOFitCriterionFactory,
     SmoothedEDBOKernelFactory,
     SmoothedEDBOLikelihoodFactory,
     SmoothedEDBOMeanFactory,
 )
 
 __all__ = [
     # Core
+    "FitCriterion",
     "GaussianProcessPreset",
     # Default BayBE preset
+    "BayBEFitCriterionFactory",
     "BayBEKernelFactory",
     "BayBELikelihoodFactory",
     "BayBEMeanFactory",
     # Chen preset
+    "CHENFitCriterionFactory",
     "CHENKernelFactory",
     # EDBO preset
+    "EDBOFitCriterionFactory",
     "EDBOKernelFactory",
     "EDBOLikelihoodFactory",
     "EDBOMeanFactory",
     # Smoothed EDBO preset
+    "SmoothedEDBOFitCriterionFactory",
     "SmoothedEDBOKernelFactory",
     "SmoothedEDBOLikelihoodFactory",
     "SmoothedEDBOMeanFactory",

baybe/surrogates/gaussian_process/presets/baybe.py

@@ -17,6 +17,10 @@
     to_parameter_selector,
 )
 from baybe.searchspace.core import SearchSpace
+from baybe.surrogates.gaussian_process.components.fit_criterion import (
+    FitCriterion,
+    FitCriterionFactoryProtocol,
+)
 from baybe.surrogates.gaussian_process.components.kernel import _PureKernelFactory
 from baybe.surrogates.gaussian_process.components.mean import LazyConstantMeanFactory
 from baybe.surrogates.gaussian_process.presets.edbo_smoothed import (
@@ -85,7 +89,24 @@ def _make(
 BayBELikelihoodFactory = SmoothedEDBOLikelihoodFactory
 """The factory providing the default likelihood for Gaussian process surrogates."""
 
-# Aliases for generic preset imports
-PresetKernelFactory = BayBEKernelFactory
-PresetMeanFactory = BayBEMeanFactory
-PresetLikelihoodFactory = BayBELikelihoodFactory
+
+@define
+class BayBEFitCriterionFactory(FitCriterionFactoryProtocol):
+    """The factory providing the default fitting criterion for Gaussian process surrogates."""  # noqa: E501
+
+    @override
+    def __call__(
+        self, searchspace: SearchSpace, train_x: Tensor, train_y: Tensor
+    ) -> FitCriterion:
+        return (
+            FitCriterion.MARGINAL_LOG_LIKELIHOOD
+            if searchspace.task_idx is None
+            else FitCriterion.LEAVE_ONE_OUT_PSEUDOLIKELIHOOD
+        )
+
+
+# Preset defaults
+PRESET_KERNEL_FACTORY = BayBEKernelFactory()
+PRESET_MEAN_FACTORY = BayBEMeanFactory()
+PRESET_LIKELIHOOD_FACTORY = BayBELikelihoodFactory()
+PRESET_FIT_CRITERION_FACTORY = BayBEFitCriterionFactory()

baybe/surrogates/gaussian_process/presets/chen.py

@@ -18,6 +18,10 @@
     to_parameter_selector,
 )
 from baybe.priors.basic import GammaPrior
+from baybe.surrogates.gaussian_process.components.fit_criterion import (
+    FitCriterion,
+    PlainFitCriterionFactory,
+)
 from baybe.surrogates.gaussian_process.components.kernel import (
     _PureKernelFactory,
 )
@@ -68,10 +72,14 @@ def _make(
         )
 
 
+CHENFitCriterionFactory = PlainFitCriterionFactory(FitCriterion.MARGINAL_LOG_LIKELIHOOD)
+"""A factory providing fitting criteria for the CHEN preset."""
+
 # Collect leftover original slotted classes processed by `attrs.define`
 gc.collect()
 
-# Aliases for generic preset imports
-PresetKernelFactory = CHENKernelFactory
-PresetMeanFactory = LazyConstantMeanFactory
-PresetLikelihoodFactory = LazyGaussianLikelihoodFactory
+# Preset defaults
+PRESET_KERNEL_FACTORY = CHENKernelFactory()
+PRESET_MEAN_FACTORY = LazyConstantMeanFactory()
+PRESET_LIKELIHOOD_FACTORY = LazyGaussianLikelihoodFactory()
+PRESET_FIT_CRITERION_FACTORY = CHENFitCriterionFactory