Rename Criterion to FitCriterion

Author: AdrianSosic

CHANGELOG.md

@@ -15,7 +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 optimization criterion for Gaussian process hyperparameter selection
+- 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

@@ -1,9 +1,9 @@
 """Gaussian process surrogate components."""
 
 from baybe.surrogates.gaussian_process.components.criterion import (
-    Criterion,
-    CriterionFactoryProtocol,
-    PlainCriterionFactory,
+    FitCriterion,
+    FitCriterionFactoryProtocol,
+    PlainFitCriterionFactory,
 )
 from baybe.surrogates.gaussian_process.components.kernel import (
     KernelFactoryProtocol,
@@ -21,9 +21,9 @@
 
 __all__ = [
     # Criterion
-    "Criterion",
-    "CriterionFactoryProtocol",
-    "PlainCriterionFactory",
+    "FitCriterion",
+    "FitCriterionFactoryProtocol",
+    "PlainFitCriterionFactory",
     # Kernel
     "KernelFactoryProtocol",
     "PlainKernelFactory",

baybe/surrogates/gaussian_process/components/criterion.py

@@ -1,4 +1,4 @@
-"""Optimization criteria for the Gaussian process surrogate."""
+"""Fitting criteria for the Gaussian process surrogate."""
 
 from __future__ import annotations
 
@@ -16,8 +16,8 @@
     from gpytorch.models import GP as GPyTorchModel
 
 
-class Criterion(Enum):
-    """Available optimization criteria for GP hyperparameter selection."""
+class FitCriterion(Enum):
+    """Available fitting criteria for GP hyperparameter optimization."""
 
     MARGINAL_LOG_LIKELIHOOD = "MARGINAL_LOG_LIKELIHOOD"
     """Exact marginal log-likelihood."""
@@ -32,14 +32,14 @@ def to_gpytorch(
         import gpytorch
 
         mll_class = {
-            Criterion.MARGINAL_LOG_LIKELIHOOD: gpytorch.ExactMarginalLogLikelihood,
-            Criterion.LEAVE_ONE_OUT: gpytorch.mlls.LeaveOneOutPseudoLikelihood,
+            FitCriterion.MARGINAL_LOG_LIKELIHOOD: gpytorch.ExactMarginalLogLikelihood,
+            FitCriterion.LEAVE_ONE_OUT: gpytorch.mlls.LeaveOneOutPseudoLikelihood,
         }[self]
         return mll_class(likelihood, model)
 
 
-CriterionFactoryProtocol = GPComponentFactoryProtocol[Criterion]
-"""A protocol defining the interface for criterion factories."""
+FitCriterionFactoryProtocol = GPComponentFactoryProtocol[FitCriterion]
+"""A protocol defining the interface for fit criterion factories."""
 
-PlainCriterionFactory = PlainGPComponentFactory[Criterion]
-"""A trivial factory that returns a fixed criterion."""
+PlainFitCriterionFactory = PlainGPComponentFactory[FitCriterion]
+"""A trivial factory that returns a fixed fit criterion."""

baybe/surrogates/gaussian_process/components/generic.py

@@ -23,10 +23,10 @@
     from gpytorch.means import Mean as GPyTorchMean
     from torch import Tensor
 
-    from baybe.surrogates.gaussian_process.components.criterion import Criterion
+    from baybe.surrogates.gaussian_process.components.criterion import FitCriterion
 
     GPyTorchGPComponent: TypeAlias = GPyTorchKernel | GPyTorchMean | GPyTorchLikelihood
-    GPComponent: TypeAlias = BayBEGPComponent | GPyTorchGPComponent | Criterion
+    GPComponent: TypeAlias = BayBEGPComponent | GPyTorchGPComponent | FitCriterion
 else:
     # At runtime, we use only the BayBE types for serialization compatibility
     GPComponent: TypeAlias = BayBEGPComponent
@@ -47,7 +47,7 @@ class GPComponentType(Enum):
     """Gaussian process likelihood."""
 
     CRITERION = "CRITERION"
-    """Gaussian process optimization criterion."""
+    """Gaussian process fitting criterion."""
 
     def get_types(self) -> tuple[type, ...]:
         """Get the accepted BayBE and GPyTorch types for this component."""
@@ -60,10 +60,10 @@ def get_types(self) -> tuple[type, ...]:
             types.append(Kernel)
         elif self is GPComponentType.CRITERION:
             from baybe.surrogates.gaussian_process.components.criterion import (
-                Criterion,
+                FitCriterion,
             )
 
-            types.append(Criterion)
+            types.append(FitCriterion)
 
         # Add GPyTorch type if available
         if sys.modules.get("gpytorch") is not None:

baybe/surrogates/gaussian_process/core.py

@@ -20,8 +20,8 @@
 from baybe.searchspace.core import SearchSpace
 from baybe.surrogates.base import Surrogate
 from baybe.surrogates.gaussian_process.components.criterion import (
-    Criterion,
-    CriterionFactoryProtocol,
+    FitCriterion,
+    FitCriterionFactoryProtocol,
 )
 from baybe.surrogates.gaussian_process.components.generic import (
     GPComponentType,
@@ -39,7 +39,7 @@
     GaussianProcessPreset,
 )
 from baybe.surrogates.gaussian_process.presets.baybe import (
-    BayBECriterionFactory,
+    BayBEFitCriterionFactory,
     BayBEKernelFactory,
     BayBELikelihoodFactory,
     BayBEMeanFactory,
@@ -183,19 +183,19 @@ class GaussianProcessSurrogate(Surrogate):
         * :class:`gpytorch.likelihoods.Likelihood`
     """
 
-    criterion_factory: CriterionFactoryProtocol = field(
+    criterion_factory: FitCriterionFactoryProtocol = field(
         alias="criterion_or_factory",
-        factory=BayBECriterionFactory,
+        factory=BayBEFitCriterionFactory,
         converter=partial(  # type: ignore[misc]
             to_component_factory, component_type=GPComponentType.CRITERION
         ),
         validator=is_callable(),
     )
-    """The optimization criterion for hyperparameter selection.
+    """The fitting criterion for Gaussian process hyperparameter optimization.
 
     Accepts:
-        * :class:`.components.criterion.Criterion`
-        * :class:`.components.criterion.CriterionFactoryProtocol`
+        * :class:`.components.criterion.FitCriterion`
+        * :class:`.components.criterion.FitCriterionFactoryProtocol`
     """
 
     # TODO: type should be Optional[botorch.models.SingleTaskGP] but is currently
@@ -215,7 +215,7 @@ def from_preset(
         likelihood_or_factory: LikelihoodFactoryProtocol
         | GPyTorchLikelihood
         | None = None,
-        criterion_or_factory: Criterion | CriterionFactoryProtocol | None = None,
+        criterion_or_factory: FitCriterion | FitCriterionFactoryProtocol | None = None,
     ) -> Self:
         """Create a Gaussian process surrogate from one of the defined presets."""
         preset = GaussianProcessPreset(preset)
@@ -230,7 +230,9 @@ def from_preset(
         likelihood = likelihood_or_factory or getattr(
             module, "PRESET_LIKELIHOOD_FACTORY"
         )
-        criterion = criterion_or_factory or getattr(module, "PRESET_CRITERION_FACTORY")
+        criterion = criterion_or_factory or getattr(
+            module, "PRESET_FIT_CRITERION_FACTORY"
+        )
 
         return cls(kernel, mean, likelihood, criterion)
 
@@ -324,7 +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("Criterion factory", self.criterion_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

@@ -1,19 +1,19 @@
 """Gaussian process surrogate presets."""
 
 # Criterion
-from baybe.surrogates.gaussian_process.components.criterion import Criterion
+from baybe.surrogates.gaussian_process.components.criterion import FitCriterion
 
 # Default preset
 from baybe.surrogates.gaussian_process.presets.baybe import (
-    BayBECriterionFactory,
+    BayBEFitCriterionFactory,
     BayBEKernelFactory,
     BayBELikelihoodFactory,
     BayBEMeanFactory,
 )
 
 # Chen preset
 from baybe.surrogates.gaussian_process.presets.chen import (
-    CHENCriterionFactory,
+    CHENFitCriterionFactory,
     CHENKernelFactory,
 )
 
@@ -22,39 +22,39 @@
 
 # EDBO preset
 from baybe.surrogates.gaussian_process.presets.edbo import (
-    EDBOCriterionFactory,
+    EDBOFitCriterionFactory,
     EDBOKernelFactory,
     EDBOLikelihoodFactory,
     EDBOMeanFactory,
 )
 
 # Smoothed EDBO preset
 from baybe.surrogates.gaussian_process.presets.edbo_smoothed import (
-    SmoothedEDBOCriterionFactory,
+    SmoothedEDBOFitCriterionFactory,
     SmoothedEDBOKernelFactory,
     SmoothedEDBOLikelihoodFactory,
     SmoothedEDBOMeanFactory,
 )
 
 __all__ = [
     # Core
-    "Criterion",
+    "FitCriterion",
     "GaussianProcessPreset",
     # Default BayBE preset
-    "BayBECriterionFactory",
+    "BayBEFitCriterionFactory",
     "BayBEKernelFactory",
     "BayBELikelihoodFactory",
     "BayBEMeanFactory",
     # Chen preset
-    "CHENCriterionFactory",
+    "CHENFitCriterionFactory",
     "CHENKernelFactory",
     # EDBO preset
-    "EDBOCriterionFactory",
+    "EDBOFitCriterionFactory",
     "EDBOKernelFactory",
     "EDBOLikelihoodFactory",
     "EDBOMeanFactory",
     # Smoothed EDBO preset
-    "SmoothedEDBOCriterionFactory",
+    "SmoothedEDBOFitCriterionFactory",
     "SmoothedEDBOKernelFactory",
     "SmoothedEDBOLikelihoodFactory",
     "SmoothedEDBOMeanFactory",

baybe/surrogates/gaussian_process/presets/baybe.py

@@ -18,8 +18,8 @@
 )
 from baybe.searchspace.core import SearchSpace
 from baybe.surrogates.gaussian_process.components.criterion import (
-    Criterion,
-    CriterionFactoryProtocol,
+    FitCriterion,
+    FitCriterionFactoryProtocol,
 )
 from baybe.surrogates.gaussian_process.components.kernel import _PureKernelFactory
 from baybe.surrogates.gaussian_process.components.mean import LazyConstantMeanFactory
@@ -91,22 +91,22 @@ def _make(
 
 
 @define
-class BayBECriterionFactory(CriterionFactoryProtocol):
-    """The factory providing the default optimization criterion for Gaussian process surrogates."""  # noqa: E501
+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
-    ) -> Criterion:
+    ) -> FitCriterion:
         return (
-            Criterion.MARGINAL_LOG_LIKELIHOOD
+            FitCriterion.MARGINAL_LOG_LIKELIHOOD
             if searchspace.task_idx is None
-            else Criterion.LEAVE_ONE_OUT
+            else FitCriterion.LEAVE_ONE_OUT
         )
 
 
 # Preset defaults
 PRESET_KERNEL_FACTORY = BayBEKernelFactory()
 PRESET_MEAN_FACTORY = BayBEMeanFactory()
 PRESET_LIKELIHOOD_FACTORY = BayBELikelihoodFactory()
-PRESET_CRITERION_FACTORY = BayBECriterionFactory()
+PRESET_FIT_CRITERION_FACTORY = BayBEFitCriterionFactory()

baybe/surrogates/gaussian_process/presets/chen.py

@@ -19,8 +19,8 @@
 )
 from baybe.priors.basic import GammaPrior
 from baybe.surrogates.gaussian_process.components.criterion import (
-    Criterion,
-    PlainCriterionFactory,
+    FitCriterion,
+    PlainFitCriterionFactory,
 )
 from baybe.surrogates.gaussian_process.components.kernel import (
     _PureKernelFactory,
@@ -72,8 +72,8 @@ def _make(
         )
 
 
-CHENCriterionFactory = PlainCriterionFactory(Criterion.MARGINAL_LOG_LIKELIHOOD)
-"""A factory providing optimization criteria for the CHEN preset."""
+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()
@@ -82,4 +82,4 @@ def _make(
 PRESET_KERNEL_FACTORY = CHENKernelFactory()
 PRESET_MEAN_FACTORY = LazyConstantMeanFactory()
 PRESET_LIKELIHOOD_FACTORY = LazyGaussianLikelihoodFactory()
-PRESET_CRITERION_FACTORY = CHENCriterionFactory
+PRESET_FIT_CRITERION_FACTORY = CHENFitCriterionFactory

baybe/surrogates/gaussian_process/presets/edbo.py

@@ -22,8 +22,8 @@
 from baybe.priors.basic import GammaPrior
 from baybe.searchspace.discrete import SubspaceDiscrete
 from baybe.surrogates.gaussian_process.components.criterion import (
-    Criterion,
-    PlainCriterionFactory,
+    FitCriterion,
+    PlainFitCriterionFactory,
 )
 from baybe.surrogates.gaussian_process.components.kernel import (
     _PureKernelFactory,
@@ -180,13 +180,13 @@ def __call__(
 
 
 # Collect leftover original slotted classes processed by `attrs.define`
-EDBOCriterionFactory = PlainCriterionFactory(Criterion.MARGINAL_LOG_LIKELIHOOD)
-"""A factory providing optimization criteria for the EDBO preset."""
+EDBOFitCriterionFactory = PlainFitCriterionFactory(FitCriterion.MARGINAL_LOG_LIKELIHOOD)
+"""A factory providing fitting criteria for the EDBO preset."""
 
 gc.collect()
 
 # Preset defaults
 PRESET_KERNEL_FACTORY = EDBOKernelFactory()
 PRESET_MEAN_FACTORY = EDBOMeanFactory()
 PRESET_LIKELIHOOD_FACTORY = EDBOLikelihoodFactory()
-PRESET_CRITERION_FACTORY = EDBOCriterionFactory
+PRESET_FIT_CRITERION_FACTORY = EDBOFitCriterionFactory

baybe/surrogates/gaussian_process/presets/edbo_smoothed.py

@@ -19,8 +19,8 @@
 )
 from baybe.priors.basic import GammaPrior
 from baybe.surrogates.gaussian_process.components.criterion import (
-    Criterion,
-    PlainCriterionFactory,
+    FitCriterion,
+    PlainFitCriterionFactory,
 )
 from baybe.surrogates.gaussian_process.components.kernel import (
     _PureKernelFactory,
@@ -130,8 +130,10 @@ def __call__(
         return likelihood
 
 
-SmoothedEDBOCriterionFactory = PlainCriterionFactory(Criterion.MARGINAL_LOG_LIKELIHOOD)
-"""A factory providing optimization criteria for the smoothed EDBO preset."""
+SmoothedEDBOFitCriterionFactory = PlainFitCriterionFactory(
+    FitCriterion.MARGINAL_LOG_LIKELIHOOD
+)
+"""A factory providing fitting criteria for the smoothed EDBO preset."""
 
 # Collect leftover original slotted classes processed by `attrs.define`
 gc.collect()
@@ -140,4 +142,4 @@ def __call__(
 PRESET_KERNEL_FACTORY = SmoothedEDBOKernelFactory()
 PRESET_MEAN_FACTORY = SmoothedEDBOMeanFactory()
 PRESET_LIKELIHOOD_FACTORY = SmoothedEDBOLikelihoodFactory()
-PRESET_CRITERION_FACTORY = SmoothedEDBOCriterionFactory
+PRESET_FIT_CRITERION_FACTORY = SmoothedEDBOFitCriterionFactory