New acquisition functions

CHANGELOG.md

@@ -9,10 +9,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `mypy` for search space and objectives
 - Class hierarchy for objectives
 - Deserialization is now also possible from optional class name abbreviations
-- Hypothesis strategies for acquisition functions
 - `Kernel` base class allowing to specify kernels
 - `MaternKernel` class can be chosen for GP surrogates
-- `hypothesis` strategies and roundtrip test for kernels, constraints and objectives
+- `hypothesis` strategies and roundtrip test for kernels, constraints, objectives and acquisition
+  functions
+- New acquisition functions: `qSR`, `qNEI`, `LogEI`, `qLogEI`, `qLogNEI`
 
 ### Changed
 - Reorganized acquisition.py into `acquisition` subpackage

baybe/acquisition/__init__.py

@@ -2,35 +2,67 @@
 
 from baybe.acquisition.acqfs import (
     ExpectedImprovement,
+    LogExpectedImprovement,
+    PosteriorMean,
     ProbabilityOfImprovement,
     UpperConfidenceBound,
     qExpectedImprovement,
+    qLogExpectedImprovement,
+    qLogNoisyExpectedImprovement,
+    qNoisyExpectedImprovement,
     qProbabilityOfImprovement,
+    qSimpleRegret,
     qUpperConfidenceBound,
 )
 
+PM = PosteriorMean
+qSR = qSimpleRegret
 EI = ExpectedImprovement
-PI = ProbabilityOfImprovement
-UCB = UpperConfidenceBound
 qEI = qExpectedImprovement
+LogEI = LogExpectedImprovement
+qLogEI = qLogExpectedImprovement
+qNEI = qNoisyExpectedImprovement
+qLogNEI = qLogNoisyExpectedImprovement
+PI = ProbabilityOfImprovement
 qPI = qProbabilityOfImprovement
+UCB = UpperConfidenceBound
 qUCB = qUpperConfidenceBound
 
 __all__ = [
-    # ---------------------------
-    # Acquisition functions
+    ######################### Acquisition functions
+    # Posterior Mean
+    "PosteriorMean",
+    # Simple Regret
+    "qSimpleRegret",
+    # Expected Improvement
     "ExpectedImprovement",
-    "ProbabilityOfImprovement",
-    "UpperConfidenceBound",
     "qExpectedImprovement",
+    "LogExpectedImprovement",
+    "qLogExpectedImprovement",
+    "qNoisyExpectedImprovement",
+    "qLogNoisyExpectedImprovement",
+    # Probability of Improvement
+    "ProbabilityOfImprovement",
     "qProbabilityOfImprovement",
+    # Upper Confidence Bound
+    "UpperConfidenceBound",
     "qUpperConfidenceBound",
-    # ---------------------------
-    # Abbreviations
+    ######################### Abbreviations
+    # Posterior Mean
+    "PM",
+    # Simple Regret
+    "qSR",
+    # Expected Improvement
     "EI",
-    "PI",
-    "UCB",
     "qEI",
+    "LogEI",
+    "qLogEI",
+    "qNEI",
+    "qLogNEI",
+    # Probability of Improvement
+    "PI",
     "qPI",
+    # Upper Confidence Bound
+    "UCB",
     "qUCB",
 ]

baybe/acquisition/_adapter.py

@@ -0,0 +1,45 @@
+"""Adapter for making BoTorch's acquisition functions work with BayBE models."""
+
+from typing import Any, Callable, Optional
+
+import gpytorch.distributions
+from botorch.models.gpytorch import Model
+from botorch.posteriors import Posterior
+from botorch.posteriors.gpytorch import GPyTorchPosterior
+from torch import Tensor
+
+from baybe.surrogates.base import Surrogate
+
+
+class AdapterModel(Model):
+    """A BoTorch model that uses a BayBE surrogate model for posterior computation.
+
+    Can be used, for example, as an adapter layer for making a BayBE
+    surrogate model usable in conjunction with BoTorch acquisition functions.
+
+    Args:
+        surrogate: The internal surrogate model
+    """
+
+    def __init__(self, surrogate: Surrogate):
+        super().__init__()
+        self._surrogate = surrogate
+
+    @property
+    def num_outputs(self) -> int:  # noqa: D102
+        # See base class.
+        # TODO: So far, the usage is limited to single-output models.
+        return 1
+
+    def posterior(  # noqa: D102
+        self,
+        X: Tensor,
+        output_indices: Optional[list[int]] = None,
+        observation_noise: bool = False,
+        posterior_transform: Optional[Callable[[Posterior], Posterior]] = None,
+        **kwargs: Any,
+    ) -> Posterior:
+        # See base class.
+        mean, var = self._surrogate.posterior(X)
+        mvn = gpytorch.distributions.MultivariateNormal(mean, var)
+        return GPyTorchPosterior(mvn)

baybe/acquisition/acqfs.py

@@ -3,25 +3,31 @@
 from typing import ClassVar
 
 from attrs import define, field
-from attrs.validators import ge
+from attrs.validators import ge, instance_of
 
 from baybe.acquisition.base import AcquisitionFunction
 
 
+########################################################################################
+### Posterior Mean
 @define(frozen=True)
 class PosteriorMean(AcquisitionFunction):
     """Posterior mean."""
 
     _abbreviation: ClassVar[str] = "PM"
 
 
+########################################################################################
+### Simple Regret
 @define(frozen=True)
-class qExpectedImprovement(AcquisitionFunction):
-    """Monte Carlo based expected improvement."""
+class qSimpleRegret(AcquisitionFunction):
+    """Monte Carlo based simple regret."""
 
-    _abbreviation: ClassVar[str] = "qEI"
+    _abbreviation: ClassVar[str] = "qSR"
 
 
+########################################################################################
+### Expected Improvement
 @define(frozen=True)
 class ExpectedImprovement(AcquisitionFunction):
     """Analytical expected improvement."""
@@ -30,12 +36,48 @@ class ExpectedImprovement(AcquisitionFunction):
 
 
 @define(frozen=True)
-class qProbabilityOfImprovement(AcquisitionFunction):
-    """Monte Carlo based probability of improvement."""
+class qExpectedImprovement(AcquisitionFunction):
+    """Monte Carlo based expected improvement."""
+
+    _abbreviation: ClassVar[str] = "qEI"
+
+
+@define(frozen=True)
+class LogExpectedImprovement(AcquisitionFunction):
+    """Logarithmic analytical expected improvement."""
+
+    _abbreviation: ClassVar[str] = "LogEI"
+
+
+@define(frozen=True)
+class qLogExpectedImprovement(AcquisitionFunction):
+    """Logarithmic Monte Carlo based expected improvement."""
+
+    _abbreviation: ClassVar[str] = "qLogEI"
+
+
+@define(frozen=True)
+class qNoisyExpectedImprovement(AcquisitionFunction):
+    """Monte Carlo based noisy expected improvement."""
+
+    _abbreviation: ClassVar[str] = "qNEI"
+
+    prune_baseline: bool = field(default=True, validator=instance_of(bool))
+    """Auto-prune candidates that are unlikely to be the best."""
 
-    _abbreviation: ClassVar[str] = "qPI"
 
+@define(frozen=True)
+class qLogNoisyExpectedImprovement(AcquisitionFunction):
+    """Logarithmic Monte Carlo based noisy expected improvement."""
+
+    _abbreviation: ClassVar[str] = "qLogNEI"
+
+    prune_baseline: bool = field(default=True, validator=instance_of(bool))
+    """Auto-prune candidates that are unlikely to be the best."""
 
+
+########################################################################################
+### Probability of Improvement
 @define(frozen=True)
 class ProbabilityOfImprovement(AcquisitionFunction):
     """Analytical probability of improvement."""
@@ -44,10 +86,19 @@ class ProbabilityOfImprovement(AcquisitionFunction):
 
 
 @define(frozen=True)
-class qUpperConfidenceBound(AcquisitionFunction):
-    """Monte Carlo based upper confidence bound."""
+class qProbabilityOfImprovement(AcquisitionFunction):
+    """Monte Carlo based probability of improvement."""
+
+    _abbreviation: ClassVar[str] = "qPI"
 
-    _abbreviation: ClassVar[str] = "qUCB"
+
+########################################################################################
+### Upper Confidence Bound
+@define(frozen=True)
+class UpperConfidenceBound(AcquisitionFunction):
+    """Analytical upper confidence bound."""
+
+    _abbreviation: ClassVar[str] = "UCB"
 
     beta: float = field(converter=float, validator=ge(0.0), default=0.2)
     """Trade-off parameter for mean and variance.
@@ -59,10 +110,10 @@ class qUpperConfidenceBound(AcquisitionFunction):
 
 
 @define(frozen=True)
-class UpperConfidenceBound(AcquisitionFunction):
-    """Analytical upper confidence bound."""
+class qUpperConfidenceBound(AcquisitionFunction):
+    """Monte Carlo based upper confidence bound."""
 
-    _abbreviation: ClassVar[str] = "UCB"
+    _abbreviation: ClassVar[str] = "qUCB"
 
     beta: float = field(converter=float, validator=ge(0.0), default=0.2)
     """Trade-off parameter for mean and variance.