Information-based Mutlifidelity

bofire/data_models/acquisition_functions/acquisition_function.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Dict, Literal, Optional
+from typing import Annotated, Dict, List, Literal, Optional, Union
 
 from pydantic import Field, PositiveFloat
 
@@ -18,6 +18,10 @@ class MultiObjectiveAcquisitionFunction(AcquisitionFunction):
     type: str
 
 
+class MultiFideltyAcquisitionFunction(AcquisitionFunction):
+    type: str
+
+
 class qNEI(SingleObjectiveAcquisitionFunction):
     type: Literal["qNEI"] = "qNEI"
     prune_baseline: bool = True
@@ -87,3 +91,17 @@ class qNegIntPosVar(SingleObjectiveAcquisitionFunction):
     type: Literal["qNegIntPosVar"] = "qNegIntPosVar"
     n_mc_samples: IntPowerOfTwo = 512
     weights: Optional[Dict[str, PositiveFloat]] = Field(default_factory=lambda: None)
+
+
+class qMFMES(MultiFideltyAcquisitionFunction):
+    type: Literal["qMFMES"] = "qMFMES"
+    num_fantasies: IntPowerOfTwo = 16
+    num_mv_samples: int = 10
+    num_y_samples: IntPowerOfTwo = 128
+    fidelity_costs: list[float]
+
+
+class qMFVariance(MultiFideltyAcquisitionFunction):
+    type: Literal["qMFVariance"] = "qMFVariance"
+    beta: Annotated[float, Field(ge=0)] = 0.2
+    fidelity_thresholds: Union[List[float], float] = 0.1

bofire/data_models/acquisition_functions/api.py

@@ -2,6 +2,7 @@
 
 from bofire.data_models.acquisition_functions.acquisition_function import (
     AcquisitionFunction,
+    MultiFideltyAcquisitionFunction,
     MultiObjectiveAcquisitionFunction,
     SingleObjectiveAcquisitionFunction,
     qEHVI,
@@ -10,6 +11,8 @@
     qLogEI,
     qLogNEHVI,
     qLogNEI,
+    qMFMES,
+    qMFVariance,
     qNegIntPosVar,
     qNEHVI,
     qNEI,
@@ -23,6 +26,7 @@
     AcquisitionFunction,
     SingleObjectiveAcquisitionFunction,
     MultiObjectiveAcquisitionFunction,
+    MultiFideltyAcquisitionFunction,
 ]
 
 AnyAcquisitionFunction = Union[
@@ -53,3 +57,5 @@
 AnyMultiObjectiveAcquisitionFunction = Union[qEHVI, qLogEHVI, qNEHVI, qLogNEHVI]
 
 AnyActiveLearningAcquisitionFunction = qNegIntPosVar
+
+AnyMultiFidelityAcquisitionFunction = Union[qMFMES, qMFVariance]

bofire/data_models/strategies/predictives/multi_fidelity.py

@@ -1,7 +1,11 @@
-from typing import List, Literal, Union
+from typing import Literal
 
-from pydantic import model_validator
+from pydantic import Field, model_validator
 
+from bofire.data_models.acquisition_functions.api import (
+    AnyMultiFidelityAcquisitionFunction,
+    qMFVariance,
+)
 from bofire.data_models.domain.api import Domain, Outputs
 from bofire.data_models.features.api import TaskInput
 from bofire.data_models.strategies.predictives.sobo import SoboStrategy
@@ -11,20 +15,24 @@
 class MultiFidelityStrategy(SoboStrategy):
     type: Literal["MultiFidelityStrategy"] = "MultiFidelityStrategy"
 
-    fidelity_thresholds: Union[List[float], float] = 0.1
+    fidelity_acquisition_function: AnyMultiFidelityAcquisitionFunction = Field(
+        default_factory=lambda: qMFVariance(),
+    )
 
     @model_validator(mode="after")
     def validate_tasks_and_fidelity_thresholds(self):
         """Ensures that there is one threshold per fidelity"""
         task_input, *_ = self.domain.inputs.get(includes=TaskInput, exact=True)
         num_tasks = len(task_input.categories)  # type: ignore
+        fid_acqf = self.fidelity_acquisition_function
 
         if (
-            isinstance(self.fidelity_thresholds, list)
-            and len(self.fidelity_thresholds) != num_tasks
+            isinstance(fid_acqf, qMFVariance)
+            and isinstance(fid_acqf.fidelity_thresholds, list)
+            and len(fid_acqf.fidelity_thresholds) != num_tasks
         ):
             raise ValueError(
-                f"The number of tasks should be equal to the number of fidelity thresholds (got {num_tasks} tasks, {len(self.fidelity_thresholds)} thresholds)."
+                f"The number of tasks should be equal to the number of fidelity thresholds (got {num_tasks} tasks, {len(fid_acqf.fidelity_thresholds)} thresholds)."
             )
 
         return self