Make method for surrogates

bofire/benchmarks/hyperopt.py

@@ -19,7 +19,7 @@ def __init__(
         show_progress_bar: bool = False,
     ) -> None:
         super().__init__()
-        if surrogate_data.hyperconfig is None:
+        if surrogate_data.hyperconfig_access is None:
             raise ValueError("No hyperoptimization configuration found.")
         self.surrogate_data = surrogate_data
         self.training_data = training_data
@@ -30,11 +30,11 @@ def __init__(
 
     @property
     def domain(self) -> Domain:
-        return self.surrogate_data.hyperconfig.domain  # type: ignore
+        return self.surrogate_data.hyperconfig_access.domain  # type: ignore
 
     @property
     def target_metric(self):
-        return self.surrogate_data.hyperconfig.target_metric  # type: ignore
+        return self.surrogate_data.hyperconfig_access.target_metric  # type: ignore
 
     def _f(self, candidates: pd.DataFrame) -> pd.DataFrame:
         for i, candidate in tqdm(

bofire/data_models/surrogates/bnn.py

@@ -1,11 +1,31 @@
-from typing import Literal, Optional
+from typing import Literal, Optional, Type
 
+from pydantic import Field
+
+from bofire.data_models.features.api import AnyOutput
+from bofire.data_models.features.continuous import ContinuousOutput
 from bofire.data_models.kernels.api import InfiniteWidthBNNKernel
-from bofire.data_models.surrogates.single_task_gp import SingleTaskGPSurrogate
+from bofire.data_models.priors.api import HVARFNER_NOISE_PRIOR, AnyPrior
+from bofire.data_models.surrogates.single_task_gp import TrainableBotorchSurrogate
 from bofire.data_models.surrogates.trainable import Hyperconfig
 
 
-class SingleTaskIBNNSurrogate(SingleTaskGPSurrogate):
+class SingleTaskIBNNSurrogate(TrainableBotorchSurrogate):
     type: Literal["SingleTaskIBNNSurrogate"] = "SingleTaskIBNNSurrogate"
     kernel: InfiniteWidthBNNKernel = InfiniteWidthBNNKernel()
     hyperconfig: Optional[Hyperconfig] = None
+    noise_prior: AnyPrior = Field(default_factory=lambda: HVARFNER_NOISE_PRIOR())
+
+    @classmethod
+    def is_output_implemented(cls, my_type: Type[AnyOutput]) -> bool:
+        """Abstract method to check output type for surrogate models
+        Args:
+            my_type: continuous or categorical output
+        Returns:
+            bool: True if the output type is valid for the surrogate chosen, False otherwise
+        """
+        return isinstance(my_type, type(ContinuousOutput))
+
+    @property
+    def hyperconfig_access(self) -> Optional[Hyperconfig]:
+        return self.hyperconfig

bofire/data_models/surrogates/fully_bayesian.py

@@ -1,8 +1,9 @@
-from typing import Annotated, List, Literal, Type
+from typing import Annotated, List, Literal, Optional, Type
 
 from pydantic import AfterValidator, Field, field_validator, model_validator
 
 from bofire.data_models.features.api import AnyOutput, ContinuousOutput
+from bofire.data_models.surrogates.trainable import Hyperconfig
 from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 from bofire.data_models.types import make_unique_validator
 
@@ -19,6 +20,12 @@ class FullyBayesianSingleTaskGPSurrogate(TrainableBotorchSurrogate):
         List[str], AfterValidator(make_unique_validator("Features"))
     ] = []
 
+    hyperconfig: Optional[Hyperconfig] = None
+
+    @property
+    def hyperconfig_access(self) -> Optional[Hyperconfig]:
+        return self.hyperconfig
+
     @model_validator(mode="after")
     def validate_features_to_warp(self):
         input_keys = self.inputs.get_keys()

bofire/data_models/surrogates/linear.py

@@ -1,4 +1,4 @@
-from typing import Literal, Type
+from typing import Literal, Optional, Type
 
 from pydantic import Field
 
@@ -7,12 +7,19 @@
 from bofire.data_models.kernels.api import LinearKernel
 from bofire.data_models.priors.api import THREESIX_NOISE_PRIOR, AnyPrior
 from bofire.data_models.surrogates.scaler import ScalerEnum
+from bofire.data_models.surrogates.trainable import Hyperconfig
 from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
 class LinearSurrogate(TrainableBotorchSurrogate):
     type: Literal["LinearSurrogate"] = "LinearSurrogate"
 
+    hyperconfig: Optional[Hyperconfig] = None
+
+    @property
+    def hyperconfig_access(self) -> Optional[Hyperconfig]:
+        return self.hyperconfig
+
     kernel: LinearKernel = Field(default_factory=lambda: LinearKernel())
     noise_prior: AnyPrior = Field(default_factory=lambda: THREESIX_NOISE_PRIOR())
     scaler: ScalerEnum = ScalerEnum.NORMALIZE

bofire/data_models/surrogates/map_saas.py

@@ -1,8 +1,9 @@
-from typing import Literal, Type
+from typing import Literal, Optional, Type
 
-from pydantic import PositiveInt
+from pydantic import Field, PositiveInt
 
 from bofire.data_models.features.api import AnyOutput, ContinuousOutput
+from bofire.data_models.surrogates.trainable import Hyperconfig
 from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
@@ -25,6 +26,12 @@ class AdditiveMapSaasSingleTaskGPSurrogate(TrainableBotorchSurrogate):
     )
     n_taus: PositiveInt = 4
 
+    hyperconfig: Optional[Hyperconfig] = Field(default=None)
+
+    @property
+    def hyperconfig_access(self) -> Optional[Hyperconfig]:
+        return self.hyperconfig
+
     @classmethod
     def is_output_implemented(cls, my_type: Type[AnyOutput]) -> bool:
         """Abstract method to check output type for surrogate models

bofire/data_models/surrogates/mixed_single_task_gp.py

@@ -54,53 +54,6 @@ class MixedSingleTaskGPHyperconfig(Hyperconfig):
         "FractionalFactorialStrategy", "SoboStrategy", "RandomStrategy"
     ] = "FractionalFactorialStrategy"
 
-    @staticmethod
-    def _update_hyperparameters(
-        surrogate_data: "MixedSingleTaskGPSurrogate",
-        hyperparameters: pd.Series,
-    ):
-        if hyperparameters.prior == "mbo":
-            noise_prior, lengthscale_prior, _ = (
-                MBO_NOISE_PRIOR(),
-                MBO_LENGTHSCALE_PRIOR(),
-                MBO_OUTPUTSCALE_PRIOR(),
-            )
-        elif hyperparameters.prior == "threesix":
-            noise_prior, lengthscale_prior, _ = (
-                THREESIX_NOISE_PRIOR(),
-                THREESIX_LENGTHSCALE_PRIOR(),
-                THREESIX_SCALE_PRIOR(),
-            )
-        else:
-            noise_prior, lengthscale_prior = (
-                HVARFNER_NOISE_PRIOR(),
-                HVARFNER_LENGTHSCALE_PRIOR(),
-            )
-
-        surrogate_data.noise_prior = noise_prior
-        if hyperparameters.continuous_kernel == "rbf":
-            surrogate_data.continuous_kernel = RBFKernel(
-                ard=hyperparameters.ard,
-                lengthscale_prior=lengthscale_prior,
-            )
-
-        elif hyperparameters.continuous_kernel == "matern_2.5":
-            surrogate_data.continuous_kernel = MaternKernel(
-                ard=hyperparameters.ard,
-                lengthscale_prior=lengthscale_prior,
-                nu=2.5,
-            )
-
-        elif hyperparameters.continuous_kernel == "matern_1.5":
-            surrogate_data.continuous_kernel = MaternKernel(
-                ard=hyperparameters.ard,
-                lengthscale_prior=lengthscale_prior,
-                nu=1.5,
-            )
-
-        else:
-            raise ValueError(f"Kernel {hyperparameters.kernel} not known.")
-
 
 class MixedSingleTaskGPSurrogate(TrainableBotorchSurrogate):
     type: Literal["MixedSingleTaskGPSurrogate"] = "MixedSingleTaskGPSurrogate"
@@ -121,6 +74,10 @@ class MixedSingleTaskGPSurrogate(TrainableBotorchSurrogate):
         default_factory=lambda: MixedSingleTaskGPHyperconfig(),
     )
 
+    @property
+    def hyperconfig_access(self) -> Optional[Hyperconfig]:
+        return self.hyperconfig
+
     @classmethod
     def _default_categorical_encodings(
         cls,
@@ -190,3 +147,51 @@ def is_output_implemented(cls, my_type: Type[AnyOutput]) -> bool:
             bool: True if the output type is valid for the surrogate chosen, False otherwise
         """
         return isinstance(my_type, type(ContinuousOutput))
+
+    def update_hyperparameters(
+        self,
+        hyperparameters: pd.Series,
+    ):
+        super().update_hyperparameters(hyperparameters)
+
+        if hyperparameters.prior == "mbo":
+            noise_prior, lengthscale_prior, _ = (
+                MBO_NOISE_PRIOR(),
+                MBO_LENGTHSCALE_PRIOR(),
+                MBO_OUTPUTSCALE_PRIOR(),
+            )
+        elif hyperparameters.prior == "threesix":
+            noise_prior, lengthscale_prior, _ = (
+                THREESIX_NOISE_PRIOR(),
+                THREESIX_LENGTHSCALE_PRIOR(),
+                THREESIX_SCALE_PRIOR(),
+            )
+        else:
+            noise_prior, lengthscale_prior = (
+                HVARFNER_NOISE_PRIOR(),
+                HVARFNER_LENGTHSCALE_PRIOR(),
+            )
+
+        self.noise_prior = noise_prior
+        if hyperparameters.continuous_kernel == "rbf":
+            self.continuous_kernel = RBFKernel(
+                ard=hyperparameters.ard,
+                lengthscale_prior=lengthscale_prior,
+            )
+
+        elif hyperparameters.continuous_kernel == "matern_2.5":
+            self.continuous_kernel = MaternKernel(
+                ard=hyperparameters.ard,
+                lengthscale_prior=lengthscale_prior,
+                nu=2.5,
+            )
+
+        elif hyperparameters.continuous_kernel == "matern_1.5":
+            self.continuous_kernel = MaternKernel(
+                ard=hyperparameters.ard,
+                lengthscale_prior=lengthscale_prior,
+                nu=1.5,
+            )
+
+        else:
+            raise ValueError(f"Kernel {hyperparameters.kernel} not known.")

bofire/data_models/surrogates/mlp.py

@@ -1,5 +1,5 @@
 from collections.abc import Sequence
-from typing import Annotated, Literal, Type
+from typing import Annotated, Literal, Optional, Type
 
 from pydantic import Field
 
@@ -9,11 +9,18 @@
     ContinuousOutput,
 )
 from bofire.data_models.surrogates.scaler import ScalerEnum
+from bofire.data_models.surrogates.trainable import Hyperconfig
 from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
 class MLPEnsemble(TrainableBotorchSurrogate):
     type: Literal["MLPEnsemble"] = "MLPEnsemble"
+    hyperconfig: Optional[Hyperconfig] = None
+
+    @property
+    def hyperconfig_access(self) -> Optional[Hyperconfig]:
+        return self.hyperconfig
+
     n_estimators: Annotated[int, Field(ge=1)] = 5
     hidden_layer_sizes: Sequence = (100,)
     activation: Literal["relu", "logistic", "tanh"] = "relu"

bofire/data_models/surrogates/multi_task_gp.py

@@ -44,47 +44,6 @@ class MultiTaskGPHyperconfig(Hyperconfig):
         "FractionalFactorialStrategy", "SoboStrategy", "RandomStrategy"
     ] = "FractionalFactorialStrategy"
 
-    @staticmethod
-    def _update_hyperparameters(
-        surrogate_data: "MultiTaskGPSurrogate",
-        hyperparameters: pd.Series,
-    ):
-        def matern_25(ard: bool, lengthscale_prior: AnyPrior) -> MaternKernel:
-            return MaternKernel(nu=2.5, lengthscale_prior=lengthscale_prior, ard=ard)
-
-        def matern_15(ard: bool, lengthscale_prior: AnyPrior) -> MaternKernel:
-            return MaternKernel(nu=1.5, lengthscale_prior=lengthscale_prior, ard=ard)
-
-        if hyperparameters.prior == "mbo":
-            noise_prior, lengthscale_prior = (
-                MBO_NOISE_PRIOR(),
-                MBO_LENGTHSCALE_PRIOR(),
-            )
-        else:
-            noise_prior, lengthscale_prior = (
-                THREESIX_NOISE_PRIOR(),
-                THREESIX_LENGTHSCALE_PRIOR(),
-            )
-
-        surrogate_data.noise_prior = noise_prior
-        if hyperparameters.kernel == "rbf":
-            surrogate_data.kernel = RBFKernel(
-                ard=hyperparameters.ard,
-                lengthscale_prior=lengthscale_prior,
-            )
-        elif hyperparameters.kernel == "matern_2.5":
-            surrogate_data.kernel = matern_25(
-                ard=hyperparameters.ard,
-                lengthscale_prior=lengthscale_prior,
-            )
-        elif hyperparameters.kernel == "matern_1.5":
-            surrogate_data.kernel = matern_15(
-                ard=hyperparameters.ard,
-                lengthscale_prior=lengthscale_prior,
-            )
-        else:
-            raise ValueError(f"Kernel {hyperparameters.kernel} not known.")
-
 
 class MultiTaskGPSurrogate(TrainableBotorchSurrogate):
     type: Literal["MultiTaskGPSurrogate"] = "MultiTaskGPSurrogate"
@@ -101,6 +60,10 @@ class MultiTaskGPSurrogate(TrainableBotorchSurrogate):
         default_factory=lambda: MultiTaskGPHyperconfig(),
     )
 
+    @property
+    def hyperconfig_access(self) -> Optional[Hyperconfig]:
+        return self.hyperconfig
+
     @classmethod
     def _default_categorical_encodings(
         cls,
@@ -155,3 +118,45 @@ def validate_encoding(cls, v, info):
             )
 
         return v
+
+    def update_hyperparameters(
+        self,
+        hyperparameters: pd.Series,
+    ):
+        super().update_hyperparameters(hyperparameters)
+
+        def matern_25(ard: bool, lengthscale_prior: AnyPrior) -> MaternKernel:
+            return MaternKernel(nu=2.5, lengthscale_prior=lengthscale_prior, ard=ard)
+
+        def matern_15(ard: bool, lengthscale_prior: AnyPrior) -> MaternKernel:
+            return MaternKernel(nu=1.5, lengthscale_prior=lengthscale_prior, ard=ard)
+
+        if hyperparameters.prior == "mbo":
+            noise_prior, lengthscale_prior = (
+                MBO_NOISE_PRIOR(),
+                MBO_LENGTHSCALE_PRIOR(),
+            )
+        else:
+            noise_prior, lengthscale_prior = (
+                THREESIX_NOISE_PRIOR(),
+                THREESIX_LENGTHSCALE_PRIOR(),
+            )
+
+        self.noise_prior = noise_prior
+        if hyperparameters.kernel == "rbf":
+            self.kernel = RBFKernel(
+                ard=hyperparameters.ard,
+                lengthscale_prior=lengthscale_prior,
+            )
+        elif hyperparameters.kernel == "matern_2.5":
+            self.kernel = matern_25(
+                ard=hyperparameters.ard,
+                lengthscale_prior=lengthscale_prior,
+            )
+        elif hyperparameters.kernel == "matern_1.5":
+            self.kernel = matern_15(
+                ard=hyperparameters.ard,
+                lengthscale_prior=lengthscale_prior,
+            )
+        else:
+            raise ValueError(f"Kernel {hyperparameters.kernel} not known.")