gp output scaler

bofire/data_models/surrogates/fully_bayesian.py

@@ -2,17 +2,14 @@
 
 from pydantic import conint, validator
 
-from bofire.data_models.surrogates.botorch import BotorchSurrogate
-from bofire.data_models.surrogates.scaler import ScalerEnum
-from bofire.data_models.surrogates.trainable import TrainableSurrogate
+from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
-class SaasSingleTaskGPSurrogate(BotorchSurrogate, TrainableSurrogate):
+class SaasSingleTaskGPSurrogate(TrainableBotorchSurrogate):
     type: Literal["SaasSingleTaskGPSurrogate"] = "SaasSingleTaskGPSurrogate"
     warmup_steps: conint(ge=1) = 256  # type: ignore
     num_samples: conint(ge=1) = 128  # type: ignore
     thinning: conint(ge=1) = 16  # type: ignore
-    scaler: ScalerEnum = ScalerEnum.NORMALIZE
 
     @validator("thinning")
     def validate_thinning(cls, value, values):

bofire/data_models/surrogates/linear.py

@@ -6,14 +6,11 @@
 from bofire.data_models.priors.api import BOTORCH_NOISE_PRIOR, AnyPrior
 
 # from bofire.data_models.strategies.api import FactorialStrategy
-from bofire.data_models.surrogates.botorch import BotorchSurrogate
-from bofire.data_models.surrogates.scaler import ScalerEnum
-from bofire.data_models.surrogates.trainable import TrainableSurrogate
+from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
-class LinearSurrogate(BotorchSurrogate, TrainableSurrogate):
+class LinearSurrogate(TrainableBotorchSurrogate):
     type: Literal["LinearSurrogate"] = "LinearSurrogate"
 
     kernel: LinearKernel = Field(default_factory=lambda: LinearKernel())
     noise_prior: AnyPrior = Field(default_factory=lambda: BOTORCH_NOISE_PRIOR())
-    scaler: ScalerEnum = ScalerEnum.NORMALIZE

bofire/data_models/surrogates/mixed_single_task_gp.py

@@ -9,20 +9,17 @@
     HammondDistanceKernel,
     MaternKernel,
 )
-from bofire.data_models.surrogates.botorch import BotorchSurrogate
-from bofire.data_models.surrogates.single_task_gp import ScalerEnum
-from bofire.data_models.surrogates.trainable import TrainableSurrogate
+from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
-class MixedSingleTaskGPSurrogate(BotorchSurrogate, TrainableSurrogate):
+class MixedSingleTaskGPSurrogate(TrainableBotorchSurrogate):
     type: Literal["MixedSingleTaskGPSurrogate"] = "MixedSingleTaskGPSurrogate"
     continuous_kernel: AnyContinuousKernel = Field(
         default_factory=lambda: MaternKernel(ard=True, nu=2.5)
     )
     categorical_kernel: AnyCategoricalKernal = Field(
         default_factory=lambda: HammondDistanceKernel(ard=True)
     )
-    scaler: ScalerEnum = ScalerEnum.NORMALIZE
 
     @validator("input_preprocessing_specs")
     def validate_categoricals(cls, v, values):

bofire/data_models/surrogates/mlp.py

@@ -1,13 +1,12 @@
 from typing import Annotated, Literal, Sequence
 
-from pydantic import Field, validator
+from pydantic import Field
 
-from bofire.data_models.surrogates.botorch import BotorchSurrogate
 from bofire.data_models.surrogates.scaler import ScalerEnum
-from bofire.data_models.surrogates.trainable import TrainableSurrogate
+from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
-class MLPEnsemble(BotorchSurrogate, TrainableSurrogate):
+class MLPEnsemble(TrainableBotorchSurrogate):
     type: Literal["MLPEnsemble"] = "MLPEnsemble"
     n_estimators: Annotated[int, Field(ge=1)] = 5
     hidden_layer_sizes: Sequence = (100,)
@@ -19,23 +18,4 @@ class MLPEnsemble(BotorchSurrogate, TrainableSurrogate):
     weight_decay: Annotated[float, Field(ge=0.0)] = 0.0
     subsample_fraction: Annotated[float, Field(gt=0.0)] = 1.0
     shuffle: bool = True
-    scaler: ScalerEnum = ScalerEnum.NORMALIZE
-    output_scaler: ScalerEnum = ScalerEnum.STANDARDIZE
-
-    @validator("output_scaler")
-    def validate_output_scaler(cls, output_scaler):
-        """validates that output_scaler is a valid type
-
-        Args:
-            output_scaler (ScalerEnum): Scaler used to transform the output
-
-        Raises:
-            ValueError: when ScalerEnum.NORMALIZE is used
-
-        Returns:
-            ScalerEnum: Scaler used to transform the output
-        """
-        if output_scaler == ScalerEnum.NORMALIZE:
-            raise ValueError("Normalize is not supported as an output transform.")
-
-        return output_scaler
+    scaler: ScalerEnum = ScalerEnum.STANDARDIZE

bofire/data_models/surrogates/polynomial.py

@@ -9,17 +9,14 @@
 from bofire.data_models.priors.api import BOTORCH_NOISE_PRIOR, AnyPrior
 
 # from bofire.data_models.strategies.api import FactorialStrategy
-from bofire.data_models.surrogates.botorch import BotorchSurrogate
-from bofire.data_models.surrogates.scaler import ScalerEnum
-from bofire.data_models.surrogates.trainable import TrainableSurrogate
+from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
-class PolynomialSurrogate(BotorchSurrogate, TrainableSurrogate):
+class PolynomialSurrogate(TrainableBotorchSurrogate):
     type: Literal["PolynomialSurrogate"] = "PolynomialSurrogate"
 
     kernel: PolynomialKernel = Field(default_factory=lambda: PolynomialKernel(power=2))
     noise_prior: AnyPrior = Field(default_factory=lambda: BOTORCH_NOISE_PRIOR())
-    scaler: ScalerEnum = ScalerEnum.NORMALIZE
 
     @staticmethod
     def from_power(power: int, inputs: Inputs, outputs: Outputs):

bofire/data_models/surrogates/random_forest.py

@@ -3,11 +3,11 @@
 from pydantic import Field
 from typing_extensions import Annotated
 
-from bofire.data_models.surrogates.botorch import BotorchSurrogate
-from bofire.data_models.surrogates.trainable import TrainableSurrogate
+from bofire.data_models.surrogates.scaler import ScalerEnum
+from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
-class RandomForestSurrogate(BotorchSurrogate, TrainableSurrogate):
+class RandomForestSurrogate(TrainableBotorchSurrogate):
     type: Literal["RandomForestSurrogate"] = "RandomForestSurrogate"
 
     # hyperparams passed down to `RandomForestRegressor`
@@ -30,3 +30,5 @@ class RandomForestSurrogate(BotorchSurrogate, TrainableSurrogate):
     random_state: Optional[int] = None
     ccp_alpha: Annotated[float, Field(ge=0)] = 0.0
     max_samples: Optional[Union[int, float]] = None
+    scaler: ScalerEnum = ScalerEnum.IDENTITY
+    output_scaler: ScalerEnum = ScalerEnum.IDENTITY

bofire/data_models/surrogates/single_task_gp.py

@@ -23,9 +23,8 @@
 )
 
 # from bofire.data_models.strategies.api import FactorialStrategy
-from bofire.data_models.surrogates.botorch import BotorchSurrogate
-from bofire.data_models.surrogates.scaler import ScalerEnum
-from bofire.data_models.surrogates.trainable import Hyperconfig, TrainableSurrogate
+from bofire.data_models.surrogates.trainable import Hyperconfig
+from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
 class SingleTaskGPHyperconfig(Hyperconfig):
@@ -92,7 +91,7 @@ def matern_15(ard: bool, lengthscale_prior: AnyPrior) -> MaternKernel:
             raise ValueError(f"Kernel {hyperparameters.kernel} not known.")
 
 
-class SingleTaskGPSurrogate(BotorchSurrogate, TrainableSurrogate):
+class SingleTaskGPSurrogate(TrainableBotorchSurrogate):
     type: Literal["SingleTaskGPSurrogate"] = "SingleTaskGPSurrogate"
 
     kernel: AnyKernel = Field(
@@ -106,7 +105,6 @@ class SingleTaskGPSurrogate(BotorchSurrogate, TrainableSurrogate):
         )
     )
     noise_prior: AnyPrior = Field(default_factory=lambda: BOTORCH_NOISE_PRIOR())
-    scaler: ScalerEnum = ScalerEnum.NORMALIZE
     hyperconfig: Optional[SingleTaskGPHyperconfig] = Field(
         default_factory=lambda: SingleTaskGPHyperconfig()
     )

bofire/data_models/surrogates/tanimoto_gp.py

@@ -9,12 +9,11 @@
     BOTORCH_SCALE_PRIOR,
     AnyPrior,
 )
-from bofire.data_models.surrogates.botorch import BotorchSurrogate
 from bofire.data_models.surrogates.scaler import ScalerEnum
-from bofire.data_models.surrogates.trainable import TrainableSurrogate
+from bofire.data_models.surrogates.trainable_botorch import TrainableBotorchSurrogate
 
 
-class TanimotoGPSurrogate(BotorchSurrogate, TrainableSurrogate):
+class TanimotoGPSurrogate(TrainableBotorchSurrogate):
     type: Literal["TanimotoGPSurrogate"] = "TanimotoGPSurrogate"
 
     kernel: AnyKernel = Field(

bofire/data_models/surrogates/trainable_botorch.py

@@ -0,0 +1,28 @@
+from pydantic import validator
+
+from bofire.data_models.surrogates.botorch import BotorchSurrogate
+from bofire.data_models.surrogates.scaler import ScalerEnum
+from bofire.data_models.surrogates.trainable import TrainableSurrogate
+
+
+class TrainableBotorchSurrogate(BotorchSurrogate, TrainableSurrogate):
+    scaler: ScalerEnum = ScalerEnum.NORMALIZE
+    output_scaler: ScalerEnum = ScalerEnum.STANDARDIZE
+
+    @validator("output_scaler")
+    def validate_output_scaler(cls, output_scaler):
+        """validates that output_scaler is a valid type
+
+        Args:
+            output_scaler (ScalerEnum): Scaler used to transform the output
+
+        Raises:
+            ValueError: when ScalerEnum.NORMALIZE is used
+
+        Returns:
+            ScalerEnum: Scaler used to transform the output
+        """
+        if output_scaler == ScalerEnum.NORMALIZE:
+            raise ValueError("Normalize is not supported as an output transform.")
+
+        return output_scaler

bofire/surrogates/fully_bayesian.py

@@ -9,6 +9,7 @@
 
 from bofire.data_models.enum import OutputFilteringEnum
 from bofire.data_models.surrogates.api import SaasSingleTaskGPSurrogate as DataModel
+from bofire.data_models.surrogates.scaler import ScalerEnum
 from bofire.surrogates.botorch import BotorchSurrogate
 from bofire.surrogates.single_task_gp import get_scaler
 from bofire.surrogates.trainable import TrainableSurrogate
@@ -25,6 +26,7 @@ def __init__(
         self.num_samples = data_model.num_samples
         self.thinning = data_model.thinning
         self.scaler = data_model.scaler
+        self.output_scaler = data_model.output_scaler
         super().__init__(data_model=data_model, **kwargs)
 
     model: Optional[SaasFullyBayesianSingleTaskGP] = None
@@ -42,7 +44,9 @@ def _fit(self, X: pd.DataFrame, Y: pd.DataFrame, disable_progbar: bool = True):
         self.model = SaasFullyBayesianSingleTaskGP(
             train_X=tX,
             train_Y=tY,
-            outcome_transform=Standardize(m=1),
+            outcome_transform=Standardize(m=1)
+            if self.output_scaler == ScalerEnum.STANDARDIZE
+            else None,
             input_transform=scaler,
         )
         fit_fully_bayesian_model_nuts(

bofire/surrogates/mixed_single_task_gp.py

@@ -12,6 +12,7 @@
 import bofire.kernels.api as kernels
 from bofire.data_models.enum import CategoricalEncodingEnum, OutputFilteringEnum
 from bofire.data_models.surrogates.api import MixedSingleTaskGPSurrogate as DataModel
+from bofire.data_models.surrogates.scaler import ScalerEnum
 from bofire.surrogates.botorch import BotorchSurrogate
 from bofire.surrogates.single_task_gp import get_scaler
 from bofire.surrogates.trainable import TrainableSurrogate
@@ -27,6 +28,7 @@ def __init__(
         self.continuous_kernel = data_model.continuous_kernel
         self.categorical_kernel = data_model.categorical_kernel
         self.scaler = data_model.scaler
+        self.output_scaler = data_model.output_scaler
         super().__init__(data_model=data_model, **kwargs)
 
     model: Optional[botorch.models.MixedSingleTaskGP] = None
@@ -81,7 +83,9 @@ def _fit(self, X: pd.DataFrame, Y: pd.DataFrame):
             cat_dims=cat_dims,
             # cont_kernel_factory=self.continuous_kernel.to_gpytorch,
             cont_kernel_factory=partial(kernels.map, data_model=self.continuous_kernel),
-            outcome_transform=Standardize(m=tY.shape[-1]),
+            outcome_transform=Standardize(m=tY.shape[-1])
+            if self.output_scaler == ScalerEnum.STANDARDIZE
+            else None,
             input_transform=tf,
         )
         mll = ExactMarginalLogLikelihood(self.model.likelihood, self.model)

bofire/surrogates/random_forest.py

@@ -1,18 +1,21 @@
-import codecs
-import pickle
+import base64
+import io
 from typing import Optional
 
 import numpy as np
 import pandas as pd
 import torch
 from botorch.models.ensemble import EnsembleModel
+from botorch.models.transforms.outcome import OutcomeTransform, Standardize
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.utils.validation import check_is_fitted
 from torch import Tensor
 
 from bofire.data_models.enum import OutputFilteringEnum
 from bofire.data_models.surrogates.api import RandomForestSurrogate as DataModel
+from bofire.data_models.surrogates.scaler import ScalerEnum
 from bofire.surrogates.botorch import BotorchSurrogate
+from bofire.surrogates.single_task_gp import get_scaler
 from bofire.surrogates.trainable import TrainableSurrogate
 from bofire.utils.torch_tools import tkwargs
 
@@ -22,7 +25,11 @@ class _RandomForest(EnsembleModel):
     Predictions of the individual trees are interpreted as uncertainty.
     """
 
-    def __init__(self, rf: RandomForestRegressor):
+    def __init__(
+        self,
+        rf: RandomForestRegressor,
+        output_scaler: Optional[OutcomeTransform] = None,
+    ):
         """Constructs the model.
 
         Args:
@@ -33,6 +40,8 @@ def __init__(self, rf: RandomForestRegressor):
             raise ValueError("`rf` is not a sklearn RandomForestRegressor.")
         check_is_fitted(rf)
         self._rf = rf
+        if output_scaler is not None:
+            self.outcome_transform = output_scaler
 
     def forward(self, X: Tensor):
         r"""Compute the model output at X.
@@ -97,6 +106,8 @@ def __init__(
         self.random_state = data_model.random_state
         self.ccp_alpha = data_model.ccp_alpha
         self.max_samples = data_model.max_samples
+        self.scaler = data_model.scaler
+        self.output_scaler = data_model.output_scaler
         super().__init__(data_model=data_model, **kwargs)
 
     _output_filtering: OutputFilteringEnum = OutputFilteringEnum.ALL
@@ -110,6 +121,22 @@ def _fit(self, X: pd.DataFrame, Y: pd.DataFrame):
             Y (pd.DataFrame): Dataframe with Y values.
         """
         transformed_X = self.inputs.transform(X, self.input_preprocessing_specs)
+
+        scaler = get_scaler(self.inputs, self.input_preprocessing_specs, self.scaler, X)
+        tX = (
+            scaler.transform(torch.from_numpy(transformed_X.values)).numpy()
+            if scaler is not None
+            else transformed_X.values
+        )
+
+        if self.output_scaler == ScalerEnum.STANDARDIZE:
+            output_scaler = Standardize(m=Y.shape[-1])
+            ty = torch.from_numpy(Y.values).to(**tkwargs)
+            ty = output_scaler(ty)[0].numpy()
+        else:
+            output_scaler = None
+            ty = Y.values
+
         rf = RandomForestRegressor(
             n_estimators=self.n_estimators,
             criterion=self.criterion,
@@ -126,15 +153,19 @@ def _fit(self, X: pd.DataFrame, Y: pd.DataFrame):
             ccp_alpha=self.ccp_alpha,
             max_samples=self.max_samples,
         )
-        rf.fit(X=transformed_X.values, y=Y.values.ravel())
-        self.model = _RandomForest(rf=rf)
+        rf.fit(X=tX, y=ty.ravel())
+
+        self.model = _RandomForest(rf=rf, output_scaler=output_scaler)
+        if scaler is not None:
+            self.model.input_transform = scaler
 
     def _dumps(self) -> str:
         """Dumps the random forest to a string via pickle as this is not directly json serializable."""
-        return codecs.encode(pickle.dumps(self.model._rf), "base64").decode()  # type: ignore
+        buffer = io.BytesIO()
+        torch.save(self.model, buffer)
+        return base64.b64encode(buffer.getvalue()).decode()
 
     def loads(self, data: str):
         """Loads the actual random forest from a base64 encoded pickle bytes object and writes it to the `model` attribute."""
-        self.model = _RandomForest(
-            rf=pickle.loads(codecs.decode(data.encode(), "base64"))
-        )
+        buffer = io.BytesIO(base64.b64decode(data.encode()))
+        self.model = torch.load(buffer)

bofire/surrogates/single_task_gp.py

@@ -91,6 +91,7 @@ def __init__(
     ):
         self.kernel = data_model.kernel
         self.scaler = data_model.scaler
+        self.output_scaler = data_model.output_scaler
         self.noise_prior = data_model.noise_prior
         super().__init__(data_model=data_model, **kwargs)
 
@@ -115,7 +116,9 @@ def _fit(self, X: pd.DataFrame, Y: pd.DataFrame):
                 active_dims=list(range(tX.shape[1])),
                 ard_num_dims=1,  # this keyword is ingored
             ),
-            outcome_transform=Standardize(m=tY.shape[-1]),
+            outcome_transform=Standardize(m=tY.shape[-1])
+            if self.output_scaler == ScalerEnum.STANDARDIZE
+            else None,
             input_transform=scaler,
         )