Don't allow unused keyword arguments in Model.construct_inputs

botorch/models/contextual.py

@@ -47,7 +47,6 @@ def construct_inputs(
         cls,
         training_data: SupervisedDataset,
         decomposition: Dict[str, List[int]],
-        **kwargs: Any,
     ) -> Dict[str, Any]:
         r"""Construct `Model` keyword arguments from a dict of `SupervisedDataset`.
 
@@ -56,7 +55,7 @@ def construct_inputs(
             decomposition: Dictionary of context names and their indexes of the
                 corresponding active context parameters.
         """
-        base_inputs = super().construct_inputs(training_data=training_data, **kwargs)
+        base_inputs = super().construct_inputs(training_data=training_data)
         return {
             **base_inputs,
             "decomposition": decomposition,
@@ -127,7 +126,6 @@ def construct_inputs(
         embs_feature_dict: Optional[Dict] = None,
         embs_dim_list: Optional[List[int]] = None,
         context_weight_dict: Optional[Dict] = None,
-        **kwargs: Any,
     ) -> Dict[str, Any]:
         r"""Construct `Model` keyword arguments from a dict of `SupervisedDataset`.
 
@@ -147,7 +145,7 @@ def construct_inputs(
                 dimension is set to 1 for each categorical variable.
             context_weight_dict: Known population weights of each context.
         """
-        base_inputs = super().construct_inputs(training_data=training_data, **kwargs)
+        base_inputs = super().construct_inputs(training_data=training_data)
         index_decomp = {
             c: [training_data.feature_names.index(i) for i in v]
             for c, v in decomposition.items()

botorch/models/gp_regression.py

@@ -31,7 +31,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import NoReturn, Optional
+from typing import Dict, NoReturn, Optional, Union
 
 import torch
 from botorch.models.gpytorch import BatchedMultiOutputGPyTorchModel
@@ -44,6 +44,8 @@
     get_matern_kernel_with_gamma_prior,
     MIN_INFERRED_NOISE_LEVEL,
 )
+from botorch.utils.containers import BotorchContainer
+from botorch.utils.datasets import SupervisedDataset
 from gpytorch.constraints.constraints import GreaterThan
 from gpytorch.distributions.multivariate_normal import MultivariateNormal
 from gpytorch.likelihoods.gaussian_likelihood import (
@@ -207,6 +209,31 @@ def __init__(
             self.input_transform = input_transform
         self.to(train_X)
 
+    @classmethod
+    def construct_inputs(
+        cls, training_data: SupervisedDataset, *, task_feature: Optional[int] = None
+    ) -> Dict[str, Union[BotorchContainer, Tensor]]:
+        r"""Construct `SingleTaskGP` keyword arguments from a `SupervisedDataset`.
+
+        Args:
+            training_data: A `SupervisedDataset`, with attributes `train_X`,
+                `train_Y`, and, optionally, `train_Yvar`.
+            task_feature: Deprecated and allowed only for backward
+                compatibility; ignored.
+
+        Returns:
+            A dict of keyword arguments that can be used to initialize a `SingleTaskGP`,
+            with keys `train_X`, `train_Y`, and, optionally, `train_Yvar`.
+        """
+        if task_feature is not None:
+            warnings.warn(
+                "`task_feature` is deprecated and will be ignored. In the "
+                "future, this will be an error.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+        return super().construct_inputs(training_data=training_data)
+
     def forward(self, x: Tensor) -> MultivariateNormal:
         if self.training:
             x = self.transform_inputs(x)

botorch/models/gp_regression_fidelity.py

@@ -167,15 +167,14 @@ def construct_inputs(
         cls,
         training_data: SupervisedDataset,
         fidelity_features: List[int],
-        **kwargs,
     ) -> Dict[str, Any]:
         r"""Construct `Model` keyword arguments from a dict of `SupervisedDataset`.
 
         Args:
             training_data: Dictionary of `SupervisedDataset`.
             fidelity_features: Index of fidelity parameter as input columns.
         """
-        inputs = super().construct_inputs(training_data=training_data, **kwargs)
+        inputs = super().construct_inputs(training_data=training_data)
         inputs["data_fidelities"] = fidelity_features
         return inputs
 

botorch/models/gp_regression_mixed.py

@@ -187,7 +187,6 @@ def construct_inputs(
         training_data: SupervisedDataset,
         categorical_features: List[int],
         likelihood: Optional[Likelihood] = None,
-        **kwargs: Any,
     ) -> Dict[str, Any]:
         r"""Construct `Model` keyword arguments from a dict of `SupervisedDataset`.
 
@@ -196,7 +195,7 @@ def construct_inputs(
             categorical_features: Column indices of categorical features.
             likelihood: Optional likelihood used to constuct the model.
         """
-        base_inputs = super().construct_inputs(training_data=training_data, **kwargs)
+        base_inputs = super().construct_inputs(training_data=training_data)
         return {
             **base_inputs,
             "cat_dims": categorical_features,

botorch/models/model.py

@@ -186,15 +186,13 @@ def condition_on_observations(self, X: Tensor, Y: Tensor, **kwargs: Any) -> Mode
     def construct_inputs(
         cls,
         training_data: SupervisedDataset,
-        **kwargs: Any,
     ) -> Dict[str, Union[BotorchContainer, Tensor]]:
         """
         Construct `Model` keyword arguments from a `SupervisedDataset`.
 
         Args:
             training_data: A `SupervisedDataset`, with attributes `train_X`,
                 `train_Y`, and, optionally, `train_Yvar`.
-            kwargs: Ignored.
 
         Returns:
             A dict of keyword arguments that can be used to initialize a `Model`,

botorch/models/multitask.py

@@ -334,7 +334,6 @@ def construct_inputs(
         task_covar_prior: Optional[Prior] = None,
         prior_config: Optional[dict] = None,
         rank: Optional[int] = None,
-        **kwargs,
     ) -> Dict[str, Any]:
         r"""Construct `Model` keyword arguments from a dataset and other args.
 
@@ -367,9 +366,8 @@ def construct_inputs(
                 raise ValueError(f"eta must be a real number, your eta was {eta}.")
             task_covar_prior = LKJCovariancePrior(num_tasks, eta, sd_prior)
 
-        base_inputs = super().construct_inputs(
-            training_data=training_data, task_feature=task_feature, **kwargs
-        )
+        # Call Model.construct_inputs to parse training data
+        base_inputs = super().construct_inputs(training_data=training_data)
         if isinstance(training_data, MultiTaskDataset):
             all_tasks = list(range(len(training_data.datasets)))
             base_inputs["all_tasks"] = all_tasks

botorch/models/pairwise_gp.py

@@ -781,15 +781,13 @@ def batch_shape(self) -> torch.Size:
     def construct_inputs(
         cls,
         training_data: SupervisedDataset,
-        **kwargs: Any,
     ) -> Dict[str, Tensor]:
         r"""
         Construct `Model` keyword arguments from a `RankingDataset`.
 
         Args:
             training_data: A `RankingDataset`, with attributes `train_X`,
                 `train_Y`, and, optionally, `train_Yvar`.
-            kwargs: Ignored.
 
         Returns:
             A dict of keyword arguments that can be used to initialize a

test/models/test_gp_regression.py

@@ -36,7 +36,7 @@
 from gpytorch.priors import GammaPrior
 
 
-class TestSingleTaskGP(BotorchTestCase):
+class TestGPRegressionBase(BotorchTestCase):
     def _get_model_and_data(
         self,
         batch_shape,
@@ -398,6 +398,28 @@ def test_set_transformed_inputs(self):
             self.assertEqual(X.shape, tf_X.shape)
 
 
+class TestSingleTaskGP(TestGPRegressionBase):
+    model_class = SingleTaskGP
+
+    def test_construct_inputs_task_feature_deprecated(self) -> None:
+        model, model_kwargs = self._get_model_and_data(
+            batch_shape=torch.Size([]),
+            m=1,
+            device=self.device,
+            dtype=torch.double,
+        )
+        X = model_kwargs["train_X"]
+        Y = model_kwargs["train_Y"]
+        training_data = SupervisedDataset(
+            X,
+            Y,
+            feature_names=[f"x{i}" for i in range(X.shape[-1])],
+            outcome_names=["y"],
+        )
+        with self.assertWarnsRegex(DeprecationWarning, "`task_feature` is deprecated"):
+            model.construct_inputs(training_data, task_feature=0)
+
+
 class TestFixedNoiseGP(TestSingleTaskGP):
     model_class = FixedNoiseGP
 
@@ -542,7 +564,7 @@ class TestFixedNoiseSingleTaskGP(TestFixedNoiseGP):
     model_class = SingleTaskGP
 
 
-class TestHeteroskedasticSingleTaskGP(TestSingleTaskGP):
+class TestHeteroskedasticSingleTaskGP(TestGPRegressionBase):
     def _get_model_and_data(
         self, batch_shape, m, outcome_transform=None, input_transform=None, **tkwargs
     ):