add clone method to datasets (#2625)

Summary:

This makes it far easier to obtain slices of different kinds of datasets (Supervised, MultiTask, Contextual), which will be helpful for things like doing LOOCV MBM in Ax.

Reviewed By: saitcakmak

Differential Revision: D65616941

botorch/utils/containers.py

@@ -8,10 +8,14 @@
 
 from __future__ import annotations
 
+import dataclasses
+
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, fields
 from typing import Any
 
+import torch
+
 from torch import device as Device, dtype as Dtype, LongTensor, Size, Tensor
 
 
@@ -102,6 +106,9 @@ def _validate(self) -> None:
                     f"`event shape` {self.event_shape}."
                 )
 
+    def clone(self) -> DenseContainer:
+        return dataclasses.replace(self)
+
 
 @dataclass(eq=False)
 class SliceContainer(BotorchContainer):
@@ -149,3 +156,10 @@ def _validate(self) -> None:
                 f"Shapes of `values` {values.shape} and `indices` "
                 f"{indices.shape} incompatible with `event_shape` {event_shape}."
             )
+
+    def clone(self) -> SliceContainer:
+        return type(self)(
+            values=self.values.clone(),
+            indices=self.indices.clone(),
+            event_shape=torch.Size(self.event_shape),
+        )

botorch/utils/datasets.py

@@ -8,6 +8,9 @@
 
 from __future__ import annotations
 
+import copy
+
+import warnings
 from typing import Any
 
 import torch
@@ -70,6 +73,7 @@ def __init__(
         self._Yvar = Yvar
         self.feature_names = feature_names
         self.outcome_names = outcome_names
+        self.validate_init = validate_init
         if validate_init:
             self._validate()
 
@@ -147,6 +151,50 @@ def __eq__(self, other: Any) -> bool:
             and self.outcome_names == other.outcome_names
         )
 
+    def clone(
+        self, deepcopy: bool = False, mask: Tensor | None = None
+    ) -> SupervisedDataset:
+        """Return a copy of the dataset.
+
+        Args:
+            deepcopy: If True, perform a deep copy. Otherwise, use the same tensors/lists.
+            mask: A `n`-dim boolean mask indicating which rows to keep. This is used along the -2 dimension.
+
+        Returns:
+            The new dataset.
+        """
+        new_X = self._X
+        new_Y = self._Y
+        new_Yvar = self._Yvar
+        feature_names = self.feature_names
+        outcome_names = self.outcome_names
+        if mask is not None:
+            if any(isinstance(x, BotorchContainer) for x in [new_X, new_Y, new_Yvar]):
+                raise NotImplementedError(
+                    "Masking is not supported for BotorchContainers."
+                )
+            new_X = new_X[..., mask, :]
+            new_Y = new_Y[..., mask, :]
+            if new_Yvar is not None:
+                new_Yvar = new_Yvar[..., mask, :]
+        if deepcopy:
+            new_X = new_X.clone()
+            new_Y = new_Y.clone()
+            new_Yvar = new_Yvar.clone() if new_Yvar is not None else None
+            feature_names = copy.copy(self.feature_names)
+            outcome_names = copy.copy(self.outcome_names)
+        kwargs = {}
+        if new_Yvar is not None:
+            kwargs = {"Yvar": new_Yvar}
+        return type(self)(
+            X=new_X,
+            Y=new_Y,
+            feature_names=feature_names,
+            outcome_names=outcome_names,
+            validate_init=self.validate_init,
+            **kwargs,
+        )
+
 
 class RankingDataset(SupervisedDataset):
     r"""A SupervisedDataset whose labelled pairs `(x, y)` consist of m-ary combinations
@@ -339,7 +387,7 @@ def from_joint_dataset(
                 outcome_names=[outcome_name],
             )
             datasets.append(new_dataset)
-        # Return the new
+        # Return the new dataset
         return cls(
             datasets=datasets,
             target_outcome_name=outcome_names_per_task.get(
@@ -466,6 +514,35 @@ def __eq__(self, other: Any) -> bool:
             and self.task_feature_index == other.task_feature_index
         )
 
+    def clone(
+        self, deepcopy: bool = False, mask: Tensor | None = None
+    ) -> MultiTaskDataset:
+        """Return a copy of the dataset.
+
+        Args:
+            deepcopy: If True, perform a deep copy. Otherwise, use the same tensors/lists/datasets.
+            mask: A `n`-dim boolean mask indicating which rows to keep from the target dataset. This is used along the -2 dimension.
+
+        Returns:
+            The new dataset.
+        """
+        datasets = list(self.datasets.values())
+        if mask is not None or deepcopy:
+            new_datasets = []
+            for outcome, ds in self.datasets.items():
+                new_datasets.append(
+                    ds.clone(
+                        deepcopy=deepcopy,
+                        mask=mask if outcome == self.target_outcome_name else None,
+                    )
+                )
+            datasets = new_datasets
+        return MultiTaskDataset(
+            datasets=datasets,
+            target_outcome_name=self.target_outcome_name,
+            task_feature_index=self.task_feature_index,
+        )
+
 
 class ContextualDataset(SupervisedDataset):
     """This is a contextual dataset that is constructed from either a single
@@ -627,3 +704,31 @@ def _validate_decompositions(self) -> None:
                     raise InputDataError(
                         f"{outcome} is missing in metric_decomposition."
                     )
+
+    def clone(
+        self, deepcopy: bool = False, mask: Tensor | None = None
+    ) -> ContextualDataset:
+        """Return a copy of the dataset.
+
+        Args:
+            deepcopy: If True, perform a deep copy. Otherwise, use the same tensors/lists/datasets.
+            mask: A `n`-dim boolean mask indicating which rows to keep. This is used along the -2
+                dimension. `n` here corresponds to the number of rows in an individual dataset.
+
+        Returns:
+            The new dataset.
+        """
+        datasets = list(self.datasets.values())
+        if mask is not None or deepcopy:
+            datasets = [ds.clone(deepcopy=deepcopy, mask=mask) for ds in datasets]
+        if deepcopy:
+            parameter_decomposition = copy.deepcopy(self.parameter_decomposition)
+            metric_decomposition = copy.deepcopy(self.metric_decomposition)
+        else:
+            parameter_decomposition = self.parameter_decomposition
+            metric_decomposition = self.metric_decomposition
+        return ContextualDataset(
+            datasets=datasets,
+            parameter_decomposition=parameter_decomposition,
+            metric_decomposition=metric_decomposition,
+        )