Python 3.11 compliance for dataclasses: Don't use mutable defaults (#…

…1927)

Summary:
## Motivation

See error here: https://github.com/pytorch/botorch/actions/runs/5508336054/jobs/10039481686?pr=1924
* Tested with a tensor instead of a `DenseContainer` since we needed something non-mutable
* Added a test that the tensor is properly cast to a `DenseContainer`
* made code more readable (for me anyway)

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes

Pull Request resolved: #1927

Test Plan:
Units

## Related PRs

Unblocks #1924

Reviewed By: saitcakmak

Differential Revision: D47341881

Pulled By: esantorella

fbshipit-source-id: 64c497504bffde8cc227dff8eaaa85e17aa36b95

botorch/utils/datasets.py

@@ -37,28 +37,29 @@ def __call__(cls, *args: Any, **kwargs: Any):
  r"""Converts Tensor-valued fields to DenseContainer under the assumption
  that said fields house collections of feature vectors."""
  hints = get_type_hints(cls)
- f_iter = filter(lambda f: f.init, fields(cls))
+ fields_iter = (item for item in fields(cls) if item.init is not None)
  f_dict = {}
- for obj, f in chain(
- zip(args, f_iter), ((kwargs.pop(f.name, MISSING), f) for f in f_iter)
+ for value, field in chain(
+ zip(args, fields_iter),
+ ((kwargs.pop(field.name, MISSING), field) for field in fields_iter),
  ):
- if obj is MISSING:
- if f.default is not MISSING:
- obj = f.default
- elif f.default_factory is not MISSING:
- obj = f.default_factory()
+ if value is MISSING:
+ if field.default is not MISSING:
+ value = field.default
+ elif field.default_factory is not MISSING:
+ value = field.default_factory()
  else:
- raise RuntimeError(f"Missing required field `{f.name}`.")
+ raise RuntimeError(f"Missing required field `{field.name}`.")
 
- if issubclass(hints[f.name], BotorchContainer):
- if isinstance(obj, Tensor):
- obj = DenseContainer(obj, event_shape=obj.shape[-1:])
- elif not isinstance(obj, BotorchContainer):
+ if issubclass(hints[field.name], BotorchContainer):
+ if isinstance(value, Tensor):
+ value = DenseContainer(value, event_shape=value.shape[-1:])
+ elif not isinstance(value, BotorchContainer):
  raise TypeError(
- f"Expected <BotorchContainer | Tensor> for field `{f.name}` "
- f"but was {type(obj)}."
+ "Expected <BotorchContainer | Tensor> for field "
+ f"`{field.name}` but was {type(value)}."
  )
- f_dict[f.name] = obj
+ f_dict[field.name] = value
 
  return super().__call__(**f_dict, **kwargs)
 

test/utils/test_datasets.py

@@ -30,14 +30,15 @@ def test_base(self):
  def test_supervised_meta(self):
  X = rand(3, 2)
  Y = rand(3, 1)
- A = DenseContainer(rand(3, 5), event_shape=Size([5]))
+ t = rand(3, 5)
+ A = DenseContainer(t, event_shape=Size([5]))
  B = rand(2, 1)
 
  SupervisedDatasetWithDefaults = make_dataclass(
  cls_name="SupervisedDatasetWithDefaults",
  bases=(SupervisedDataset,),
  fields=[
- ("default", DenseContainer, field(default=A)),
+ ("default", DenseContainer, field(default=t)),
  ("factory", DenseContainer, field(default_factory=lambda: A)),
  ("other", Tensor, field(default_factory=lambda: B)),
  ],
@@ -55,6 +56,7 @@ def test_supervised_meta(self):
 
  # Check handling of default values and factories
  dataset = SupervisedDatasetWithDefaults(X=X, Y=Y)
+ self.assertIsInstance(dataset.default, DenseContainer)
  self.assertEqual(dataset.default, A)
  self.assertEqual(dataset.factory, A)
  self.assertTrue(dataset.other is B)