Add type checks to batch size validators

Author: jp-dark

src/tiledbsoma_ml/dataset.py

@@ -11,7 +11,7 @@
 import torch
 from attr import evolve
 from attrs import define, field
-from attrs.validators import gt
+from attrs.validators import and_, instance_of, gt
 from tiledbsoma import ExperimentAxisQuery
 from torch.utils.data import IterableDataset
 
@@ -117,9 +117,9 @@ class ExperimentDataset(IterableDataset[MiniBatch]):  # type: ignore[misc]
     """Names of ``obs`` columns to return."""
 
     # Configuration fields with defaults
-    batch_size: int = field(default=1, validator=gt(0))
+    batch_size: int = field(default=1, validator=and_(instance_of(int), gt(0)))
     """Number of rows of ``X`` and ``obs`` data to yield in each |MiniBatch|."""
-    io_batch_size: int = field(default=DEFAULT_IO_BATCH_SIZE, validator=gt(0))
+    io_batch_size: int = field(default=DEFAULT_IO_BATCH_SIZE, validator=and_(instance_of(int), gt(0)))
     """Number of ``obs``/``X`` rows to fetch together, when reading from the provided |ExperimentAxisQuery|."""
     shuffle: bool = field(default=True)
     """Whether to shuffle the ``obs`` and ``X`` data being returned."""
@@ -223,9 +223,7 @@ def __init__(
         """
         if query and layer_name:
             if x_locator or query_ids:
-                raise ValueError(
-                    "Expected `{query,layer_name}` xor `{x_locator,query_ids}`"
-                )
+                raise ValueError("Expected `{query,layer_name}` xor `{x_locator,query_ids}`")
             query_ids = QueryIDs.create(query=query)
             x_locator = XLocator.create(
                 query.experiment,
@@ -234,13 +232,9 @@ def __init__(
             )
         elif x_locator and query_ids:
             if query or layer_name:
-                raise ValueError(
-                    "Expected `{query,layer_name}` xor `{x_locator,query_ids}`"
-                )
+                raise ValueError("Expected `{query,layer_name}` xor `{x_locator,query_ids}`")
         else:
-            raise ValueError(
-                "Expected `{query,layer_name}` xor `{x_locator,query_ids}`"
-            )
+            raise ValueError("Expected `{query,layer_name}` xor `{x_locator,query_ids}`")
 
         self.__attrs_init__(
             x_locator=x_locator,
@@ -264,14 +258,10 @@ def __attrs_post_init__(self) -> None:
         if self.shuffle:
             # Verify `io_batch_size` is a multiple of `shuffle_chunk_size`
             if self.io_batch_size % self.shuffle_chunk_size:
-                raise ValueError(
-                    f"{self.io_batch_size=} is not a multiple of {self.shuffle_chunk_size=}"
-                )
+                raise ValueError(f"{self.io_batch_size=} is not a multiple of {self.shuffle_chunk_size=}")
 
         if self.seed is None:
-            object.__setattr__(
-                self, "seed", np.random.default_rng().integers(0, 2**32 - 1)
-            )
+            object.__setattr__(self, "seed", np.random.default_rng().integers(0, 2**32 - 1))
 
         # Set distributed state
         rank, world_size = get_distributed_rank_and_world_size()
@@ -425,6 +415,4 @@ def set_epoch(self, epoch: int) -> None:
         self.epoch = epoch
 
     def __getitem__(self, index: int) -> MiniBatch:
-        raise NotImplementedError(
-            "`Experiment` can only be iterated - does not support mapping"
-        )
+        raise NotImplementedError("`Experiment` can only be iterated - does not support mapping")