Define a EMPTY_SIZE constant for allocators (facebookresearch#790)

Summary:

All inducing point allocators now use an empty_size constant as the argument default for the batch shape. This avoids the flake8 warning.

Differential Revision: D73886891

Author: JasonKChow

aepsych/models/inducing_points/base.py

@@ -13,6 +13,8 @@
 from aepsych.utils import get_dims
 from botorch.models.utils.inducing_point_allocators import InducingPointAllocator
 
+EMPTY_SIZE = torch.Size([])
+
 
 class BaseAllocator(InducingPointAllocator, ConfigurableMixin):
     """Base class for inducing point allocators."""
@@ -34,7 +36,7 @@ def allocate_inducing_points(
         inputs: torch.Tensor | None = None,
         covar_module: torch.nn.Module | None = None,
         num_inducing: int = 100,
-        input_batch_shape: torch.Size = torch.Size([]),
+        input_batch_shape: torch.Size = EMPTY_SIZE,
     ) -> torch.Tensor:
         """
         Abstract method for allocating inducing points. Must replace the

aepsych/models/inducing_points/fixed.py

@@ -8,7 +8,7 @@
 from typing import Any
 
 import torch
-from aepsych.models.inducing_points.base import BaseAllocator
+from aepsych.models.inducing_points.base import BaseAllocator, EMPTY_SIZE
 
 
 class FixedAllocator(BaseAllocator):
@@ -31,7 +31,7 @@ def allocate_inducing_points(
         inputs: torch.Tensor | None = None,
         covar_module: torch.nn.Module | None = None,
         num_inducing: int = 100,
-        input_batch_shape: torch.Size = torch.Size([]),
+        input_batch_shape: torch.Size = EMPTY_SIZE,
     ) -> torch.Tensor:
         """Allocate inducing points by returning the fixed inducing points.
 
@@ -93,7 +93,7 @@ def allocate_inducing_points(
         inputs: torch.Tensor | None = None,
         covar_module: torch.nn.Module | None = None,
         num_inducing: int = 100,
-        input_batch_shape: torch.Size = torch.Size([]),
+        input_batch_shape: torch.Size = EMPTY_SIZE,
     ) -> torch.Tensor:
         points = self.main_allocator.allocate_inducing_points(
             inputs=inputs,

aepsych/models/inducing_points/greedy_variance_reduction.py

@@ -6,7 +6,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-from aepsych.models.inducing_points.base import BaseAllocator
+from aepsych.models.inducing_points.base import BaseAllocator, EMPTY_SIZE
 from botorch.models.utils.inducing_point_allocators import (
     GreedyVarianceReduction as BaseGreedyVarianceReduction,
 )
@@ -18,7 +18,7 @@ def allocate_inducing_points(
         inputs: torch.Tensor | None = None,
         covar_module: torch.nn.Module | None = None,
         num_inducing: int = 100,
-        input_batch_shape: torch.Size = torch.Size([]),
+        input_batch_shape: torch.Size = EMPTY_SIZE,
     ) -> torch.Tensor:
         """Allocate inducing points using the GreedyVarianceReduction strategy. This is
         a thin wrapper around BoTorch's GreedyVarianceRedution inducing point allocator.

aepsych/models/inducing_points/kmeans.py

@@ -6,7 +6,7 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch
-from aepsych.models.inducing_points.base import BaseAllocator
+from aepsych.models.inducing_points.base import BaseAllocator, EMPTY_SIZE
 from scipy.cluster.vq import kmeans2
 
 
@@ -18,7 +18,7 @@ def allocate_inducing_points(
         inputs: torch.Tensor | None = None,
         covar_module: torch.nn.Module | None = None,
         num_inducing: int = 100,
-        input_batch_shape: torch.Size = torch.Size([]),
+        input_batch_shape: torch.Size = EMPTY_SIZE,
     ) -> torch.Tensor:
         """
         Generates `num_inducing` inducing points using k-means++ initialization on the input data.

aepsych/models/inducing_points/sobol.py

@@ -9,7 +9,7 @@
 
 import torch
 from aepsych.config import Config
-from aepsych.models.inducing_points.base import BaseAllocator
+from aepsych.models.inducing_points.base import BaseAllocator, EMPTY_SIZE
 from botorch.utils.sampling import draw_sobol_samples
 
 
@@ -36,7 +36,7 @@ def allocate_inducing_points(
         inputs: torch.Tensor | None = None,
         covar_module: torch.nn.Module | None = None,
         num_inducing: int = 100,
-        input_batch_shape: torch.Size = torch.Size([]),
+        input_batch_shape: torch.Size = EMPTY_SIZE,
     ) -> torch.Tensor:
         """
         Generates `num_inducing` inducing points within the specified bounds using Sobol sampling.