get_probabilistic_reparameterization_input_transform gives incorrect dimension when optimising over only categorical inputs #2
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I'm trying to optimise a function which just has categorical inputs. However I think the get_probabilistic_reparameterization_input_transform functions appears to fail in this case.
Here is an example with one continuous feature and two categorical features.
import torch
from discrete_mixed_bo.probabilistic_reparameterization import get_probabilistic_reparameterization_input_transform
input_transform = get_probabilistic_reparameterization_input_transform(
dim=13, ##
use_analytic=True,
integer_indices=[],
integer_bounds=torch.zeros((2, 0), dtype=torch.float64),
categorical_features=OrderedDict([(1, 6), (2, 6)]),
tau=0.1,
)
print(input_transform(torch.rand(100, 1, 1, 13)).shape)
This will output torch.Size([100, 36, 1, 13]). Now if we remove the continuous feature.
import torch
from discrete_mixed_bo.probabilistic_reparameterization import get_probabilistic_reparameterization_input_transform
input_transform = get_probabilistic_reparameterization_input_transform(
dim=12, ##
use_analytic=True,
integer_indices=[],
integer_bounds=torch.zeros((2, 0), dtype=torch.float64),
categorical_features=OrderedDict([(0, 6), (1, 6)]),
tau=0.1,
)
print(input_transform(torch.rand(100, 1, 1, 12)).shape)
And the output is torch.Size([36, 12]). Should this not be torch.Size([100, 36, 1, 12])?
I've managed to find the line that seems to cause this issue. It is the tf.eval() on line 95. I'm not sure why this causes the transform to collapse the dimensions.
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