torch.distributions.kumaraswamy.Kumaraswamy generates samples outside its support

[kiranchari]

The Kumaraswamy distribution is defined over the open interval (0,1) but torch.distributions.kumaraswamy.Kumaraswamy can generate samples that equal 1. This causes “nan” outputs when computing the log_prob of such samples.

import torch as ch
import torch.distributions as dist
kumaraswamy = dist.kumaraswamy.Kumaraswamy(ch.Tensor([0.2]), ch.Tensor([0.2]))
samples = kumaraswamy.sample(ch.Size([1000000]))
assert len(samples[samples==1.0]) > 0

I suspect this happens to due to the use of torch’s Uniform distribution, which is defined on the half-open interval [0,1), in the sample() method: torch.distributions.transformed_distribution — PyTorch 1.13 documentation

Since torch.distributions.uniform.Uniform can generate samples that equal 0, these are perhaps transformed to samples that equal 1 for the Kumaraswamy distribution, outside its support.

[KFrank]

Hi Kiranchari!

@kiranchari

The Kumaraswamy distribution is defined over the open interval (0,1)

I think that it is legitimate to expect that pytorch’s Kumaraswamy not return
samples that are equal to 1.0 (nor 0.0).

but torch.distributions.kumaraswamy.Kumaraswamy can generate samples that equal 1.

I can reproduce this issue running your code.

Furthermore, Kumaraswamy (0.1, 0.1) generates samples of 0.0 as well
as 1.0.

(Note, performing the analogous test with Beta does not produce samples
outside of its open-interval, (0, 1) support.)

I would consider this a legitimate bug – could you file a github issue?

(@ptrblck, perhaps an expert could chime in.)

I suspect this happens to due to the use of torch’s Uniform distribution, which is defined on the half-open interval [0,1), in the sample() method

Since torch.distributions.uniform.Uniform can generate samples that equal 0

This doesn’t seem likely because your test with “only” 1,000,000 samples is
quite unlikely to sample an exact 0.0 from Uniform.

these are perhaps transformed to samples that equal 1 for the Kumaraswamy distribution, outside its support.

Note that floating-point numbers can get closer to 0.0 than to 1.0. I expect
that samples from Uniform that are close to 0.0 (but not equal) get transformed
to 1.0 (and Uniform samples that are close to 1.0 get transformed to 0.0).

Best.

K. Frank

[ptrblck]

Thanks for pinging on this issue!
I assume this Uniform usage might create these invalid values?
@kiranchari As @KFrank mentioned, would you mind creating an issue on GitHub so that we could track and fix it, please?

CC @vishwakftw for viz

[kiranchari]

Thanks @ptrblck and @KFrank. I have created an issue on Github: torch.distributions.kumaraswamy.Kumaraswamy generates samples outside its support (0,1) · Issue #95548 · pytorch/pytorch · GitHub