hi @j-wilson, thanks for spotting this!
do you perhaps fancy putting up a PR to fix this? :)

Thanks for raising the issue!

As far as I understand, randomize_hyperparameters sets hyperparameters to random samples from the constrained domain if there is a (Sigmoid) constraint, and uses the hyper priors only if there isn't (eg see #297). Could you please describe your use case here, so I can better understand how to test this?

@uri-granta Sure. Here's a quick example:

import math
import gpflow
import tensorflow_probability as tfp
from trieste.models.gpflow.utils import randomize_hyperparameters

kernel = gpflow.kernels.Matern52()
kernel.variance = gpflow.base.Parameter(
    value=0.1,
    prior=tfp.distributions.Uniform(math.log(0.01), math.log(10.0)),
    prior_on=gpflow.base.PriorOn.UNCONSTRAINED,
    transform=tfp.bijectors.Exp(),
)
randomize_hyperparameters(kernel)

Depending on whether or not the draw from the prior is positive, this code either produces an error

tensorflow.python.framework.errors_impl.InvalidArgumentError: {{function_node __wrapped__CheckNumerics_device_/job:localhost/replica:0/task:0/device:CPU:0}} gpflow.Parameter: the value to be assigned is incompatible with this parameter's transform (the corresponding unconstrained value has NaN or Inf) and hence cannot be assigned. : Tensor had NaN values [Op:CheckNumerics]

or silently assigns an incorrect value to kernel.variance.unconstrained_variable (incorrect because the sample will be mistakenly pulled backward through tfp.bijectors.Exp prior to being assigned).

Thanks! Out of curiosity, does anyone know why in the case of sigmoid transforms we prioritise sampling from the constrained domain over using any priors? (cc @vpicheny @hstojic)

Closing this issue as it's now been fixed. However, will follow up #794 for automatically extracting parameter bounds and create a separate issue if appropriate.