Missing band-passes

[JohannesBuchner]

Hi all,

@jrleja @joshspeagle pointed me here.

Thank you for this repository.

I was able to train a SED model with it, but cannot do inference with it yet.

I am confused about this line: anpe._x_shape = Ut.x_shape_from_simulation(y_tensor) which causes a "AssertionError: Observed data shape (torch.Size([1, 33])) must match the shape of simulated data x (torch.Size([1, 29]))." for me when doing:

            x = np.concatenate([y_obs, sig_obs])
            ave_theta = hatp_x_y.sample((run_params['np_baseline'],), x=torch.as_tensor(x.astype(np.float32)).to(device), show_progress_bars=False)

I would expect that hatp_x_y._x_shape has to take the shape of x_tensor[0], given that we insert data values and want posteriors of shape of the parameters, but it is set to the latter?

Secondly, I was wondering about an alternative approach to deal with missing values, namely to set fluxes and errors to some special negative value (e.g. -1) randomly (proportional to their missing fraction) and train with such a modified data set, or introduce an additional indicator vector {0, 1} that indicates whether the observation is present. Then one would not need MC later (which requires ordering of a 1d data set and assumption that there are no strong emission/absorption lines). Have you tried this approach and dismissed it for some reason, does it not work? It seems to me that it would need fewer code lines.

Finally, if I see well, this code is a wrapper around https://www.mackelab.org/sbi/reference/#sbi.inference.snpe.snpe_c.SNPE_C and adds handling missing data. It would be good to encourage users to also cite the original work on SNPE_C listed there and perhaps other foundation papers. For an example of a suggested list of references, see https://johannesbuchner.github.io/UltraNest/issues.html#how-should-i-cite-ultranest