When you mean test do you mean unittest or test netcdf files?
if its test netcdf files theres a couple "precompiled" https://github.com/arviz-devs/arviz/blob/master/arviz/data/datasets.py#L50
the ones in that url for example
if its unittests its all in the test folder https://github.com/arviz-devs/arviz/tree/master/arviz/data
I'm taking a quick look to see how hard it would be to generate InferenceData NetCDF directly from CmdStan (C++). If it's possible, the next decision would be software design... whether it makes sense to implement directly in CmdStan or whether it makes sense to just write a general C++ library (that conceptually could be at the ArviZ level).
For precedent on an external library arviz.jl is an example of an "external" library that provides functionality in another language https://github.com/arviz-devs/ArviZ.jl
not exactly one of the two options, but from the two you mentioned it sounds like either will work. I'm curious to see which route ends up working better
I am looking for adding log_likelihood in InferenceData from TFP trace. After computing log_likelihood, I am confused whether to add it as a variable in
sample_stats or as a separate group. I see it can be possible in both ways. But I am wondering if adding it in a certain way affects underneath computations. Any help in this regard? Thanks
and @Sayam753 great to see you here :)
I observed this channel for a few days before asking, suspecting if the community has moved to a new place.
The cookbook has a good example of this
And in case it helped, it used to be stored in the
sample_stats group with the name log_likelihood, but this approach was not really compatible with multiple likelihood functions nor with custom model comparison tasks which is why this was moved to its own group
Therefore, everything still works with both options, but the
log_likelihood group is preferred and is the only option that supports multiple likelihoods (as seen in this example)
Hey Guys!My name is Madhu.I am from India and I would like to get started contributing to ArviZ and would like to know about some getting started guide as well as some beginner issues to get a grasp of the codebase.Kindly guide me through some beginner issues as well as some Information/resources to better understand about the codebase
hey @OriolAbril can you please send the link of specific GSoC channel where we can discuss project ideas?
Welcome to the community @thesarthakbhardwaj:matrix.org, this https://gitter.im/arviz-devs/GSoC is the channel to discuss gsoc topics
Hello to everyone. After writing ggmcmc for R (https://cran.r-project.org/web/packages/ggmcmc/index.html) and keeping it alive to use it with JAGS and stan, I am trying to also start with pyMC3.
However, there is something very basic with ArviZ that is keeping me from the joy of seeing my first figure. I am using GNU/Linux and the figures do not show up. See this: https://discourse.pymc.io/t/arviz-figures-not-showing-up-after-sampling-gnu-linux/7978. I am sure it is something very simple, but would anyone suggest some kewords or ideas on how to solve it and move forward? Thank you very much.
Oh, sorry. It seems someone already pointed my towards the right direction: https://discourse.pymc.io/t/arviz-figures-not-showing-up-after-sampling-gnu-linux/7978. Anyway, very nice to be able to share ideas with you.
Hi, I am running into OOM errors with my pymc3 idata objects (during the computation of the pointwise log likelihood values), and it sounds like the arviz/dask integration may be the solution to this issue (see here https://discourse.pymc.io/t/memory-spike-at-the-end-of-the-mcmc-sampling/5669). I saw that the corresponding PR has been merged (https://github.com/arviz-devs/arviz/pull/1229) but could not yet find any example notebooks or documentation that would explain how to switch Dask integration on. Could anyone point me in the right direction? Thanks a lot!
Youre right it doenst seem weve made that notebook...... https://arviz-devs.github.io/arviz/user_guide/computation.html
i dont have a good answer off of the top of my head but give me a couple of days and ill think through this one, my apologies for the subpar experience
so heres what it looks like. Theres a Dask class in arviz.utils
I think the way to enable ask is to do
az.utils.Dask.enable_dask(**kwargs)
and give it a shot
if things dont work it would be great if you could create an issue ticket or post here, we didnt know whether people wanted dask, but postnig and issue shows it being used and what the specific errors are. With both of those we can address problems :)