Comments (5)
There is a huge power in having easy ways to make useful pictures. My hypothesis would be that mostly we just need to make sure the raw materials (data) are available for the plots we want, and then existing plotting tools in python will allow us to make the figures. But it will be interesting to see where we end up writing a bunch of additional glue...
In addition to the ones you listed, visualizing (potentially high-dimensional) posteriors and learned embeddings will be really useful. So having ready access to t-sne, etc? Maybe doing some fancy automatic viz for distributions?
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A small amount of glue that would probably get us most of the tools we need (and which would be quite valuable independently of everything else): rendering matplotlib figure objects in a custom Pane type in visdom via mpld3.
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As I am writing this I am taking a crack at implementing a first version of a variational t-sne in pyro.
If I get it working, we could consider turning it into a compactified little package that can be called in a library to help visualization purposes.
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A small amount of glue that would probably get us most of the tools we need (and which would be quite valuable independently of everything else): rendering matplotlib figure objects in a custom Pane type in visdom via mpld3.
Looks like that's a thing now, awesome!
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Closing in favor of more targeted discussion in future
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Related Issues (20)
- Add detailed descriptions of `Message` and `InferDict` keys HOT 2
- [FR] Tutorial on PyroModule + ELBO module training with torch optimizers
- [discussion] Time Complexity of calculating ITEs in CEVAE HOT 1
- VS Code mypy warnings due to Pyro's type hints HOT 1
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- Use of the outdated 'jupyter' metapackage HOT 3
- [FR] Predictive with deterministic site in the guide HOT 6
- How to confirm the training is converged? HOT 1
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- Add Helper Function for evaluating log_likelihood HOT 4
- Add effect handler that forces sample sites to sample the same value [feature request] HOT 1
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