RatioEstimator abstraction

[bkmi]

What does this implement/fix? Explain your changes

It introduces a RatioEstimator abstraction. It will wrap neural networks that process x and theta to estimate ratios. The base class is suggestive of how to create extensions that use alternative data types (dict, etc.) while flexible enough to handle how embedding works and how embedded data is combined. Natural extensions include ratio estimators with specific network architectures such as transformers or convnets.

This implements #992 which is the ratio specific issue for #1046. It also implements the ratio part of #957 and makes the documentation confirm to the shape goals of #1041.

Does this close any currently open issues?

Closes:
#992

Also #1036 since it eliminates a confusingly name and now-irrelevant class.

The others are more general and require more work on subjects related to ratios before closing.

Any relevant code examples, logs, error output, etc?

Nope

Any other comments?

There is a test that doesn't pass, namely #1090, but I think it is not because of my changes.

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[bkmi]

I added a test. it's extremely simple, but it checks that the estimated ratios are the correct size . (like log_prob).

[tomMoral]

A few comments on the base class, to match with what has been discussed in #1046 and #1072

[bkmi]

I will follow up after Friday.

[bkmi]

@tomMoral I think this is done now.

[codecov]

Codecov Report

Attention: Patch coverage is 95.55556% with 2 lines in your changes are missing coverage. Please review.

Project coverage is 77.25%. Comparing base (46db263) to head (8bae751).
Report is 10 commits behind head on main.

❗ Current head 8bae751 differs from pull request most recent head 9c39ff1. Consider uploading reports for the commit 9c39ff1 to get more accurate results

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1097      +/-   ##
==========================================
- Coverage   85.26%   77.25%   -8.02%     
==========================================
  Files          89       90       +1     
  Lines        6616     6629      +13     
==========================================
- Hits         5641     5121     -520     
- Misses        975     1508     +533     

Flag	Coverage Δ
unittests	77.25% <95.55%> (-8.02%)	⬇️

Flags with carried forward coverage won't be shown. Click here to find out more.

Files	Coverage Δ
sbi/inference/potentials/ratio_based_potential.py	100.00% <100.00%> (ø)
sbi/inference/snre/snre_base.py	94.28% <100.00%> (ø)
sbi/neural_nets/__init__.py	100.00% <ø> (ø)
sbi/neural_nets/classifier.py	100.00% <100.00%> (ø)
sbi/neural_nets/ratio_estimators.py	92.59% <92.59%> (ø)

... and 23 files with indirect coverage changes

[tomMoral]

A few extra comments but overall it looks good!

[michaeldeistler]

Hi Ben,

thanks a lot! The shape-PR for DensityEstimators is finally merged in #1066 so we can start pushing this along. See a few questions below.

[michaeldeistler]

Please add a test that checks if 2D data (images) can be handled with appropriate embedding nets.

[janfb]

Thanks for pushing this along! 👏

I think it makes sense that @michaeldeistler reviews this.

[bkmi]

There is a wip push, but supporting sample dim will require:

• making warnings that it is not allowed to be used in loss function computations

[bkmi]

goals:

• no abstraction, only one RatioEstimator
• move RatioEstimator out of neural_nets
• primarily RatioEstimator keeps track of x and theta shapes
• simple check shapes, i.e. (1, 1, *event_shape) for log_prob equivalent.
• RatioEstimator can have embeddings
• make sure that the loss is computed in the learning algorithm
• update tests

relevant issues:
#1066
#1149