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fix: check for NaNs in emd loss matrix #623

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Types of changes

This PR introduces an additional check for NaNs in the loss matrix of the emd computation. If NaNs are detected we raise an error in order to protect against segfaults in the C++ backend.

Motivation and context / Related issue

The motivation of this PR is to fail more gracefully in cases of NaN costs.
Closes #469

How has this been tested (if it applies)

Added new tests.

PR checklist

  • I have read the CONTRIBUTING document.
  • The documentation is up-to-date with the changes I made (check build artifacts).
  • All tests passed, and additional code has been covered with new tests.
  • I have added the PR and Issue fix to the RELEASES.md file.

⚠️
Some notes on the checklist above:

  • I did not find a CONTRIBUTING.md
  • While I ran all related tests, I did not run the entire suite (due to some modules missing on my system). I assume the entire suite is run in the CI?
  • Please let me know if this is something you would like a release issue for. If so please let me know.

Comment on lines 306 to 309

if np.isnan(M).any():
raise ValueError('The loss matrix should not contain NaN values.')

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Failing early here ensures that we do not segfault in the accelerated emd_c call.

I did not look too deep into the emd_c implementation, but my assumption is that this check is somewhat pessimistic. Maybe it is possible to formulate problems for which we do not need to access a subset of values in the loss matrix (possibly due to the graph being disconnected). In that case we could support NaN values in some cases. @rflamary what is your opinion on this?

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if the graph is disconnected then the parts that are not used should have an infinite value (which is ha,ndled by the C++ solver). i'm OK with not handling naNs.

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A few comments. Thanks @bobluppes for the PR

@@ -302,6 +304,9 @@ def emd(a, b, M, numItermax=100000, log=False, center_dual=True, numThreads=1, c
ot.optim.cg : General regularized OT
"""

if np.isnan(M).any():
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A problem here is that you are using numpy on arrays that might not be numpy (see backend function below). You should do the test later in the function on the OT loss marix that hhas been converted to numpy to avoid backend errors.

@bobluppes bobluppes marked this pull request as draft May 20, 2024 08:04
@rflamary rflamary marked this pull request as ready for review June 25, 2024 08:46
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Nan in metric cost matrix causes 'segmentation fault core dumped' for GW solver
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