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[WIP] Gaussian initialization for sinkhorn - #555

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rflamary wants to merge 1 commit into
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gauss_init
Open

[WIP] Gaussian initialization for sinkhorn #555
rflamary wants to merge 1 commit into
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gauss_init

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@rflamary

@rflamary rflamary commented Nov 2, 2023 •
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This is a first shot for intializing empirical sinkhorn but the computational gain is not very clear on tests I did locally.

The following code

n = 2000
rng = np.random.RandomState(0)
x = rng.randn(n, 2)
x2 = 10*rng.randn(n//2, 2)
x2[:,0]+=2
ot.tic()
G, log = ot.empirical_sinkhorn(x,x2, 1, method='sinkhorn_log', warmstart=None, verbose=False, isLazy=False, stopThr=1e-5, log = True)
ot.toc()
print("Err=",log['err'][-1], "niter=", log['niter'])
ot.tic()
G2, log2 = ot.empirical_sinkhorn(x,x2, 1, method='sinkhorn_log', warmstart='gaussian', verbose=False, isLazy=False, stopThr=1e-5, log = True)
ot.toc()
print("Err=",log2['err'][-1], "niter=", log2['niter'])

give sthe following output

Elapsed time : 3.0527355670928955 s
Err= 9.441113655553818e-06 niter= 140
Elapsed time : 2.391462564468384 s
Err= 9.89690596643644e-06 niter= 110`

Quite far from the computational gains in the paper. Will investigate it more.

Motivation and context / Related issue

How has this been tested (if it applies)

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codecov Bot commented Nov 2, 2023 •
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Codecov Report

❌ Patch coverage is 91.83673% with 4 lines in your changes missing coverage. Please review.
✅ Project coverage is 83.70%. Comparing base (53dde7a) to head (b3be5a6).
⚠️ Report is 136 commits behind head on master.

❗ There is a different number of reports uploaded between BASE (53dde7a) and HEAD (b3be5a6). Click for more details.

HEAD has 12 uploads less than BASE
Flag BASE (53dde7a) HEAD (b3be5a6)
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@@ Coverage Diff @@
## master #555 +/- ##
===========================================
- Coverage 96.49% 83.70% -12.79% 
===========================================
 Files 67 67 
 Lines 14663 14708 +45 
===========================================
- Hits 14149 12312 -1837 
- Misses 514 2396 +1882
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I believe that your implementation is correct, i am not sure how authors handle the bias in empirical_bures_wasserstein_mapping set by default to True in POT.
From the experiments in the paper I would say that gains seem specific to low regimes for the entropic regularization, did you check that ?

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will do. iI have students looking into this. Will come ack to the PR after.
cedricvincentcuaz reacted with thumbs up emoji

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