[WIP] quantized gromov wasserstein solver #603
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cedricvincentcuaz
changed the title
…fficient for numpy and torch
Codecov Report
Additional details and impacted files@@ Coverage Diff @@
## master #603 +/- ##
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+ Coverage 96.78% 96.79% +0.01%
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Lines 16027 16270 +243
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+ Hits 15511 15748 +237
- Misses 516 522 +6 |
rflamary
reviewed
Mar 29, 2024
| # Compute their quantized Gromov-Wasserstein distance using the wrapper | ||
| # --------------------------------------------------------- | ||
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| # 0) qGW(spC2, h2, spC3, h3) while partitioning the adjacency matrices C2 and C3 |
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I dont undestand this sentence, maybe do bity. with and without the C? Also I dont' understand what those two ways to represnet the graph are used for
| spC2, spC3, 2, 3, C2, C3, h2, h3, part_method=part_method, | ||
| rep_method=rep_method, log=True) | ||
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| qGW_dist = log['qGW_dist'] |
| # 1-a) Partition C2 and C3 in 2 and 3 clusters respectively using the Fluid | ||
| # algorithm implementation from networkx. Encode these partitions via vectors of assignments. | ||
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| part2 = _get_partition(C2, npart=2, part_method=part_method) |
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I woudl put the two steps partion then qg efore the usinuqe function so that the user undrstand better
ot/gromov/_quantized.py
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| """ | ||
| if (part_method in ['fluid', 'louvain'] or rep_method == 'pagerank') and (not networkx_import): | ||
| warnings.warn( |
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this one cna be tested with the test-minila that do not install networkx
| rep_method = 'random' | ||
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| if (part_method in ['kmeans', 'spectral'] or rep_method == 'kmeans') and (not sklearn_import): | ||
| warnings.warn( |
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Types of changes
Implementation of first solvers for the quantized Fused Gromov-Wasserstein (qFGW) distance.
ot/gromov/_quantized.pyandtest/gromov/test_quantized.py.quantized_fused_gromov_wasserstein_partitioned: main function to compute the OT between two partitioned joint spaces with minimal inputs to compute global and local alignments, so that the user can perform any partitioning and representant selection as pre-processing. A boolean optionbuild_OTallows the user to construct the OT matrix between non-partitioned spaces which might require a lot of memory. Other inputs mainly relate to innerfused_gromov_wasserstein,gromov_wassersteinandemd_1dsolvers.remark 1 : i) only the (F)GW conditional gradient solver is considered here. Note that authors also made use of the entropic projected gradient solver. ii) only the square loss is considered for both types of OT problems.
quantized_fused_gromov_wasserstein: main function to compute the OT between two joint spaces with axioms used by authors to partition both spaces. Taking as inputs structure matricesC1andC2treated as graphs, optionally as feature matricesF1andF2, optionally other structure matricesC1_auxandC2_auxused for partitioning and representant selection implemented inget_graph_partitionandget_graph_representants.quantized_fused_gromov_wasserstein_samples: main function to compute the OT between two distributions in different spaces potentially endowed with features, with axioms used by authors to partition both spaces. Taking as inputs samplesX1andX2endowed with an euclidean geometry on their respective space, optionally with feature matricesF1andF2, where both can be considered for partitioning and representant selection (e.g kmeans) implemented inget_partition_and_representants_samples.Motivation and context / Related issue
How has this been tested (if it applies)
test.test_gromov.py::test_quantized_gromovPR checklist