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| author | Rémi Flamary <remi.flamary@gmail.com> | 2022-12-15 09:28:01 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-12-15 09:28:01 +0100 |
| commit | 0411ea22a96f9c22af30156b45c16ef39ffb520d (patch) | |
| tree | 7c131ad804d5b16a8c362c2fe296350a770400df /test/test_unbalanced.py | |
| parent | 8490196dcc982c492b7565e1ec4de5f75f006acf (diff) | |
[MRG] New API for OT solver (with pre-computed ground cost matrix) (#388)
* new API for OT solver
* use itertools for product of parameters
* add tests for result class
* add tests for result class
* add tests for result class last time?
* add sinkhorn
* make partial OT bckend compatible
* add TV as unbalanced flavor
* better tests
* make smoth backend compatible and add l2 tregularizatio to solve
* add reularizedd unbalanced
* add test for more complex attibutes
* add test for more complex attibutes
* add generic unbalaned solver and implement it for ot.solve
* add entropy to possible regularization
* star of documentation for ot.solv
* weird new pep8
* documenttaion for function ot.solve done
* pep8
* Update ot/solvers.py
Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>
* update release file
* Apply suggestions from code review
Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>
* add test NotImplemented
* pep8
* pep8gcmp pep8!
* compute kl in backend
* debug tensorflow kl backend
Co-authored-by: Alexandre Gramfort <alexandre.gramfort@m4x.org>
Diffstat (limited to 'test/test_unbalanced.py')
| -rw-r--r-- | test/test_unbalanced.py | 23 |
1 files changed, 23 insertions, 0 deletions
diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index fc40df0..b76d738 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -5,6 +5,7 @@ # # License: MIT License +import itertools import numpy as np import ot import pytest @@ -289,6 +290,28 @@ def test_implemented_methods(nx): method=method) +@pytest.mark.parametrize("reg_div,regm_div", itertools.product(['kl', 'l2', 'entropy'], ['kl', 'l2'])) +def test_lbfgsb_unbalanced(nx, reg_div, regm_div): + + np.random.seed(42) + + xs = np.random.randn(5, 2) + xt = np.random.randn(6, 2) + + M = ot.dist(xs, xt) + + a = ot.unif(5) + b = ot.unif(6) + + G, log = ot.unbalanced.lbfgsb_unbalanced(a, b, M, 1, 10, reg_div=reg_div, regm_div=regm_div, log=True, verbose=False) + + ab, bb, Mb = nx.from_numpy(a, b, M) + + Gb, log = ot.unbalanced.lbfgsb_unbalanced(ab, bb, Mb, 1, 10, reg_div=reg_div, regm_div=regm_div, log=True, verbose=False) + + np.testing.assert_allclose(G, nx.to_numpy(Gb)) + + def test_mm_convergence(nx): n = 100 rng = np.random.RandomState(42) |