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| -rw-r--r-- | README.md | 7 | ||||
| -rw-r--r-- | test/test_stochastic.py | 47 |
2 files changed, 43 insertions, 11 deletions
@@ -24,6 +24,7 @@ It provides the following solvers: * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized free support Wasserstein barycenters [20]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -163,7 +164,7 @@ The contributors to this library are: * [Stanislas Chambon](https://slasnista.github.io/) * [Antoine Rolet](https://arolet.github.io/) * Erwan Vautier (Gromov-Wasserstein) -* [Kilian Fatras](https://kilianfatras.github.io/) (Stochastic optimization) +* [Kilian Fatras](https://kilianfatras.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -222,6 +223,8 @@ You can also post bug reports and feature requests in Github issues. Make sure t [17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). -[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](arXiv preprint arxiv:1605.08527). Advances in Neural Information Processing Systems (2016). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016). [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning diff --git a/test/test_stochastic.py b/test/test_stochastic.py index f315c88..88ad666 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -137,8 +137,8 @@ def test_stochastic_dual_sgd(): # test sgd n = 10 reg = 1 - numItermax = 300000 - batch_size = 8 + numItermax = 15000 + batch_size = 10 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -151,9 +151,9 @@ def test_stochastic_dual_sgd(): # check constratints np.testing.assert_allclose( - u, G.sum(1), atol=1e-02) # cf convergence sgd + u, G.sum(1), atol=1e-04) # cf convergence sgd np.testing.assert_allclose( - u, G.sum(0), atol=1e-02) # cf convergence sgd + u, G.sum(0), atol=1e-04) # cf convergence sgd ############################################################################# @@ -168,10 +168,11 @@ def test_dual_sgd_sinkhorn(): # test all dual algorithms n = 10 reg = 1 - nb_iter = 300000 - batch_size = 8 + nb_iter = 150000 + batch_size = 10 rng = np.random.RandomState(0) +# Test uniform x = rng.randn(n, 2) u = ot.utils.unif(n) zero = np.zeros(n) @@ -184,8 +185,36 @@ def test_dual_sgd_sinkhorn(): # check constratints np.testing.assert_allclose( - zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-02) # cf convergence sgd + zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-04) # cf convergence sgd + np.testing.assert_allclose( + zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-04) # cf convergence sgd + np.testing.assert_allclose( + G_sgd, G_sinkhorn, atol=1e-04) # cf convergence sgd + +# Test gaussian + n = 30 + n_source = n + n_target = n + reg = 1 + numItermax = 150000 + batch_size = 30 + + a = ot.datasets.get_1D_gauss(n_source, m=15, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n_target, m=15, s=5) + X_source = np.arange(n_source,dtype=np.float64) + Y_target = np.arange(n_target,dtype=np.float64) + M = ot.dist(X_source.reshape((n_source, 1)), Y_target.reshape((n_target, 1))) + M /= M.max() + + G_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, + numItermax=nb_iter) + + G_sinkhorn = ot.sinkhorn(a, b, M, reg) + + # check constratints + np.testing.assert_allclose( + zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-04) # cf convergence sgd np.testing.assert_allclose( - zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-02) # cf convergence sgd + zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-04) # cf convergence sgd np.testing.assert_allclose( - G_sgd, G_sinkhorn, atol=1e-02) # cf convergence sgd + G_sgd, G_sinkhorn, atol=1e-04) # cf convergence sgd |