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Diffstat (limited to 'test/test_stochastic.py')
| -rw-r--r-- | test/test_stochastic.py | 215 |
1 files changed, 215 insertions, 0 deletions
diff --git a/test/test_stochastic.py b/test/test_stochastic.py new file mode 100644 index 0000000..f0f3fc8 --- /dev/null +++ b/test/test_stochastic.py @@ -0,0 +1,215 @@ +""" +========================== +Stochastic test +========================== + +This example is designed to test the stochatic optimization algorithms module +for descrete and semicontinous measures from the POT library. + +""" + +# Author: Kilian Fatras <kilian.fatras@gmail.com> +# +# License: MIT License + +import numpy as np +import ot + + +############################################################################# +# COMPUTE TEST FOR SEMI-DUAL PROBLEM +############################################################################# + +############################################################################# +# +# TEST SAG algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + + +def test_stochastic_sag(): + # test sag + n = 15 + reg = 1 + numItermax = 30000 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "sag", + numItermax=numItermax) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-04) # cf convergence sag + np.testing.assert_allclose( + u, G.sum(0), atol=1e-04) # cf convergence sag + + +############################################################################# +# +# TEST ASGD algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + + +def test_stochastic_asgd(): + # test asgd + n = 15 + reg = 1 + numItermax = 100000 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", + numItermax=numItermax) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-03) # cf convergence asgd + np.testing.assert_allclose( + u, G.sum(0), atol=1e-03) # cf convergence asgd + + +############################################################################# +# +# TEST Convergence SAG and ASGD toward Sinkhorn's solution +# -------------------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + + +def test_sag_asgd_sinkhorn(): + # test all algorithms + n = 15 + reg = 1 + nb_iter = 100000 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + M = ot.dist(x, x) + + G_asgd = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", + numItermax=nb_iter) + G_sag = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "sag", + numItermax=nb_iter) + G_sinkhorn = ot.sinkhorn(u, u, M, reg) + + # check constratints + np.testing.assert_allclose( + G_sag.sum(1), G_sinkhorn.sum(1), atol=1e-03) + np.testing.assert_allclose( + G_sag.sum(0), G_sinkhorn.sum(0), atol=1e-03) + np.testing.assert_allclose( + G_asgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) + np.testing.assert_allclose( + G_asgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) + np.testing.assert_allclose( + G_sag, G_sinkhorn, atol=1e-03) # cf convergence sag + np.testing.assert_allclose( + G_asgd, G_sinkhorn, atol=1e-03) # cf convergence asgd + + +############################################################################# +# COMPUTE TEST FOR DUAL PROBLEM +############################################################################# + +############################################################################# +# +# TEST SGD algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a batch_size and a number of iteration + + +def test_stochastic_dual_sgd(): + # test sgd + n = 10 + reg = 1 + numItermax = 15000 + batch_size = 10 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, + numItermax=numItermax) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-03) # cf convergence sgd + np.testing.assert_allclose( + u, G.sum(0), atol=1e-03) # cf convergence sgd + + +############################################################################# +# +# TEST Convergence SGD toward Sinkhorn's solution +# -------------------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a batch_size and a number of iteration + + +def test_dual_sgd_sinkhorn(): + # test all dual algorithms + n = 10 + reg = 1 + nb_iter = 15000 + batch_size = 10 + rng = np.random.RandomState(0) + +# Test uniform + x = rng.randn(n, 2) + u = ot.utils.unif(n) + M = ot.dist(x, x) + + G_sgd = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, + numItermax=nb_iter) + + G_sinkhorn = ot.sinkhorn(u, u, M, reg) + + # check constratints + np.testing.assert_allclose( + G_sgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) + np.testing.assert_allclose( + G_sgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) + np.testing.assert_allclose( + G_sgd, G_sinkhorn, atol=1e-03) # cf convergence sgd + +# Test gaussian + n = 30 + reg = 1 + batch_size = 30 + + a = ot.datasets.make_1D_gauss(n, 15, 5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n, 15, 5) + X_source = np.arange(n, dtype=np.float64) + Y_target = np.arange(n, dtype=np.float64) + M = ot.dist(X_source.reshape((n, 1)), Y_target.reshape((n, 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( + G_sgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) + np.testing.assert_allclose( + G_sgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) + np.testing.assert_allclose( + G_sgd, G_sinkhorn, atol=1e-03) # cf convergence sgd |