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-rw-r--r--test/test_ot.py15
-rw-r--r--test/test_smooth.py79
-rw-r--r--test/test_stochastic.py191
3 files changed, 285 insertions, 0 deletions
diff --git a/test/test_ot.py b/test/test_ot.py
index 399e549..45e777a 100644
--- a/test/test_ot.py
+++ b/test/test_ot.py
@@ -135,6 +135,21 @@ def test_lp_barycenter():
np.testing.assert_allclose(bary.sum(), 1)
+def test_free_support_barycenter():
+
+ measures_locations = [np.array([-1.]).reshape((1, 1)), np.array([1.]).reshape((1, 1))]
+ measures_weights = [np.array([1.]), np.array([1.])]
+
+ X_init = np.array([-12.]).reshape((1, 1))
+
+ # obvious barycenter location between two diracs
+ bar_locations = np.array([0.]).reshape((1, 1))
+
+ X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init)
+
+ np.testing.assert_allclose(X, bar_locations, rtol=1e-5, atol=1e-7)
+
+
@pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available")
def test_lp_barycenter_cvxopt():
diff --git a/test/test_smooth.py b/test/test_smooth.py
new file mode 100644
index 0000000..2afa4f8
--- /dev/null
+++ b/test/test_smooth.py
@@ -0,0 +1,79 @@
+"""Tests for ot.smooth model """
+
+# Author: Remi Flamary <remi.flamary@unice.fr>
+#
+# License: MIT License
+
+import numpy as np
+import ot
+import pytest
+
+
+def test_smooth_ot_dual():
+
+ # get data
+ n = 100
+ rng = np.random.RandomState(0)
+
+ x = rng.randn(n, 2)
+ u = ot.utils.unif(n)
+
+ M = ot.dist(x, x)
+
+ with pytest.raises(NotImplementedError):
+ Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='none')
+
+ Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10)
+
+ # check constratints
+ np.testing.assert_allclose(
+ u, Gl2.sum(1), atol=1e-05) # cf convergence sinkhorn
+ np.testing.assert_allclose(
+ u, Gl2.sum(0), atol=1e-05) # cf convergence sinkhorn
+
+ # kl regyularisation
+ G = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='kl', stopThr=1e-10)
+
+ # check constratints
+ np.testing.assert_allclose(
+ u, G.sum(1), atol=1e-05) # cf convergence sinkhorn
+ np.testing.assert_allclose(
+ u, G.sum(0), atol=1e-05) # cf convergence sinkhorn
+
+ G2 = ot.sinkhorn(u, u, M, 1, stopThr=1e-10)
+ np.testing.assert_allclose(G, G2, atol=1e-05)
+
+
+def test_smooth_ot_semi_dual():
+
+ # get data
+ n = 100
+ rng = np.random.RandomState(0)
+
+ x = rng.randn(n, 2)
+ u = ot.utils.unif(n)
+
+ M = ot.dist(x, x)
+
+ with pytest.raises(NotImplementedError):
+ Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='none')
+
+ Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10)
+
+ # check constratints
+ np.testing.assert_allclose(
+ u, Gl2.sum(1), atol=1e-05) # cf convergence sinkhorn
+ np.testing.assert_allclose(
+ u, Gl2.sum(0), atol=1e-05) # cf convergence sinkhorn
+
+ # kl regyularisation
+ G = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='kl', stopThr=1e-10)
+
+ # check constratints
+ np.testing.assert_allclose(
+ u, G.sum(1), atol=1e-05) # cf convergence sinkhorn
+ np.testing.assert_allclose(
+ u, G.sum(0), atol=1e-05) # cf convergence sinkhorn
+
+ G2 = ot.sinkhorn(u, u, M, 1, stopThr=1e-10)
+ np.testing.assert_allclose(G, G2, atol=1e-05)
diff --git a/test/test_stochastic.py b/test/test_stochastic.py
new file mode 100644
index 0000000..f315c88
--- /dev/null
+++ b/test/test_stochastic.py
@@ -0,0 +1,191 @@
+"""
+==========================
+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 = 300000
+ 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 = 300000
+ 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 = 300000
+ rng = np.random.RandomState(0)
+
+ x = rng.randn(n, 2)
+ u = ot.utils.unif(n)
+ zero = np.zeros(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(
+ zero, (G_sag - G_sinkhorn).sum(1), atol=1e-03) # cf convergence sag
+ np.testing.assert_allclose(
+ zero, (G_sag - G_sinkhorn).sum(0), atol=1e-03) # cf convergence sag
+ np.testing.assert_allclose(
+ zero, (G_asgd - G_sinkhorn).sum(1), atol=1e-03) # cf convergence asgd
+ np.testing.assert_allclose(
+ zero, (G_asgd - G_sinkhorn).sum(0), atol=1e-03) # cf convergence asgd
+ 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 = 300000
+ batch_size = 8
+ 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-02) # cf convergence sgd
+ np.testing.assert_allclose(
+ u, G.sum(0), atol=1e-02) # 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 = 300000
+ batch_size = 8
+ rng = np.random.RandomState(0)
+
+ x = rng.randn(n, 2)
+ u = ot.utils.unif(n)
+ zero = np.zeros(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(
+ zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-02) # cf convergence sgd
+ np.testing.assert_allclose(
+ zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-02) # cf convergence sgd
+ np.testing.assert_allclose(
+ G_sgd, G_sinkhorn, atol=1e-02) # cf convergence sgd
generated by cgit v1.2.3 (git 2.39.1) at 2024-07-02 19:58:51 +0000