Merge pull request #86 from tvayer/master

[MRG] Gromov-Wasserstein closed form for linesearch and integration of Fused Gromov-Wasserstein

This PR closes #82 

Thank you @tvayer for all the work.

2 files changed, 113 insertions, 8 deletions

diff --git a/test/test_gromov.py b/test/test_gromov.py
index 305ae84..70fa83f 100644
--- a/test/test_gromov.py
+++ b/test/test_gromov.py
@@ -2,6 +2,7 @@
 

 # Author: Erwan Vautier <erwan.vautier@gmail.com>

 #         Nicolas Courty <ncourty@irisa.fr>

+#         Titouan Vayer <titouan.vayer@irisa.fr>

 #

 # License: MIT License

 

@@ -15,7 +16,7 @@ def test_gromov():
     mu_s = np.array([0, 0])

     cov_s = np.array([[1, 0], [0, 1]])

 

-    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)

+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=4)

 

     xt = xs[::-1].copy()

 

@@ -36,6 +37,11 @@ def test_gromov():
     np.testing.assert_allclose(

         q, G.sum(0), atol=1e-04)  # cf convergence gromov

 

+    Id = (1 / (1.0 * n_samples)) * np.eye(n_samples, n_samples)

+

+    np.testing.assert_allclose(

+        G, np.flipud(Id), atol=1e-04)

+

     gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True)

 

     G = log['T']

@@ -55,7 +61,7 @@ def test_entropic_gromov():
     mu_s = np.array([0, 0])

     cov_s = np.array([[1, 0], [0, 1]])

 

-    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)

+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)

 

     xt = xs[::-1].copy()

 

@@ -92,12 +98,11 @@ def test_entropic_gromov():
 

 

 def test_gromov_barycenter():

-

     ns = 50

     nt = 60

 

-    Xs, ys = ot.datasets.make_data_classif('3gauss', ns)

-    Xt, yt = ot.datasets.make_data_classif('3gauss2', nt)

+    Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)

+    Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)

 

     C1 = ot.dist(Xs)

     C2 = ot.dist(Xt)

@@ -120,12 +125,11 @@ def test_gromov_barycenter():
 

 

 def test_gromov_entropic_barycenter():

-

     ns = 50

     nt = 60

 

-    Xs, ys = ot.datasets.make_data_classif('3gauss', ns)

-    Xt, yt = ot.datasets.make_data_classif('3gauss2', nt)

+    Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)

+    Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)

 

     C1 = ot.dist(Xs)

     C2 = ot.dist(Xt)

@@ -145,3 +149,98 @@ def test_gromov_entropic_barycenter():
                                                 'kl_loss', 2e-3,

                                                 max_iter=100, tol=1e-3)

     np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples))

+

+

+def test_fgw():

+

+    n_samples = 50  # nb samples

+

+    mu_s = np.array([0, 0])

+    cov_s = np.array([[1, 0], [0, 1]])

+

+    xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42)

+

+    xt = xs[::-1].copy()

+

+    ys = np.random.randn(xs.shape[0], 2)

+    yt = ys[::-1].copy()

+

+    p = ot.unif(n_samples)

+    q = ot.unif(n_samples)

+

+    C1 = ot.dist(xs, xs)

+    C2 = ot.dist(xt, xt)

+

+    C1 /= C1.max()

+    C2 /= C2.max()

+

+    M = ot.dist(ys, yt)

+    M /= M.max()

+

+    G = ot.gromov.fused_gromov_wasserstein(M, C1, C2, p, q, 'square_loss', alpha=0.5)

+

+    # check constratints

+    np.testing.assert_allclose(

+        p, G.sum(1), atol=1e-04)  # cf convergence fgw

+    np.testing.assert_allclose(

+        q, G.sum(0), atol=1e-04)  # cf convergence fgw

+

+    Id = (1 / (1.0 * n_samples)) * np.eye(n_samples, n_samples)

+

+    np.testing.assert_allclose(

+        G, np.flipud(Id), atol=1e-04)  # cf convergence gromov

+

+    fgw, log = ot.gromov.fused_gromov_wasserstein2(M, C1, C2, p, q, 'square_loss', alpha=0.5, log=True)

+

+    G = log['T']

+

+    np.testing.assert_allclose(fgw, 0, atol=1e-1, rtol=1e-1)

+

+    # check constratints

+    np.testing.assert_allclose(

+        p, G.sum(1), atol=1e-04)  # cf convergence gromov

+    np.testing.assert_allclose(

+        q, G.sum(0), atol=1e-04)  # cf convergence gromov

+

+

+def test_fgw_barycenter():

+    np.random.seed(42)

+

+    ns = 50

+    nt = 60

+

+    Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42)

+    Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42)

+

+    ys = np.random.randn(Xs.shape[0], 2)

+    yt = np.random.randn(Xt.shape[0], 2)

+

+    C1 = ot.dist(Xs)

+    C2 = ot.dist(Xt)

+

+    n_samples = 3

+    X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5,

+                                     fixed_structure=False, fixed_features=False,

+                                     p=ot.unif(n_samples), loss_fun='square_loss',

+                                     max_iter=100, tol=1e-3)

+    np.testing.assert_allclose(C.shape, (n_samples, n_samples))

+    np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))

+

+    xalea = np.random.randn(n_samples, 2)

+    init_C = ot.dist(xalea, xalea)

+

+    X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], ps=[ot.unif(ns), ot.unif(nt)], lambdas=[.5, .5], alpha=0.5,

+                                     fixed_structure=True, init_C=init_C, fixed_features=False,

+                                     p=ot.unif(n_samples), loss_fun='square_loss',

+                                     max_iter=100, tol=1e-3)

+    np.testing.assert_allclose(C.shape, (n_samples, n_samples))

+    np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))

+

+    init_X = np.random.randn(n_samples, ys.shape[1])

+

+    X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5,

+                                     fixed_structure=False, fixed_features=True, init_X=init_X,

+                                     p=ot.unif(n_samples), loss_fun='square_loss',

+                                     max_iter=100, tol=1e-3)

+    np.testing.assert_allclose(C.shape, (n_samples, n_samples))

+    np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1]))

diff --git a/test/test_optim.py b/test/test_optim.py
index dfefe59..ae31e1f 100644
--- a/test/test_optim.py
+++ b/test/test_optim.py
@@ -65,3 +65,9 @@ def test_generalized_conditional_gradient():
 
     np.testing.assert_allclose(a, G.sum(1), atol=1e-05)
     np.testing.assert_allclose(b, G.sum(0), atol=1e-05)
+
+
+def test_solve_1d_linesearch_quad_funct():
+    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(1, -1, 0), 0.5)
+    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 5, 0), 0)
+    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 0.5, 0), 1)