Tests + contributions

Author: tvayer

README.md

@@ -164,6 +164,7 @@ The contributors to this library are:
 * Erwan Vautier (Gromov-Wasserstein)
 * [Kilian Fatras](https://kilianfatras.github.io/)
 * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home)
+* [Vayer Titouan](https://tvayer.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):
 

ot/gromov.py

@@ -926,6 +926,10 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature
         "Optimal Transport for structured data with application on graphs"
         International Conference on Machine Learning (ICML). 2019.
     """
+    
+    class UndefinedParameter(Exception):
+        pass
+    
     S = len(Cs)
     d = Ys[0].shape[1] #dimension on the node features
     if p is None:
@@ -938,7 +942,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature
 
     if fixed_structure:
         if init_C is None:
-            C=Cs[0]
+            raise UndefinedParameter('If C is fixed it must be initialized')
         else:
             C=init_C
     else:
@@ -950,7 +954,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature
 
     if fixed_features:
         if init_X is None:
-            X=Ys[0]
+           raise UndefinedParameter('If X is fixed it must be initialized')
         else :
             X= init_X
     else:
@@ -1004,13 +1008,13 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature
         # Cs is ns,ns
         # p is N,1
         # ps is ns,1
-
+        
         T = [fused_gromov_wasserstein((1-alpha)*Ms[s],C,Cs[s],p,ps[s],loss_fun,alpha,numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)]
 
             # T is N,ns
 
         log_['Ts_iter'].append(T)
-        err_feature = np.linalg.norm(X - Xprev.reshape(d,N))
+        err_feature = np.linalg.norm(X - Xprev.reshape(N,d))
         err_structure = np.linalg.norm(C - Cprev)
 
         if log:

test/test_gromov.py

@@ -143,3 +143,78 @@ 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)
+
+    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
+
+
+def test_fgw_barycenter():
+
+    ns = 50
+    nt = 60
+
+    Xs, ys = ot.datasets.make_data_classif('3gauss', ns)
+    Xt, yt = ot.datasets.make_data_classif('3gauss2', nt)
+    
+    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,log = 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,log = 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,log = 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]))

test/test_optim.py

@@ -65,3 +65,8 @@ def df(G):
 
     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_funct(1,-1,0),0.5)
+    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1,5,0),0)
+    np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1,0.5,0),1)