From e1bd94bb7e85a0d2fd0fcd7642b06da12c1db6db Mon Sep 17 00:00:00 2001
From: tvayer <titouan.vayer@gmail.com>
Date: Wed, 29 May 2019 17:05:38 +0200
Subject: code review1

---
 test/test_gromov.py | 57 +++++++++++++++++++++++++++++++++++++++++------------
 1 file changed, 44 insertions(+), 13 deletions(-)

(limited to 'test')

diff --git a/test/test_gromov.py b/test/test_gromov.py
index cd180d4..ec85abf 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
 
@@ -10,6 +11,8 @@ import ot
 
 
 def test_gromov():
+    np.random.seed(42)
+
     n_samples = 50  # nb samples
 
     mu_s = np.array([0, 0])
@@ -36,6 +39,11 @@ def test_gromov():
     np.testing.assert_allclose(
         q, G.sum(0), atol=1e-04)  # cf convergence gromov
 
+    Id = (1 / 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']
@@ -50,6 +58,8 @@ def test_gromov():
 
 
 def test_entropic_gromov():
+    np.random.seed(42)
+
     n_samples = 50  # nb samples
 
     mu_s = np.array([0, 0])
@@ -92,6 +102,7 @@ def test_entropic_gromov():
 
 
 def test_gromov_barycenter():
+    np.random.seed(42)
 
     ns = 50
     nt = 60
@@ -120,7 +131,7 @@ def test_gromov_barycenter():
 
 
 def test_gromov_entropic_barycenter():
-
+    np.random.seed(42)
     ns = 50
     nt = 60
 
@@ -148,6 +159,8 @@ def test_gromov_entropic_barycenter():
 
 
 def test_fgw():
+    np.random.seed(42)
+
     n_samples = 50  # nb samples
 
     mu_s = np.array([0, 0])
@@ -180,8 +193,26 @@ def test_fgw():
     np.testing.assert_allclose(
         q, G.sum(0), atol=1e-04)  # cf convergence fgw
 
+    Id = (1 / 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
@@ -196,28 +227,28 @@ def test_fgw_barycenter():
     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)
+    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, 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)
+    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, 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)
+    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]))
-- 
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