speedup tests

Author: rflamary

test/test_bregman.py

@@ -118,12 +118,12 @@ def test_wasserstein_bary_2d():
     a2 += a2.min()
     a2 = a2 / np.sum(a2)
     # creating matrix A containing all distributions
-    A = np.zeros((2, 100, 100))
+    A = np.zeros((2, size, size))
     A[0, :, :] = a1
     A[1, :, :] = a2
 
     # wasserstein
-    reg = 1e-3
+    reg = 1e-2
     bary_wass = ot.bregman.convolutional_barycenter2d(A, reg)
 
     np.testing.assert_allclose(1, np.sum(bary_wass))

test/test_gromov.py

@@ -28,7 +28,7 @@ def test_gromov():
     C1 /= C1.max()
     C2 /= C2.max()
 
-    G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss')
+    G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True)
 
     # check constratints
     np.testing.assert_allclose(
@@ -69,7 +69,7 @@ def test_entropic_gromov():
     C2 /= C2.max()
 
     G = ot.gromov.entropic_gromov_wasserstein(
-        C1, C2, p, q, 'square_loss', epsilon=5e-4)
+        C1, C2, p, q, 'square_loss', epsilon=5e-4, verbose=True)
 
     # check constratints
     np.testing.assert_allclose(
@@ -107,7 +107,8 @@ def test_gromov_barycenter():
                                       [ot.unif(ns), ot.unif(nt)
                                        ], ot.unif(n_samples), [.5, .5],
                                       'square_loss',  # 5e-4,
-                                      max_iter=100, tol=1e-3)
+                                      max_iter=100, tol=1e-3,
+                                      verbose=True)
     np.testing.assert_allclose(Cb.shape, (n_samples, n_samples))
 
     Cb2 = ot.gromov.gromov_barycenters(n_samples, [C1, C2],
@@ -134,7 +135,8 @@ def test_gromov_entropic_barycenter():
                                                [ot.unif(ns), ot.unif(nt)
                                                 ], ot.unif(n_samples), [.5, .5],
                                                'square_loss', 2e-3,
-                                               max_iter=100, tol=1e-3)
+                                               max_iter=100, tol=1e-3,
+                                               verbose=True)
     np.testing.assert_allclose(Cb.shape, (n_samples, n_samples))
 
     Cb2 = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2],

test/test_ot.py

@@ -70,15 +70,15 @@ def test_emd_empty():
 
 
 def test_emd2_multi():
-    n = 1000  # nb bins
+    n = 500  # nb bins
 
     # bin positions
     x = np.arange(n, dtype=np.float64)
 
     # Gaussian distributions
     a = gauss(n, m=20, s=5)  # m= mean, s= std
 
-    ls = np.arange(20, 1000, 20)
+    ls = np.arange(20, 500, 20)
     nb = len(ls)
     b = np.zeros((n, nb))
     for i in range(nb):
@@ -207,11 +207,11 @@ def test_warnings():
 
 
 def test_dual_variables():
-    n = 5000  # nb bins
-    m = 6000  # nb bins
+    n = 500  # nb bins
+    m = 600  # nb bins
 
-    mean1 = 1000
-    mean2 = 1100
+    mean1 = 300
+    mean2 = 400
 
     # bin positions
     x = np.arange(n, dtype=np.float64)

test/test_utils.py

@@ -12,7 +12,7 @@
 
 def test_parmap():
 
-    n = 100
+    n = 10
 
     def f(i):
         return 1.0 * i * i