Reame +pep8

2 files changed, 32 insertions, 30 deletions

diff --git a/test/test_gromov.py b/test/test_gromov.py
index 43b63e1..cd180d4 100644
--- a/test/test_gromov.py
+++ b/test/test_gromov.py
@@ -145,7 +145,8 @@ 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

 

@@ -155,9 +156,9 @@ def test_fgw():
     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()

+

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

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

 

     p = ot.unif(n_samples)

     q = ot.unif(n_samples)

@@ -167,11 +168,11 @@ def test_fgw():
 

     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)

+    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(

@@ -187,36 +188,36 @@ def test_fgw_barycenter():
 

     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)

+

+    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)

+    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)

+

+    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)

+    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]))

diff --git a/test/test_optim.py b/test/test_optim.py
index 1188ef6..e7ba32a 100644
--- a/test/test_optim.py
+++ b/test/test_optim.py
@@ -65,8 +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_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)
+    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)