Remove redundant parametrization from wasserstein_1d tests (#517)

* No need to parametrize for nx param

* Remove redundant check for TF backend

* Remove redundant TF check from emd2 test

---------

Co-authored-by: Rémi Flamary <remi.flamary@gmail.com>

Author: kachayev

test/test_1d_solver.py

@@ -9,13 +9,11 @@
 import pytest
 
 import ot
+from ot.backend import tf
 from ot.lp import wasserstein_1d
 
-from ot.backend import get_backend_list, tf
 from scipy.stats import wasserstein_distance
 
-backend_list = get_backend_list()
-
 
 def test_emd_1d_emd2_1d_with_weights():
     # test emd1d gives similar results as emd
@@ -53,10 +51,7 @@ def test_emd_1d_emd2_1d_with_weights():
     np.testing.assert_allclose(w_v, G.sum(0))
 
 
-@pytest.mark.parametrize('nx', backend_list)
 def test_wasserstein_1d(nx):
-    from scipy.stats import wasserstein_distance
-
     rng = np.random.RandomState(0)
 
     n = 100
@@ -105,8 +100,6 @@ def test_wasserstein_1d_type_devices(nx):
 
 @pytest.mark.skipif(not tf, reason="tf not installed")
 def test_wasserstein_1d_device_tf():
-    if not tf:
-        return
     nx = ot.backend.TensorflowBackend()
     rng = np.random.RandomState(0)
     n = 10

test/test_ot.py

@@ -103,8 +103,6 @@ def test_emd_emd2_types_devices(nx):
 
 @pytest.mark.skipif(not tf, reason="tf not installed")
 def test_emd_emd2_devices_tf():
-    if not tf:
-        return
     nx = ot.backend.TensorflowBackend()
 
     n_samples = 100